Loaded environment variables from /Users/nickviljoen/Desktop/Visual_AI_QC/config.env OPENAI_API_KEY set: True GOOGLE_API_KEY set: True LLM Config: Loaded environment variables from /Users/nickviljoen/Desktop/Visual_AI_QC/config.env OpenAI API key loaded (length: 155) Google API key loaded (length: 39) Discovered 33 QC checks: ['accessibility', 'animation_transitions', 'aspect_ratio', 'background_contrast', 'brand_assets_visibility', 'call_to_action', 'color_format', 'crop_marks', 'curved_edges', 'curved_edges_digital', 'curved_edges_print', 'dark_mode_legibility', 'element_alignment', 'face_gaze_direction', 'face_visibility', 'file_naming', 'image_resolution', 'imperative_verb', 'inclusive', 'layer_organization', 'logo_visibility', 'lowercase_text', 'new_visibility', 'print_bleed', 'product_visibility', 'responsiveness', 'safety_area', 'supporting_images', 'text_readability', 'visual_elements_count', 'visual_hierarchy', 'visuals_left_text_right', 'word_count'] Loaded profile 'Unilever Key Visual' from /Users/nickviljoen/Desktop/Visual_AI_QC/profiles/unilever_key_visual.json Loaded profile 'Diageo Packaging' from /Users/nickviljoen/Desktop/Visual_AI_QC/profiles/diageo_packaging.json Loaded profile 'General Packaging' from /Users/nickviljoen/Desktop/Visual_AI_QC/profiles/general_packaging.json Loaded profile 'Inclusive Accessibility Profile' from /Users/nickviljoen/Desktop/Visual_AI_QC/profiles/inclusive_accessibility.json Loaded profile 'General Key Visual' from /Users/nickviljoen/Desktop/Visual_AI_QC/profiles/general_key_visual.json Loaded profile 'Unilever Packaging' from /Users/nickviljoen/Desktop/Visual_AI_QC/profiles/unilever_packaging.json Loaded profile 'All Checks' from /Users/nickviljoen/Desktop/Visual_AI_QC/profiles/default.json Loaded profile 'Diageo Key Visual' from /Users/nickviljoen/Desktop/Visual_AI_QC/profiles/diageo_key_visual.json Loaded QC check: accessibility Loaded QC check: animation_transitions Loaded QC check: aspect_ratio Loaded QC check: background_contrast Loaded QC check: brand_assets_visibility Loaded QC check: call_to_action Loaded QC check: color_format Loaded QC check: crop_marks Loaded QC check: curved_edges Loaded QC check: curved_edges_digital Loaded QC check: curved_edges_print Loaded QC check: dark_mode_legibility Loaded QC check: element_alignment Loaded QC check: face_gaze_direction Loaded QC check: face_visibility Loaded QC check: file_naming Loaded QC check: image_resolution Loaded QC check: imperative_verb Loaded QC check: inclusive Loaded QC check: layer_organization Loaded QC check: logo_visibility Loaded QC check: lowercase_text Loaded QC check: new_visibility Loaded QC check: print_bleed Loaded QC check: product_visibility Loaded QC check: responsiveness Loaded QC check: safety_area Loaded QC check: supporting_images Loaded QC check: text_readability Loaded QC check: visual_elements_count Loaded QC check: visual_hierarchy Loaded QC check: visuals_left_text_right Loaded QC check: word_count Initialized app with 33 QC apps Brand Guidelines DB initialized: 2 brands Starting Flask API server on 0.0.0.0:7183 * Serving Flask app 'api_server' * Debug mode: off WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead. * Running on all addresses (0.0.0.0) * Running on http://127.0.0.1:7183 * Running on http://192.168.10.107:7183 Press CTRL+C to quit 127.0.0.1 - - [07/Aug/2025 20:31:33] "GET /health HTTP/1.1" 200 - 127.0.0.1 - - [07/Aug/2025 20:31:44] "POST /api/start_analysis HTTP/1.1" 200 - Starting analysis with profile: diageo_key_visual, brand: diageo, mode: html DEBUG: Raw mode parameter from request: 'html' DEBUG: Processed output_mode: 'html' Background thread started for session: 20250807_203144 Parameters: brand=diageo, profile=diageo_key_visual, mode=html DEBUG: analysis_mode = 'html' DEBUG: Before validation, analysis_mode = 'html' DEBUG: analysis_mode 'html' is valid DEBUG: After validation, analysis_mode = 'html' Using specified profile: diageo_key_visual Step 1: Running QC analysis with profile 'diageo_key_visual' Processing 11 checks in batches of 15 Processing batch 1/1 (11 checks) Running check 1/11: background_contrast ==================================================================================================== DEBUG: Visual QC Analysis Starting ==================================================================================================== DEBUG: Model selected: Gemini DEBUG: Asset file path: uploads/20250807_203144/image (1) 2.png DEBUG: No reference file provided DEBUG: Prompt length: 3866 characters DEBUG: File validation passed - both files exist DEBUG: Loading main asset image from: uploads/20250807_203144/image (1) 2.png Running check 4/11: face_gaze_direction ==================================================================================================== DEBUG: Visual QC Analysis Starting ==================================================================================================== DEBUG: Model selected: Gemini DEBUG: Asset file path: uploads/20250807_203144/image (1) 2.png DEBUG: No reference file provided DEBUG: Prompt length: 3053 characters Running check 3/11: call_to_action ==================================================================================================== DEBUG: Visual QC Analysis Starting ==================================================================================================== DEBUG: Model selected: Gemini DEBUG: Asset file path: uploads/20250807_203144/image (1) 2.png DEBUG: No reference file provided DEBUG: Prompt length: 3663 characters Running check 5/11: face_visibility ==================================================================================================== DEBUG: Visual QC Analysis Starting ==================================================================================================== DEBUG: Model selected: Gemini DEBUG: Asset file path: uploads/20250807_203144/image (1) 2.png DEBUG: No reference file provided DEBUG: Prompt length: 3858 characters DEBUG: File validation passed - both files exist DEBUG: Loading main asset image from: uploads/20250807_203144/image (1) 2.png DEBUG: File validation passed - both files exist DEBUG: Loading main asset image from: uploads/20250807_203144/image (1) 2.png Running check 6/11: imperative_verb ==================================================================================================== DEBUG: Visual QC Analysis Starting ==================================================================================================== DEBUG: Model selected: Gemini DEBUG: Asset file path: uploads/20250807_203144/image (1) 2.png DEBUG: No reference file provided DEBUG: Prompt length: 3339 characters DEBUG: File validation passed - both files exist DEBUG: Loading main asset image from: uploads/20250807_203144/image (1) 2.png DEBUG: File validation passed - both files exist DEBUG: Loading main asset image from: uploads/20250807_203144/image (1) 2.png Running check 8/11: visual_elements_count ==================================================================================================== DEBUG: Visual QC Analysis Starting ==================================================================================================== DEBUG: Model selected: Gemini DEBUG: Asset file path: uploads/20250807_203144/image (1) 2.png DEBUG: No reference file provided DEBUG: Prompt length: 3714 characters Running check 11/11: text_readability ==================================================================================================== DEBUG: Visual QC Analysis Starting ==================================================================================================== DEBUG: Model selected: Gemini DEBUG: Asset file path: uploads/20250807_203144/image (1) 2.png DEBUG: No reference file provided DEBUG: Prompt length: 3563 characters Running check 10/11: word_count ==================================================================================================== DEBUG: Visual QC Analysis Starting ==================================================================================================== DEBUG: Model selected: Gemini DEBUG: Asset file path: uploads/20250807_203144/image (1) 2.png DEBUG: No reference file provided DEBUG: Prompt length: 2700 characters DEBUG: File validation passed - both files exist DEBUG: Loading main asset image from: uploads/20250807_203144/image (1) 2.png Completed check: visual_hierarchy (1/11) Completed check: logo_visibility (2/11) Completed check: brand_assets_visibility (3/11) DEBUG: File validation passed - both files exist DEBUG: Loading main asset image from: uploads/20250807_203144/image (1) 2.png DEBUG: File validation passed - both files exist DEBUG: Loading main asset image from: uploads/20250807_203144/image (1) 2.png DEBUG: Main asset loaded successfully - dimensions: (693, 1024), mode: RGB DEBUG: Calling Gemini API for visual analysis ================================================================================ DEBUG: Gemini Vision API Call ================================================================================ DEBUG: Main asset image dimensions: (693, 1024) DEBUG: Main asset image mode: RGB DEBUG: No reference image provided DEBUG: Total images being sent to LLM: 1 (main asset only) DEBUG: Original prompt being sent to LLM: ---------------------------------------- Before assessment: Users often upload complex print production files with multiple panels, cut lines, and technical markings. Your task: Focus ONLY on the primary consumer-facing visual - what the end customer would actually see after production. Key instructions: • Ignore cut lines, registration marks, and production guides • If multiple panels exist, assess the main/largest consumer-facing design • Disregard technical text, color bars, or printer information • Focus on the final visual as it would appear on shelf/in-market • Treat folded/die-cut layouts as the assembled final product STEPS TO EVALUATE: 1. Examine the design components (logos, pack shots, icons, images, text blocks, promotion badges, etc.). Each distinct object or text grouping counts as one visual element that the viewer must actively notice. 2. Count the total number of these distinct visual elements. 3. If the count is more than 4, the design fails this checkpoint, as it may be too cluttered or complex. You are tasked with performing a text analysis on a Point of Sale (POS) advertisement to determine if the advertisement text contains any imperative verbs (instructional commands such as "Buy", "Try", "Enjoy", "Discover"). You will validate the presence of imperative verbs according to the criteria below. CRITERIA TO EVALUATE: 1. Examine the text content of the advertisement. 2. Identify any verbs that are used as commands or instructions directed at the consumer. 3. Consider common imperative verbs like "Buy", "Try", "Enjoy", "Discover", or others that fit the context of providing an instruction. 4. Distinguish these imperative verbs from other verb forms that do not function as commands. STEPS TO PROCESS: 1. Text Analysis: 2. Scan through the provided text on the advertisement. 3. Look for verbs that are typically used to give commands. 4. Command Identification: Determine if any verbs are used in a commanding or instructive form. Result Validation: 5. Confirm the context of verbs to confidently classify them as imperative. YOUR OUTPUT: 1. State whether an imperative verb is present or not. 2. If an imperative verb is present, specify the verb(s) detected. 3. If not, indicate that no imperative verbs were found. 4. Present your findings in the following JSON code structure: { "imperative_verb_present": true or false, "detected_imperative_verbs": ["verb1", "verb2", ...] (only if imperative_verb_present is true) "checkpoint_result": "Pass" or "Fail" } SCORING INSTRUCTIONS: After your analysis, provide a numerical score from 1 to 10 (with 10 being perfect/excellent and 1 being poor/inadequate) based on how well the asset meets the criteria. YOUR OUTPUT FORMAT: Include a JSON code block with the following fields: - "score": a number from 1 to 10 - "explanation": your detailed reasoning for the score - "recommendations": specific suggestions for improvement if applicable Example: ```json { "score": 8, "explanation": "The logo is clearly visible and occupies approximately 12% of the advertisement area, which exceeds the minimum requirement. It has good contrast against the background and would be recognizable from the appropriate viewing distance.", "recommendations": "Consider increasing contrast around the edges of the logo for even better visibility in darker environments." } ``` DEBUG: Main asset loaded successfully - dimensions: (693, 1024), mode: RGB DEBUG: Calling Gemini API for visual analysis ================================================================================ DEBUG: Gemini Vision API Call ================================================================================ DEBUG: Main asset image dimensions: (693, 1024) DEBUG: Main asset image mode: RGB DEBUG: No reference image provided DEBUG: Total images being sent to LLM: 1 (main asset only) DEBUG: Original prompt being sent to LLM: ---------------------------------------- Before assessment: Users often upload complex print production files with multiple panels, cut lines, and technical markings. Your task: Focus ONLY on the primary consumer-facing visual - what the end customer would actually see after production. Key instructions: • Ignore cut lines, registration marks, and production guides • If multiple panels exist, assess the main/largest consumer-facing design • Disregard technical text, color bars, or printer information • Focus on the final visual as it would appear on shelf/in-market • Treat folded/die-cut layouts as the assembled final product STEPS TO EVALUATE: 1. Examine the design components (logos, pack shots, icons, images, text blocks, promotion badges, etc.). Each distinct object or text grouping counts as one visual element that the viewer must actively notice. 2. Count the total number of these distinct visual elements. 3. If the count is more than 4, the design fails this checkpoint, as it may be too cluttered or complex. You are performing a visual quality-control check on a Point of Sale (POS) advertisement. Your task is to determine whether the text on the advertisement includes an effective call-to-action with the following criteria: 1. Uses an imperative verb (such as "Buy", "Try", "Get", "Discover", etc.) 2. Contains 10 words or less 3. Is free of spelling and grammar errors 4. Encourages the viewer to buy, use, or consume the product featured STEPS TO EVALUATE: 1. Examine the Text Content: Identify any text present on the advertisement, specifically focusing on the call-to-action. 2. Call-to-Action Analysis: a. Identify the main call-to-action text (if present) b. Check if it begins with or contains an imperative verb c. Count the number of words (must be 10 or fewer) d. Check for spelling and grammar errors 3. Effectiveness Check: a. Evaluate if the text includes clear persuasive language encouraging purchase, use, or consumption b. Assess whether it goes beyond merely being a product claim c. Determine if it inspires action YOUR OUTPUT: 1. State whether a call-to-action is present 2. If present: - Note if it uses an imperative verb - Count the words (should be 10 or fewer) - Verify it's free of spelling/grammar errors - Assess if it effectively encourages action 3. Provide an overall pass/fail assessment 4. If it fails, provide 1–2 concise recommendations for improvement 5. Include a JSON code block with these fields: { "cta_present": true or false, "uses_imperative_verb": true or false, "word_count": number, "word_count_acceptable": true or false, "spelling_grammar_correct": true or false, "encourages_action": true or false, "overall_assessment": "Pass" or "Fail", "recommendations": ["Recommendation 1", "Recommendation 2"] (if failed) } SCORING INSTRUCTIONS: After your analysis, provide a numerical score from 1 to 10 (with 10 being perfect/excellent and 1 being poor/inadequate) based on how well the asset meets the criteria. YOUR OUTPUT FORMAT: Include a JSON code block with the following fields: - "score": a number from 1 to 10 - "explanation": your detailed reasoning for the score - "recommendations": specific suggestions for improvement if applicable Example: ```json { "score": 8, "explanation": "The logo is clearly visible and occupies approximately 12% of the advertisement area, which exceeds the minimum requirement. It has good contrast against the background and would be recognizable from the appropriate viewing distance.", "recommendations": "Consider increasing contrast around the edges of the logo for even better visibility in darker environments." } ``` ---------------------------------------- DEBUG: Using Gemini model: gemini-2.5-pro-preview-03-25 DEBUG: Added main asset image to parts list (dimensions: (693, 1024)) DEBUG: Total parts being sent to Gemini: 2 (1 text + 1 images) ---------------------------------------- DEBUG: Using Gemini model: gemini-2.5-pro-preview-03-25 DEBUG: Added main asset image to parts list (dimensions: (693, 1024)) DEBUG: Total parts being sent to Gemini: 2 (1 text + 1 images) DEBUG: Main asset loaded successfully - dimensions: (693, 1024), mode: RGB DEBUG: Calling Gemini API for visual analysis ================================================================================ DEBUG: Gemini Vision API Call ================================================================================ DEBUG: Main asset image dimensions: (693, 1024) DEBUG: Main asset image mode: RGB DEBUG: No reference image provided DEBUG: Total images being sent to LLM: 1 (main asset only) DEBUG: Original prompt being sent to LLM: ---------------------------------------- Before assessment: Users often upload complex print production files with multiple panels, cut lines, and technical markings. Your task: Focus ONLY on the primary consumer-facing visual - what the end customer would actually see after production. Key instructions: • Ignore cut lines, registration marks, and production guides • If multiple panels exist, assess the main/largest consumer-facing design • Disregard technical text, color bars, or printer information • Focus on the final visual as it would appear on shelf/in-market • Treat folded/die-cut layouts as the assembled final product STEPS TO EVALUATE: 1. Examine the design components (logos, pack shots, icons, images, text blocks, promotion badges, etc.). Each distinct object or text grouping counts as one visual element that the viewer must actively notice. 2. Count the total number of these distinct visual elements. 3. If the count is more than 4, the design fails this checkpoint, as it may be too cluttered or complex. You are performing a visual quality‐control check on a Point of Sale (POS) advertisement. Your task is to determine whether the text on the advertisement is readable from the appropriate viewing distance: • For large media (Floor‐Standing Display Units, posters, etc.): The text must be clearly readable from about 3 meters away. • For smaller media (wobblers, shelf talkers, etc.): The text must be clearly readable from about 1 meter away. STEPS TO EVALUATE: 1. Identify all text elements in the advertisement, excluding any terms and conditions. 2. Examine the text size, font style, and contrast with the background. 3. For each text element, assess whether it would be readable from the appropriate viewing distance. 4. Pay special attention to: a. Text size - Is it large enough to be read from a distance? b. Font choice - Is the font clear and easily readable? c. Contrast - Does the text stand out clearly against its background? d. Spacing - Is there sufficient spacing between letters, words, and lines? 5. Consider any potential obstacles to readability such as: a. Decorative fonts that sacrifice legibility for style b. Overlapping elements that obscure text c. Poor contrast that makes text blend into the background d. Text that is too small relative to viewing distance YOUR OUTPUT: • State whether the text elements "pass" or "fail" the readability checkpoint. • If they fail, provide 1–2 concise recommendations for improving text readability. • Include a JSON code block with these fields: { "text_readability": "Pass" or "Fail", "readability_score": "High", "Medium", or "Low", "recommendations": ["Recommendation 1", "Recommendation 2"] (only if "Fail") } SCORING INSTRUCTIONS: After your analysis, provide a numerical score from 1 to 10 (with 10 being perfect/excellent and 1 being poor/inadequate) based on how well the asset meets the criteria. YOUR OUTPUT FORMAT: Include a JSON code block with the following fields: - "score": a number from 1 to 10 - "explanation": your detailed reasoning for the score - "recommendations": specific suggestions for improvement if applicable Example: ```json { "score": 8, "explanation": "The logo is clearly visible and occupies approximately 12% of the advertisement area, which exceeds the minimum requirement. It has good contrast against the background and would be recognizable from the appropriate viewing distance.", "recommendations": "Consider increasing contrast around the edges of the logo for even better visibility in darker environments." } ``` DEBUG: Main asset loaded successfully - dimensions: (693, 1024), mode: RGB DEBUG: Calling Gemini API for visual analysis ================================================================================ DEBUG: Gemini Vision API Call ================================================================================ DEBUG: Main asset image dimensions: (693, 1024) DEBUG: Main asset image mode: RGB DEBUG: No reference image provided DEBUG: Total images being sent to LLM: 1 (main asset only) DEBUG: Original prompt being sent to LLM: ---------------------------------------- Before assessment: Users often upload complex print production files with multiple panels, cut lines, and technical markings. Your task: Focus ONLY on the primary consumer-facing visual - what the end customer would actually see after production. Key instructions: • Ignore cut lines, registration marks, and production guides • If multiple panels exist, assess the main/largest consumer-facing design • Disregard technical text, color bars, or printer information • Focus on the final visual as it would appear on shelf/in-market • Treat folded/die-cut layouts as the assembled final product STEPS TO EVALUATE: 1. Examine the design components (logos, pack shots, icons, images, text blocks, promotion badges, etc.). Each distinct object or text grouping counts as one visual element that the viewer must actively notice. 2. Count the total number of these distinct visual elements. 3. If the count is more than 4, the design fails this checkpoint, as it may be too cluttered or complex. You are conducting a strict visual quality‐control check on a Point of Sale (POS) advertisement to verify that crucial design elements—including product images, the brand logo, and key text—stand out crisply from the background at a typical viewing distance of 3 meters. EVALUATION STEPS: 1. Identify primary visuals: a. Brand logo (extra scrutiny for brand consistency and brightness/vibrancy). b. Product shots. c. Main text or headlines. 2. Check clarity at a maximum viewing distance (3 meters): a. Ensure the element remains legible or clearly visible without straining. 3. Evaluate color and brightness contrast: a. Visually verify that each key element "pops" against the background. b. Pay special attention to the brand logo. Any condition that makes the logo less than immediately recognizable at a glance is grounds for failing. c. If any key element is difficult to distinguish, consider contrast insufficient. 4. Use a color‐contrast analyzer (e.g., APCA): a. Confirm that larger elements meet or exceed ±Lc45. b. Confirm that smaller text or detailed logos/icons meet or exceed ±Lc75. c. Specifically check the brand logo area to ensure it meets or exceeds the recommended contrast level, highlighting any saturation/brightness issues. Decide pass/fail: • "Pass" only if ALL key elements (logo, product, text) remain sharp, legible, and clearly distinct from the background. • "Fail" if any single critical element has low contrast, appears fuzzy, or is overshadowed or faded relative to the background. Where possible, recommend one or two succinct improvement strategies (e.g., darkening the background, adding an outline/shadow, increasing the saturation of the logo colors). YOUR OUTPUT: • State whether the design "passes" or "fails" the background contrast checkpoint. • If it fails, list one concise way to improve contrast. • Format your response as a JSON code block with the structure: { "background_contrast": "[Pass or Fail]", "recommended_adjustments": "[None or Improvement]" } SCORING INSTRUCTIONS: After your analysis, provide a numerical score from 1 to 10 (with 10 being perfect/excellent and 1 being poor/inadequate) based on how well the asset meets the criteria. YOUR OUTPUT FORMAT: Include a JSON code block with the following fields: - "score": a number from 1 to 10 - "explanation": your detailed reasoning for the score - "recommendations": specific suggestions for improvement if applicable Example: ```json { "score": 8, "explanation": "The logo is clearly visible and occupies approximately 12% of the advertisement area, which exceeds the minimum requirement. It has good contrast against the background and would be recognizable from the appropriate viewing distance.", "recommendations": "Consider increasing contrast around the edges of the logo for even better visibility in darker environments." } ``` ---------------------------------------- DEBUG: Using Gemini model: gemini-2.5-pro-preview-03-25 DEBUG: Added main asset image to parts list (dimensions: (693, 1024)) DEBUG: Total parts being sent to Gemini: 2 (1 text + 1 images) DEBUG: Main asset loaded successfully - dimensions: (693, 1024), mode: RGB DEBUG: Calling Gemini API for visual analysis ================================================================================ DEBUG: Gemini Vision API Call ================================================================================ DEBUG: Main asset image dimensions: (693, 1024) DEBUG: Main asset image mode: RGB DEBUG: No reference image provided DEBUG: Total images being sent to LLM: 1 (main asset only) DEBUG: Original prompt being sent to LLM: ---------------------------------------- Before assessment: Users often upload complex print production files with multiple panels, cut lines, and technical markings. Your task: Focus ONLY on the primary consumer-facing visual - what the end customer would actually see after production. Key instructions: • Ignore cut lines, registration marks, and production guides • If multiple panels exist, assess the main/largest consumer-facing design • Disregard technical text, color bars, or printer information • Focus on the final visual as it would appear on shelf/in-market • Treat folded/die-cut layouts as the assembled final product STEPS TO EVALUATE: 1. Examine the design components (logos, pack shots, icons, images, text blocks, promotion badges, etc.). Each distinct object or text grouping counts as one visual element that the viewer must actively notice. 2. Count the total number of these distinct visual elements. 3. If the count is more than 4, the design fails this checkpoint, as it may be too cluttered or complex. You are performing a visual quality-control check on a Point of Sale (POS) advertisement. Your task is to determine whether the face (with eyes) meets these criteria, based on its intended display size: • For large media (Floor-Standing Display Units, posters, etc.): The face (including eyes) must be clearly visible from about 3 meters away. • For smaller media (wobblers, shelf talkers, etc.): The face must be clearly visible from about 1 meter away. STEPS TO EVALUATE: Examine the advertisement's overall layout. Check whether at least one face is large enough and has sufficient contrast so that its eyes are distinguishable from the stated distance. Imagine viewing the design at a reduced scale or slightly out of focus—details of the eyes should still be discernible. If you can identify not just that it's a face but also clearly detect the eye region (i.e., tell where the eyes are) at the appropriate viewing distance, it passes this checkpoint. A face whose total area (or coverage) is below 8% of the entire ad might not be clearly visible. Consider this a FAIL unless the face's positioning or contrast compensates for the lower coverage. YOUR OUTPUT: • First, clearly state whether a face is present at all in the advertisement. • If NO face is present, state "Fail - No visible face detected in the image to evaluate for visibility at distance." • If a face IS present: - Describe the face's size and prominence in the design - State whether the design "passes" or "fails" the face‐visibility‐at‐distance checkpoint - Explain why it passes or fails based on the visibility criteria - If it fails, provide 1–2 concise recommendations for improving eye/face visibility • Finally, include a JSON code block with these fields: { "face_present": true or false, "face_visibility_at_distance": "Pass" or "Fail" (only if face_present is true), "face_coverage_percentage": (numerical estimate of the fraction of the total ad canvas occupied by the face, only if face_present is true) } SCORING INSTRUCTIONS: After your analysis, provide a numerical score from 1 to 10 (with 10 being perfect/excellent and 1 being poor/inadequate) based on how well the asset meets the criteria. YOUR OUTPUT FORMAT: Include a JSON code block with the following fields: - "score": a number from 1 to 10 - "explanation": your detailed reasoning for the score - "recommendations": specific suggestions for improvement if applicable Example: ```json { "score": 8, "explanation": "The logo is clearly visible and occupies approximately 12% of the advertisement area, which exceeds the minimum requirement. It has good contrast against the background and would be recognizable from the appropriate viewing distance.", "recommendations": "Consider increasing contrast around the edges of the logo for even better visibility in darker environments." } ``` ---------------------------------------- DEBUG: Using Gemini model: gemini-2.5-pro-preview-03-25 DEBUG: Added main asset image to parts list (dimensions: (693, 1024)) DEBUG: Total parts being sent to Gemini: 2 (1 text + 1 images) ---------------------------------------- DEBUG: Using Gemini model: gemini-2.5-pro-preview-03-25 DEBUG: Added main asset image to parts list (dimensions: (693, 1024)) DEBUG: Total parts being sent to Gemini: 2 (1 text + 1 images) DEBUG: Main asset loaded successfully - dimensions: (693, 1024), mode: RGB DEBUG: Calling Gemini API for visual analysis ================================================================================ DEBUG: Gemini Vision API Call ================================================================================ DEBUG: Main asset image dimensions: (693, 1024) DEBUG: Main asset image mode: RGB DEBUG: No reference image provided DEBUG: Total images being sent to LLM: 1 (main asset only) DEBUG: Original prompt being sent to LLM: ---------------------------------------- Before assessment: Users often upload complex print production files with multiple panels, cut lines, and technical markings. Your task: Focus ONLY on the primary consumer-facing visual - what the end customer would actually see after production. Key instructions: • Ignore cut lines, registration marks, and production guides • If multiple panels exist, assess the main/largest consumer-facing design • Disregard technical text, color bars, or printer information • Focus on the final visual as it would appear on shelf/in-market • Treat folded/die-cut layouts as the assembled final product STEPS TO EVALUATE: 1. Examine the design components (logos, pack shots, icons, images, text blocks, promotion badges, etc.). Each distinct object or text grouping counts as one visual element that the viewer must actively notice. 2. Count the total number of these distinct visual elements. 3. If the count is more than 4, the design fails this checkpoint, as it may be too cluttered or complex. You are performing a visual quality-control check on a Point of Sale (POS) advertisement. Your task is to determine whether the advertisement contains 7 words or fewer. Examine the advertisement carefully and follow these steps: STEPS TO EVALUATE: 1. Identify all textual elements in the advertisement, excluding any terms and conditions, the word "NEW," brand logos, and any text present on product packaging. 2. Count the total number of words in these textual elements. 3. Determine if the count is 7 words or fewer. YOUR OUTPUT: Based on the evaluation, confirm whether the advertisement passes the word-count checkpoint, stating "Pass" if it contains 7 words or fewer, or "Fail" if it contains more. Create a JSON code block with these fields: { "word_count": (total number of words counted), "checkpoint_result": "Pass" or "Fail" } SCORING INSTRUCTIONS: After your analysis, provide a numerical score from 1 to 10 (with 10 being perfect/excellent and 1 being poor/inadequate) based on how well the asset meets the criteria. YOUR OUTPUT FORMAT: Include a JSON code block with the following fields: - "score": a number from 1 to 10 - "explanation": your detailed reasoning for the score - "recommendations": specific suggestions for improvement if applicable Example: ```json { "score": 8, "explanation": "The logo is clearly visible and occupies approximately 12% of the advertisement area, which exceeds the minimum requirement. It has good contrast against the background and would be recognizable from the appropriate viewing distance.", "recommendations": "Consider increasing contrast around the edges of the logo for even better visibility in darker environments." } ``` ---------------------------------------- DEBUG: Using Gemini model: gemini-2.5-pro-preview-03-25 DEBUG: Added main asset image to parts list (dimensions: (693, 1024)) DEBUG: Total parts being sent to Gemini: 2 (1 text + 1 images) DEBUG: Main asset loaded successfully - dimensions: (693, 1024), mode: RGB DEBUG: Calling Gemini API for visual analysis ================================================================================ DEBUG: Gemini Vision API Call ================================================================================ DEBUG: Main asset image dimensions: (693, 1024) DEBUG: Main asset image mode: RGB DEBUG: No reference image provided DEBUG: Total images being sent to LLM: 1 (main asset only) DEBUG: Original prompt being sent to LLM: ---------------------------------------- Before assessment: Users often upload complex print production files with multiple panels, cut lines, and technical markings. Your task: Focus ONLY on the primary consumer-facing visual - what the end customer would actually see after production. Key instructions: • Ignore cut lines, registration marks, and production guides • If multiple panels exist, assess the main/largest consumer-facing design • Disregard technical text, color bars, or printer information • Focus on the final visual as it would appear on shelf/in-market • Treat folded/die-cut layouts as the assembled final product STEPS TO EVALUATE: 1. Examine the design components (logos, pack shots, icons, images, text blocks, promotion badges, etc.). Each distinct object or text grouping counts as one visual element that the viewer must actively notice. 2. Count the total number of these distinct visual elements. 3. If the count is more than 4, the design fails this checkpoint, as it may be too cluttered or complex. You are performing a visual quality-control check on a POS advertisement. The brand wants to limit the design to no more than 4 distinct visual elements so that the message is clear and quick to process. IMPORTANT PRE-ASSESSMENT INSTRUCTIONS: Before assessment: Users often upload complex print production files with multiple panels, cut lines, and technical markings. Your task: Focus ONLY on the primary consumer-facing visual - what the end customer would actually see after production. Key instructions: - Ignore cut lines, registration marks, and production guides - If multiple panels exist, assess the main/largest consumer-facing design - Disregard technical text, color bars, or printer information - Focus on the final visual as it would appear on shelf/in-market - Treat folded/die-cut layouts as the assembled final product STEPS TO EVALUATE: 1. Examine the design components (logos, pack shots, icons, images, text blocks, promotion badges, etc.). Each distinct object or text grouping counts as one visual element that the viewer must actively notice. 2. Count the total number of these distinct visual elements. 3. If the count is more than 4, the design fails this checkpoint, as it may be too cluttered or complex. Otherwise, it passes. YOUR OUTPUT: • List all distinct visual elements you identified in the design (e.g., "1. Logo, 2. Product image, 3. Main headline...). • State the total count of visual elements found. • State whether the design "passes" or "fails" the simplified-design checkpoint. • Provide a clear explanation of why it passes or fails based on the element count. • If it fails, provide 1–2 concise recommendations for reducing or combining elements to achieve clarity. • Include a JSON code block with two fields: "visual_element_count": the total number of visual elements "compliance_status": "Pass" or "Fail" SCORING INSTRUCTIONS: After your analysis, provide a numerical score from 1 to 10 (with 10 being perfect/excellent and 1 being poor/inadequate) based on how well the asset meets the criteria. YOUR OUTPUT FORMAT: Include a JSON code block with the following fields: - "score": a number from 1 to 10 - "explanation": your detailed reasoning for the score - "recommendations": specific suggestions for improvement if applicable Example: ```json { "score": 8, "explanation": "The logo is clearly visible and occupies approximately 12% of the advertisement area, which exceeds the minimum requirement. It has good contrast against the background and would be recognizable from the appropriate viewing distance.", "recommendations": "Consider increasing contrast around the edges of the logo for even better visibility in darker environments." } ``` DEBUG: Main asset loaded successfully - dimensions: (693, 1024), mode: RGB DEBUG: Calling Gemini API for visual analysis ================================================================================ DEBUG: Gemini Vision API Call ================================================================================ DEBUG: Main asset image dimensions: (693, 1024) DEBUG: Main asset image mode: RGB DEBUG: No reference image provided DEBUG: Total images being sent to LLM: 1 (main asset only) DEBUG: Original prompt being sent to LLM: ---------------------------------------- Before assessment: Users often upload complex print production files with multiple panels, cut lines, and technical markings. Your task: Focus ONLY on the primary consumer-facing visual - what the end customer would actually see after production. Key instructions: • Ignore cut lines, registration marks, and production guides • If multiple panels exist, assess the main/largest consumer-facing design • Disregard technical text, color bars, or printer information • Focus on the final visual as it would appear on shelf/in-market • Treat folded/die-cut layouts as the assembled final product STEPS TO EVALUATE: 1. Examine the design components (logos, pack shots, icons, images, text blocks, promotion badges, etc.). Each distinct object or text grouping counts as one visual element that the viewer must actively notice. 2. Count the total number of these distinct visual elements. 3. If the count is more than 4, the design fails this checkpoint, as it may be too cluttered or complex. You are performing a visual quality-control check on a Point of Sale (POS) advertisement. Your task is to determine if any face(s) present are looking at the product or main message. This is crucial for assessing engagement potential. Follow these steps to evaluate: STEPS TO EVALUATE: Face Detection: Identify if there is at least one face in the advertisement. Eye Line Analysis: Determine if the line of sight of the face(s) is directed towards the product or main message. If the face is aligned towards the camera or another direction, classify as not looking at the product/message. Engagement Analysis: Based on eye line direction: "Looking at Product/Message" indicates high engagement potential. "Looking elsewhere" suggests low to medium engagement potential. YOUR OUTPUT: Indicate whether a face is present in the advertisement. If a face is present, state whether the advertisement "passes" or "fails" the eye-line-to-message alignment check. Include a JSON code block with these fields: { "face_present": true or false, "eye_line_alignment": "Pass" or "Fail" (only if face_present is true), "engagement_potential": "High" or "Low/Medium" (only if eye_line_alignment is "Pass") } SCORING INSTRUCTIONS: After your analysis, provide a numerical score from 1 to 10 (with 10 being perfect/excellent and 1 being poor/inadequate) based on how well the asset meets the criteria. YOUR OUTPUT FORMAT: Include a JSON code block with the following fields: - "score": a number from 1 to 10 - "explanation": your detailed reasoning for the score - "recommendations": specific suggestions for improvement if applicable Example: ```json { "score": 8, "explanation": "The logo is clearly visible and occupies approximately 12% of the advertisement area, which exceeds the minimum requirement. It has good contrast against the background and would be recognizable from the appropriate viewing distance.", "recommendations": "Consider increasing contrast around the edges of the logo for even better visibility in darker environments." } ``` ---------------------------------------- DEBUG: Using Gemini model: gemini-2.5-pro-preview-03-25 DEBUG: Added main asset image to parts list (dimensions: (693, 1024)) DEBUG: Total parts being sent to Gemini: 2 (1 text + 1 images) ---------------------------------------- DEBUG: Using Gemini model: gemini-2.5-pro-preview-03-25 DEBUG: Added main asset image to parts list (dimensions: (693, 1024)) DEBUG: Total parts being sent to Gemini: 2 (1 text + 1 images) DEBUG: Gemini Response received successfully: ---------------------------------------- **Call-to-Action Analysis** A call-to-action is present. * **Imperative Verb:** Yes, the CTA "Make it irresistibly RICH & CREAMY" begins with the imperative verb "Make". * **Word Count:** The CTA contains 6 words, which is under the 10-word limit. * **Spelling/Grammar:** The text is free of spelling and grammar errors. * **Encourages Action:** Yes, it directly instructs the consumer on how to use the product ("Make it...") to achieve a desirable outcome ("irresistibly rich & creamy"), effectively encouraging use and consumption. **Overall Assessment: Pass** The advertisement features a strong and effective call-to-action that meets all the required criteria. It is concise, uses a command verb, and clearly communicates the benefit of using the product. ```json { "cta_present": true, "uses_imperative_verb": true, "word_count": 6, "word_count_acceptable": true, "spelling_grammar_correct": true, "encourages_action": true, "overall_assessment": "Pass", "recommendations": [] } ``` **Scoring** ```json { "score": 10, "explanation": "The call-to-action 'Make it irresistibly RICH & CREAMY' is excellent. It perfectly meets all criteria: it starts with a strong imperative verb ('Make'), is concise at only 6 words, is grammatically correct, and effectively encourages the consumer to use the product to enhance their food. It is a clear, compelling, and actionable message.", "recommendations": [] } ``` ---------------------------------------- DEBUG: Response length: 1446 chars ================================================================================ DEBUG: Visual QC analysis completed successfully ==================================================================================================== Extracted score from JSON block: 10 Completed check: call_to_action (4/11) DEBUG: Gemini Response received successfully: ---------------------------------------- Here is the evaluation of the POS advertisement: **Visual Element Analysis:** * **Identified Elements:** 1. Image of the burger (hero shot) 2. Headline text block ("Make it irresistibly RICH & CREAMY", including the decorative bow tie icon) 3. Product packshot (the Hellmann's mayonnaise jar) * **Total Count:** 3 * **Result:** Pass **Explanation:** The design passes the simplified-design checkpoint because it contains a total of 3 distinct visual elements. This is below the maximum allowed count of 4. The composition is clean and focused, with a clear hierarchy that guides the viewer's eye from the appealing food image to the headline and finally to the product itself, making the message very quick and easy to understand. ```json { "visual_element_count": 3, "compliance_status": "Pass" } ``` **Scoring:** ```json { "score": 10, "explanation": "The asset perfectly adheres to the simplified-design principle by limiting the visual elements to just three: the hero image (burger), the headline, and the product shot. This creates a strong, uncluttered visual hierarchy that is extremely effective for point-of-sale advertising. The message is immediate and impactful.", "recommendations": "None. The asset is an excellent example of the principle being tested." } ``` ---------------------------------------- DEBUG: Response length: 1312 chars ================================================================================ DEBUG: Visual QC analysis completed successfully ==================================================================================================== Extracted score from JSON block: 10 Completed check: visual_elements_count (5/11) DEBUG: Gemini Response received successfully: ---------------------------------------- ```json { "background_contrast": "Pass", "recommended_adjustments": "None" } ``` ```json { "score": 10, "explanation": "The design is exceptionally effective for a POS advertisement. The primary visuals—the product shot (burger), the brand pack shot (mayonnaise jar), and the main headline—are all perfectly clear and legible. There is excellent color and brightness contrast between all key elements and the dark blue background, ensuring they 'pop' and are easily visible from the required 3-meter viewing distance. The brand logo is prominent on the packaging, which itself stands out clearly. The overall composition is clean, balanced, and highly appealing.", "recommendations": "None" } ```127.0.0.1 - - [07/Aug/2025 20:32:08] "GET /api/progress/20250807_203144 HTTP/1.1" 200 - ---------------------------------------- DEBUG: Response length: 702 chars ================================================================================ DEBUG: Visual QC analysis completed successfully ==================================================================================================== Extracted score from JSON block: 10 Completed check: background_contrast (6/11) DEBUG: Gemini Response received successfully: ---------------------------------------- Pass ```json { "word_count": 6, "checkpoint_result": "Pass" } ``` ```json { "score": 9, "explanation": "The asset performs excellently against the given criteria. The headline 'Make it irresistibly RICH & CREAMY' contains 6 words, which is within the 7-word limit, making the message concise and impactful. The design also adheres to the visual simplicity rule, containing exactly 4 primary elements (burger, product jar, text block, brand icon), which is the maximum allowed before being considered cluttered. The overall composition is balanced and effectively communicates the product's benefit.", "recommendations": "The design is very strong. As the number of visual elements is at the maximum threshold of 4, avoid adding any extra components (e.g., promotional flashes) in future adaptations to maintain its current clarity and effectiveness." } ``` ---------------------------------------- DEBUG: Response length: 866 chars ================================================================================ DEBUG: Visual QC analysis completed successfully ==================================================================================================== Extracted score from JSON block: 9 Completed check: word_count (7/11) DEBUG: Gemini Response received successfully: ---------------------------------------- This design has **4** distinct visual elements (the burger, the product jar, the headline text, and the bow icon). As the count is not more than 4, it **passes** the visual complexity checkpoint. *** A face is not present in the advertisement. Therefore, the advertisement **fails** the eye-line-to-message alignment check by default as there is no face to analyze. ```json { "face_present": false } ``` *** ```json { "score": 5, "explanation": "The asset performs well against one of the two primary evaluation criteria but fails the other. It passes the visual complexity test with a clear and focused layout containing exactly 4 key elements (burger, product, headline, icon). However, it fails the engagement analysis, which specifically checks for a human face directing the viewer's gaze to the product or message. Since no face is present, this condition is not met.", "recommendations": "To increase the score based on the specified criteria, the design would need to incorporate a human face with an eye-line directed towards the product or the burger. While the current design's focus on appetite appeal is a strong strategy, it does not fulfill the requirements of the eye-line engagement check." } ``` ---------------------------------------- DEBUG: Response length: 1226 chars ================================================================================ DEBUG: Visual QC analysis completed successfully ==================================================================================================== Extracted score from JSON block: 5 Completed check: face_gaze_direction (8/11) DEBUG: Gemini Response received successfully: ---------------------------------------- ```json { "imperative_verb_present": true, "detected_imperative_verbs": [ "Make" ], "checkpoint_result": "Pass" } ``` ```json { "score": 10, "explanation": "The asset is a perfect example of effective advertising based on the provided criteria. It passes the visual complexity check with exactly 4 distinct consumer-facing elements: 1) the burger image, 2) the headline text block, 3) the brand's bowtie icon, and 4) the product pack shot. This adheres to the maximum limit of 4, ensuring the design is focused and not cluttered. Furthermore, the analysis of the text confirms the presence of a strong imperative verb ('Make'), which serves as a clear and direct call to action to the consumer. The overall composition is excellent, with a strong visual hierarchy that guides the viewer's eye from the appetizing food to the call to action and the product itself.", "recommendations": "No recommendations are necessary. The design is highly effective and perfectly aligns with the evaluation criteria." } ``` ---------------------------------------- DEBUG: Response length: 1027 chars ================================================================================ DEBUG: Visual QC analysis completed successfully ==================================================================================================== Extracted score from JSON block: 10 Completed check: imperative_verb (9/11) DEBUG: Gemini Response received successfully: ---------------------------------------- Fail - No visible face detected in the image to evaluate for visibility at distance. ```json { "face_present": false } ``` ```json { "score": 5, "explanation": "The advertisement's composition is clean and effective, featuring exactly 4 primary visual elements (burger, product jar, headline, brand icon), which meets the guideline for avoiding clutter. However, the asset fails the second key evaluation criterion, as it does not contain a human face, which is a mandatory check. The score of 5 reflects that the asset passes one critical checkpoint (simplicity) but fails the other (face visibility).", "recommendations": "To meet all evaluation criteria, a version of this advertisement including a human face would be required. For instance, showing a person happily eating or about to eat the burger could fulfill the requirement and potentially increase the ad's emotional appeal." } ``` ---------------------------------------- DEBUG: Response length: 902 chars ================================================================================ DEBUG: Visual QC analysis completed successfully ==================================================================================================== Extracted score from JSON block: 5 Completed check: face_visibility (10/11) DEBUG: Gemini Response received successfully: ---------------------------------------- The text elements **Fail** the readability checkpoint. While the main headline ("Make it irresistibly RICH & CREAMY") and brand name are large, clear, and have excellent contrast, the smaller text on the product jar is not readable from the appropriate viewing distance for a large poster (~3 meters). Specifically, the claims "100% free range eggs" and "Committed to sustainably sourced oil" are too small to be legible. ```json { "text_readability": "Fail", "readability_score": "Medium", "recommendations": [ "The claims on the product jar (e.g., '100% free range eggs') are too small to be read from the required distance. If these are key messages, they should be presented as larger, separate text elements.", "Consider using a simplified packshot that removes or de-emphasizes small, illegible text to reduce visual noise." ] } ``` ```json { "score": 9, "explanation": "The design is highly effective with a clean layout of 4 key visual elements (burger, jar, headline, icon), preventing it from feeling cluttered. The primary headline and brand name have excellent readability due to their large size and high contrast against the background. The core message is communicated clearly and effectively. The score is not a perfect 10 because the secondary text on the product jar (e.g., '100% free range eggs') is too small to be legible from the standard viewing distance for a large poster.", "recommendations": "To achieve a perfect score, ensure all text, including product claims on the packshot, is legible from the intended viewing distance. This could be done by featuring key claims as separate, larger design elements." } ``` ---------------------------------------- DEBUG: Response length: 1665 chars ================================================================================127.0.0.1 - - [07/Aug/2025 20:32:34] "GET /api/progress/20250807_203144 HTTP/1.1" 200 - 127.0.0.1 - - [07/Aug/2025 20:32:55] "POST /api/start_analysis HTTP/1.1" 200 - DEBUG: Visual QC analysis completed successfully ==================================================================================================== Extracted score from JSON block: 9 Completed check: text_readability (11/11) Completed batch 1/1 Completed all 11 checks in 1 batches Step 4: Calculating overall score ERROR in background thread for session 20250807_203144: 'status' Exception type: Traceback: Traceback (most recent call last): File "/Users/nickviljoen/Desktop/Visual_AI_QC/api_server.py", line 1406, in run_analysis if result['status'] == 'success': ~~~~~~^^^^^^^^^^ KeyError: 'status' Starting analysis with profile: general_key_visual, brand: general, mode: html DEBUG: Raw mode parameter from request: 'html' DEBUG: Processed output_mode: 'html' Background thread started for session: 20250807_203255 Parameters: brand=general, profile=general_key_visual, mode=html DEBUG: analysis_mode = 'html' DEBUG: Before validation, analysis_mode = 'html' DEBUG: analysis_mode 'html' is valid DEBUG: After validation, analysis_mode = 'html' Using specified profile: general_key_visual Step 1: Running QC analysis with profile 'general_key_visual' Processing 25 checks in batches of 15 Processing batch 1/2 (15 checks) Running check 1/25: background_contrast ==================================================================================================== DEBUG: Visual QC Analysis Starting ==================================================================================================== DEBUG: Model selected: Gemini DEBUG: Asset file path: uploads/20250807_203255/image (1) 2.png DEBUG: No reference file provided DEBUG: Prompt length: 3866 characters DEBUG: File validation passed - both files exist DEBUG: Loading main asset image from: uploads/20250807_203255/image (1) 2.png Running check 4/25: curved_edges ==================================================================================================== DEBUG: Visual QC Analysis Starting ==================================================================================================== DEBUG: Model selected: Gemini DEBUG: Asset file path: uploads/20250807_203255/image (1) 2.png DEBUG: No reference file provided DEBUG: Prompt length: 2916 characters Running check 3/25: call_to_action ==================================================================================================== DEBUG: Visual QC Analysis Starting ==================================================================================================== DEBUG: Model selected: Gemini DEBUG: Asset file path: uploads/20250807_203255/image (1) 2.png DEBUG: No reference file provided DEBUG: Prompt length: 3663 characters Running check 5/25: curved_edges_digital ==================================================================================================== DEBUG: Visual QC Analysis Starting ==================================================================================================== DEBUG: Model selected: Gemini DEBUG: Asset file path: uploads/20250807_203255/image (1) 2.png DEBUG: No reference file provided DEBUG: Prompt length: 4538 characters DEBUG: File validation passed - both files exist DEBUG: Loading main asset image from: uploads/20250807_203255/image (1) 2.png DEBUG: File validation passed - both files exist DEBUG: Loading main asset image from: uploads/20250807_203255/image (1) 2.png Running check 6/25: curved_edges_print ==================================================================================================== DEBUG: Visual QC Analysis Starting ==================================================================================================== DEBUG: Model selected: Gemini DEBUG: Asset file path: uploads/20250807_203255/image (1) 2.png DEBUG: No reference file provided DEBUG: Prompt length: 4263 characters DEBUG: File validation passed - both files exist DEBUG: Loading main asset image from: uploads/20250807_203255/image (1) 2.png Running check 7/25: element_alignment ==================================================================================================== DEBUG: Visual QC Analysis Starting ==================================================================================================== DEBUG: Model selected: Gemini DEBUG: Asset file path: uploads/20250807_203255/image (1) 2.png DEBUG: No reference file provided DEBUG: Prompt length: 3631 characters DEBUG: File validation passed - both files exist DEBUG: Loading main asset image from: uploads/20250807_203255/image (1) 2.png Running check 8/25: face_gaze_direction ==================================================================================================== DEBUG: Visual QC Analysis Starting ==================================================================================================== DEBUG: Model selected: Gemini DEBUG: Asset file path: uploads/20250807_203255/image (1) 2.png DEBUG: No reference file provided DEBUG: Prompt length: 3053 characters DEBUG: File validation passed - both files exist DEBUG: Loading main asset image from: uploads/20250807_203255/image (1) 2.png Running check 9/25: face_visibility ==================================================================================================== DEBUG: Visual QC Analysis Starting ==================================================================================================== DEBUG: Model selected: Gemini DEBUG: Asset file path: uploads/20250807_203255/image (1) 2.png DEBUG: No reference file provided DEBUG: Prompt length: 3858 characters DEBUG: File validation passed - both files exist DEBUG: Loading main asset image from: uploads/20250807_203255/image (1) 2.png Running check 10/25: imperative_verb ==================================================================================================== DEBUG: Visual QC Analysis Starting ==================================================================================================== DEBUG: Model selected: Gemini DEBUG: Asset file path: uploads/20250807_203255/image (1) 2.png DEBUG: No reference file provided DEBUG: Prompt length: 3339 characters DEBUG: File validation passed - both files exist DEBUG: Loading main asset image from: uploads/20250807_203255/image (1) 2.png DEBUG: File validation passed - both files exist DEBUG: Loading main asset image from: uploads/20250807_203255/image (1) 2.png Running check 13/25: new_visibility ==================================================================================================== DEBUG: Visual QC Analysis Starting ==================================================================================================== DEBUG: Model selected: Gemini DEBUG: Asset file path: uploads/20250807_203255/image (1) 2.png DEBUG: No reference file provided DEBUG: Prompt length: 4536 characters Running check 12/25: lowercase_text ==================================================================================================== DEBUG: Visual QC Analysis Starting ==================================================================================================== DEBUG: Model selected: Gemini DEBUG: Asset file path: uploads/20250807_203255/image (1) 2.png DEBUG: No reference file provided DEBUG: Prompt length: 3148 characters DEBUG: File validation passed - both files exist DEBUG: Loading main asset image from: uploads/20250807_203255/image (1) 2.png DEBUG: File validation passed - both files exist DEBUG: Loading main asset image from: uploads/20250807_203255/image (1) 2.png Running check 14/25: product_visibility ==================================================================================================== DEBUG: Visual QC Analysis Starting ==================================================================================================== DEBUG: Model selected: Gemini DEBUG: Asset file path: uploads/20250807_203255/image (1) 2.png DEBUG: No reference file provided DEBUG: Prompt length: 3477 characters DEBUG: File validation passed - both files exist DEBUG: Loading main asset image from: uploads/20250807_203255/image (1) 2.png Running check 15/25: supporting_images ====================================================================================================Completed check: logo_visibility (1/25) Completed check: brand_assets_visibility (2/25) DEBUG: Visual QC Analysis Starting ==================================================================================================== DEBUG: Model selected: Gemini DEBUG: Asset file path: uploads/20250807_203255/image (1) 2.png DEBUG: No reference file provided DEBUG: Prompt length: 3582 characters DEBUG: File validation passed - both files exist DEBUG: Loading main asset image from: uploads/20250807_203255/image (1) 2.png DEBUG: Main asset loaded successfully - dimensions: (693, 1024), mode: RGB DEBUG: Calling Gemini API for visual analysis ================================================================================ DEBUG: Gemini Vision API Call ================================================================================ DEBUG: Main asset image dimensions: (693, 1024) DEBUG: Main asset image mode: RGB DEBUG: No reference image provided DEBUG: Total images being sent to LLM: 1 (main asset only) DEBUG: Original prompt being sent to LLM: ---------------------------------------- Before assessment: Users often upload complex print production files with multiple panels, cut lines, and technical markings. Your task: Focus ONLY on the primary consumer-facing visual - what the end customer would actually see after production. Key instructions: • Ignore cut lines, registration marks, and production guides • If multiple panels exist, assess the main/largest consumer-facing design • Disregard technical text, color bars, or printer information • Focus on the final visual as it would appear on shelf/in-market • Treat folded/die-cut layouts as the assembled final product STEPS TO EVALUATE: 1. Examine the design components (logos, pack shots, icons, images, text blocks, promotion badges, etc.). Each distinct object or text grouping counts as one visual element that the viewer must actively notice. 2. Count the total number of these distinct visual elements. 3. If the count is more than 4, the design fails this checkpoint, as it may be too cluttered or complex. You are conducting a visual quality-control check on a PRINT MATERIAL (magazine ad, brochure, poster, flyer, billboard, etc.). Your task is to determine whether the print advertisement utilizes curved outer edges instead of sharp angles, considering print-specific design requirements. PRINT-SPECIFIC EVALUATION CRITERIA: 1. **Print Production Considerations**: - Check if curved edges are compatible with print finishing processes (die-cutting, trimming) - Ensure curved elements won't be compromised by print bleed requirements - Consider how curved edges will appear in physical printed form 2. **Print Design Context**: - Evaluate curved edges in context of print layout and grid systems - Consider readability and visual hierarchy in print format - Assess how curved edges work with print typography and spacing 3. **Physical Medium Impact**: - Consider viewing distance typical for this print format - Evaluate tactile and dimensional aspects of curved edges in print - Check compatibility with print material substrate (paper, vinyl, etc.) STEPS TO EVALUATE: 1. Identify the specific print format (magazine, poster, billboard, etc.) 2. Examine the advertisement's overall design for curved edges 3. Assess curved edge implementation considering print production constraints 4. Evaluate visual impact at typical print viewing distances 5. Check for proper integration with print-specific design elements PRINT FORMAT CONSIDERATIONS: - **Magazines/Brochures**: Subtle curved elements that work with text flow - **Posters**: Bold curved edges visible from medium distance - **Billboards**: Large-scale curves readable from far distances - **Flyers**: Curved edges that enhance handheld readability YOUR OUTPUT: First, identify the print format type. Confirm whether curved edges are present and appropriate for the print medium. State whether the advertisement "passes" or "fails" the print-optimized curved-edge criteria. If it fails, provide 1-2 concise recommendations specific to print production. Finally, include a JSON code block with these fields: { "print_format_detected": "[detected print format]", "curved_edges_present": true or false, "print_production_compatible": true or false, "curved_edge_criteria": "Pass" or "Fail", "score": [1-10 rating], "recommendations": ["List of print-specific recommendations if applicable, else an empty array"] } SCORING INSTRUCTIONS: After your analysis, provide a numerical score from 1 to 10 (with 10 being perfect/excellent and 1 being poor/inadequate) based on how well the asset meets the criteria. YOUR OUTPUT FORMAT: Include a JSON code block with the following fields: - "score": a number from 1 to 10 - "explanation": your detailed reasoning for the score - "recommendations": specific suggestions for improvement if applicable Example: ```json { "score": 8, "explanation": "The logo is clearly visible and occupies approximately 12% of the advertisement area, which exceeds the minimum requirement. It has good contrast against the background and would be recognizable from the appropriate viewing distance.", "recommendations": "Consider increasing contrast around the edges of the logo for even better visibility in darker environments." } ``` ---------------------------------------- DEBUG: Using Gemini model: gemini-2.5-pro-preview-03-25 DEBUG: Added main asset image to parts list (dimensions: (693, 1024)) DEBUG: Total parts being sent to Gemini: 2 (1 text + 1 images) DEBUG: Main asset loaded successfully - dimensions: (693, 1024), mode: RGB DEBUG: Calling Gemini API for visual analysis ================================================================================ DEBUG: Gemini Vision API Call ================================================================================ DEBUG: Main asset image dimensions: (693, 1024) DEBUG: Main asset image mode: RGB DEBUG: No reference image provided DEBUG: Total images being sent to LLM: 1 (main asset only) DEBUG: Original prompt being sent to LLM: ---------------------------------------- Before assessment: Users often upload complex print production files with multiple panels, cut lines, and technical markings. Your task: Focus ONLY on the primary consumer-facing visual - what the end customer would actually see after production. Key instructions: • Ignore cut lines, registration marks, and production guides • If multiple panels exist, assess the main/largest consumer-facing design • Disregard technical text, color bars, or printer information • Focus on the final visual as it would appear on shelf/in-market • Treat folded/die-cut layouts as the assembled final product STEPS TO EVALUATE: 1. Examine the design components (logos, pack shots, icons, images, text blocks, promotion badges, etc.). Each distinct object or text grouping counts as one visual element that the viewer must actively notice. 2. Count the total number of these distinct visual elements. 3. If the count is more than 4, the design fails this checkpoint, as it may be too cluttered or complex. You are performing a visual quality-control check on an advertisement to verify the alignment of its elements. Your task is to determine whether all design elements are properly aligned according to professional design standards. ALIGNMENT CRITERIA: 1. Elements should be aligned horizontally and vertically where appropriate 2. Text blocks should have consistent alignment (left, right, center, or justified) 3. Related elements should share alignment points 4. Elements should align with a visible or implied grid structure 5. Centered elements should be precisely centered 6. Key alignment points include: - Edges (top, bottom, left, right) - Centers (horizontal and vertical) - Baselines (for text) STEPS TO EVALUATE: 1. Identify key elements in the design (logos, text blocks, images, buttons, etc.) 2. Check horizontal alignment of elements (left, center, right) 3. Check vertical alignment of elements (top, middle, bottom) 4. Look for any elements that appear misaligned or "off by a few pixels" 5. Check if text has consistent alignment (especially within the same block) 6. Check if centered elements are precisely centered 7. Look for any visible guides or grid systems and check if elements align with them YOUR OUTPUT: • State whether all elements appear properly aligned • If any misalignment is detected, identify the specific elements and alignment issues • Provide recommendations for correcting any alignment issues • Include a JSON code block with these fields: { "all_elements_properly_aligned": true or false, "alignment_check": "Pass" or "Fail", "misaligned_elements": ["List specific misaligned elements, if any, else an empty array"], "recommendations": ["List specific alignment recommendations if applicable, else an empty array"] } SCORING INSTRUCTIONS: After your analysis, provide a numerical score from 1 to 10 (with 10 being perfect/excellent and 1 being poor/inadequate) based on how well the asset meets the criteria. YOUR OUTPUT FORMAT: Include a JSON code block with the following fields: - "score": a number from 1 to 10 - "explanation": your detailed reasoning for the score - "recommendations": specific suggestions for improvement if applicable Example: ```json { "score": 8, "explanation": "The logo is clearly visible and occupies approximately 12% of the advertisement area, which exceeds the minimum requirement. It has good contrast against the background and would be recognizable from the appropriate viewing distance.", "recommendations": "Consider increasing contrast around the edges of the logo for even better visibility in darker environments." } ``` ---------------------------------------- DEBUG: Using Gemini model: gemini-2.5-pro-preview-03-25 DEBUG: Added main asset image to parts list (dimensions: (693, 1024)) DEBUG: Total parts being sent to Gemini: 2 (1 text + 1 images) DEBUG: Main asset loaded successfully - dimensions: (693, 1024), mode: RGB DEBUG: Calling Gemini API for visual analysis ================================================================================ DEBUG: Gemini Vision API Call ================================================================================ DEBUG: Main asset image dimensions: (693, 1024) DEBUG: Main asset image mode: RGB DEBUG: No reference image provided DEBUG: Total images being sent to LLM: 1 (main asset only) DEBUG: Original prompt being sent to LLM: ---------------------------------------- Before assessment: Users often upload complex print production files with multiple panels, cut lines, and technical markings. Your task: Focus ONLY on the primary consumer-facing visual - what the end customer would actually see after production. Key instructions: • Ignore cut lines, registration marks, and production guides • If multiple panels exist, assess the main/largest consumer-facing design • Disregard technical text, color bars, or printer information • Focus on the final visual as it would appear on shelf/in-market • Treat folded/die-cut layouts as the assembled final product STEPS TO EVALUATE: 1. Examine the design components (logos, pack shots, icons, images, text blocks, promotion badges, etc.). Each distinct object or text grouping counts as one visual element that the viewer must actively notice. 2. Count the total number of these distinct visual elements. 3. If the count is more than 4, the design fails this checkpoint, as it may be too cluttered or complex. You are tasked with performing a text analysis on a Point of Sale (POS) advertisement to determine if the advertisement text contains any imperative verbs (instructional commands such as "Buy", "Try", "Enjoy", "Discover"). You will validate the presence of imperative verbs according to the criteria below. CRITERIA TO EVALUATE: 1. Examine the text content of the advertisement. 2. Identify any verbs that are used as commands or instructions directed at the consumer. 3. Consider common imperative verbs like "Buy", "Try", "Enjoy", "Discover", or others that fit the context of providing an instruction. 4. Distinguish these imperative verbs from other verb forms that do not function as commands. STEPS TO PROCESS: 1. Text Analysis: 2. Scan through the provided text on the advertisement. 3. Look for verbs that are typically used to give commands. 4. Command Identification: Determine if any verbs are used in a commanding or instructive form. Result Validation: 5. Confirm the context of verbs to confidently classify them as imperative. YOUR OUTPUT: 1. State whether an imperative verb is present or not. 2. If an imperative verb is present, specify the verb(s) detected. 3. If not, indicate that no imperative verbs were found. 4. Present your findings in the following JSON code structure: { "imperative_verb_present": true or false, "detected_imperative_verbs": ["verb1", "verb2", ...] (only if imperative_verb_present is true) "checkpoint_result": "Pass" or "Fail" } SCORING INSTRUCTIONS: After your analysis, provide a numerical score from 1 to 10 (with 10 being perfect/excellent and 1 being poor/inadequate) based on how well the asset meets the criteria. YOUR OUTPUT FORMAT: Include a JSON code block with the following fields: - "score": a number from 1 to 10 - "explanation": your detailed reasoning for the score - "recommendations": specific suggestions for improvement if applicable Example: ```json { "score": 8, "explanation": "The logo is clearly visible and occupies approximately 12% of the advertisement area, which exceeds the minimum requirement. It has good contrast against the background and would be recognizable from the appropriate viewing distance.", "recommendations": "Consider increasing contrast around the edges of the logo for even better visibility in darker environments." } ``` ---------------------------------------- DEBUG: Using Gemini model: gemini-2.5-pro-preview-03-25 DEBUG: Added main asset image to parts list (dimensions: (693, 1024)) DEBUG: Total parts being sent to Gemini: 2 (1 text + 1 images) DEBUG: Main asset loaded successfully - dimensions: (693, 1024), mode: RGB DEBUG: Calling Gemini API for visual analysis ================================================================================ DEBUG: Gemini Vision API Call ================================================================================ DEBUG: Main asset image dimensions: (693, 1024) DEBUG: Main asset image mode: RGB DEBUG: No reference image provided DEBUG: Total images being sent to LLM: 1 (main asset only) DEBUG: Original prompt being sent to LLM: ---------------------------------------- Before assessment: Users often upload complex print production files with multiple panels, cut lines, and technical markings. Your task: Focus ONLY on the primary consumer-facing visual - what the end customer would actually see after production. Key instructions: • Ignore cut lines, registration marks, and production guides • If multiple panels exist, assess the main/largest consumer-facing design • Disregard technical text, color bars, or printer information • Focus on the final visual as it would appear on shelf/in-market • Treat folded/die-cut layouts as the assembled final product STEPS TO EVALUATE: 1. Examine the design components (logos, pack shots, icons, images, text blocks, promotion badges, etc.). Each distinct object or text grouping counts as one visual element that the viewer must actively notice. 2. Count the total number of these distinct visual elements. 3. If the count is more than 4, the design fails this checkpoint, as it may be too cluttered or complex. You are performing a visual quality-control check on a Point of Sale (POS) advertisement. Your task is to determine if any face(s) present are looking at the product or main message. This is crucial for assessing engagement potential. Follow these steps to evaluate: STEPS TO EVALUATE: Face Detection: Identify if there is at least one face in the advertisement. Eye Line Analysis: Determine if the line of sight of the face(s) is directed towards the product or main message. If the face is aligned towards the camera or another direction, classify as not looking at the product/message. Engagement Analysis: Based on eye line direction: "Looking at Product/Message" indicates high engagement potential. "Looking elsewhere" suggests low to medium engagement potential. YOUR OUTPUT: Indicate whether a face is present in the advertisement. If a face is present, state whether the advertisement "passes" or "fails" the eye-line-to-message alignment check. Include a JSON code block with these fields: { "face_present": true or false, "eye_line_alignment": "Pass" or "Fail" (only if face_present is true), "engagement_potential": "High" or "Low/Medium" (only if eye_line_alignment is "Pass") } SCORING INSTRUCTIONS: After your analysis, provide a numerical score from 1 to 10 (with 10 being perfect/excellent and 1 being poor/inadequate) based on how well the asset meets the criteria. YOUR OUTPUT FORMAT: Include a JSON code block with the following fields: - "score": a number from 1 to 10 - "explanation": your detailed reasoning for the score - "recommendations": specific suggestions for improvement if applicable Example: ```json { "score": 8, "explanation": "The logo is clearly visible and occupies approximately 12% of the advertisement area, which exceeds the minimum requirement. It has good contrast against the background and would be recognizable from the appropriate viewing distance.", "recommendations": "Consider increasing contrast around the edges of the logo for even better visibility in darker environments." } ``` ---------------------------------------- DEBUG: Using Gemini model: gemini-2.5-pro-preview-03-25 DEBUG: Added main asset image to parts list (dimensions: (693, 1024)) DEBUG: Total parts being sent to Gemini: 2 (1 text + 1 images) DEBUG: Main asset loaded successfully - dimensions: (693, 1024), mode: RGB DEBUG: Calling Gemini API for visual analysis ================================================================================ DEBUG: Gemini Vision API Call ================================================================================ DEBUG: Main asset image dimensions: (693, 1024) DEBUG: Main asset image mode: RGB DEBUG: No reference image provided DEBUG: Total images being sent to LLM: 1 (main asset only) DEBUG: Original prompt being sent to LLM: ---------------------------------------- Before assessment: Users often upload complex print production files with multiple panels, cut lines, and technical markings. Your task: Focus ONLY on the primary consumer-facing visual - what the end customer would actually see after production. Key instructions: • Ignore cut lines, registration marks, and production guides • If multiple panels exist, assess the main/largest consumer-facing design • Disregard technical text, color bars, or printer information • Focus on the final visual as it would appear on shelf/in-market • Treat folded/die-cut layouts as the assembled final product STEPS TO EVALUATE: 1. Examine the design components (logos, pack shots, icons, images, text blocks, promotion badges, etc.). Each distinct object or text grouping counts as one visual element that the viewer must actively notice. 2. Count the total number of these distinct visual elements. 3. If the count is more than 4, the design fails this checkpoint, as it may be too cluttered or complex. You are performing a visual quality-control check on a Point of Sale (POS) advertisement. Your task is to verify that the correct product is clearly shown in the advertisement. STEPS TO EVALUATE: 1. Product Identification: Examine the advertisement and identify the product(s) being shown. 2. Product Visibility Assessment: a. Is the product clearly visible and recognizable? b. Is the product shown from a flattering angle? c. Does the product occupy sufficient space in the overall composition? d. For packaged products: Is the packaging clearly visible with key identifiers (logo, variant, size)? 3. Product Accuracy Assessment: a. Do the visuals match the advertised product description/name? b. Is the product variant correct (if identifiable)? c. Is the product shown in its current packaging/design (not outdated)? 4. Product Presentation Quality: a. Is the product shown in high quality (sharp, well-lit, properly colored)? b. Does the product appear appealing and desirable? YOUR OUTPUT: • State whether the product is clearly visible in the advertisement. • Assess whether the product appears to be accurate and correctly represented. • Provide an overall pass/fail assessment based on product visibility and accuracy. • If the check fails, provide 1-2 concise recommendations for improvement. • Include a JSON code block with the following fields: { "product_clearly_visible": true or false, "product_correctly_represented": true or false, "product_check": "Pass" or "Fail", "recommendations": ["List specific recommendations if applicable, else an empty array"] } SCORING INSTRUCTIONS: After your analysis, provide a numerical score from 1 to 10 (with 10 being perfect/excellent and 1 being poor/inadequate) based on how well the asset meets the criteria. YOUR OUTPUT FORMAT: Include a JSON code block with the following fields: - "score": a number from 1 to 10 - "explanation": your detailed reasoning for the score - "recommendations": specific suggestions for improvement if applicable Example: ```json { "score": 8, "explanation": "The logo is clearly visible and occupies approximately 12% of the advertisement area, which exceeds the minimum requirement. It has good contrast against the background and would be recognizable from the appropriate viewing distance.", "recommendations": "Consider increasing contrast around the edges of the logo for even better visibility in darker environments." } ``` ---------------------------------------- DEBUG: Using Gemini model: gemini-2.5-pro-preview-03-25 DEBUG: Added main asset image to parts list (dimensions: (693, 1024)) DEBUG: Total parts being sent to Gemini: 2 (1 text + 1 images) DEBUG: Main asset loaded successfully - dimensions: (693, 1024), mode: RGB DEBUG: Calling Gemini API for visual analysis ================================================================================ DEBUG: Gemini Vision API Call ================================================================================ DEBUG: Main asset image dimensions: (693, 1024) DEBUG: Main asset image mode: RGB DEBUG: No reference image provided DEBUG: Total images being sent to LLM: 1 (main asset only) DEBUG: Original prompt being sent to LLM: ---------------------------------------- Before assessment: Users often upload complex print production files with multiple panels, cut lines, and technical markings. Your task: Focus ONLY on the primary consumer-facing visual - what the end customer would actually see after production. Key instructions: • Ignore cut lines, registration marks, and production guides • If multiple panels exist, assess the main/largest consumer-facing design • Disregard technical text, color bars, or printer information • Focus on the final visual as it would appear on shelf/in-market • Treat folded/die-cut layouts as the assembled final product STEPS TO EVALUATE: 1. Examine the design components (logos, pack shots, icons, images, text blocks, promotion badges, etc.). Each distinct object or text grouping counts as one visual element that the viewer must actively notice. 2. Count the total number of these distinct visual elements. 3. If the count is more than 4, the design fails this checkpoint, as it may be too cluttered or complex. You are conducting a strict visual quality‐control check on a Point of Sale (POS) advertisement to verify that crucial design elements—including product images, the brand logo, and key text—stand out crisply from the background at a typical viewing distance of 3 meters. EVALUATION STEPS: 1. Identify primary visuals: a. Brand logo (extra scrutiny for brand consistency and brightness/vibrancy). b. Product shots. c. Main text or headlines. 2. Check clarity at a maximum viewing distance (3 meters): a. Ensure the element remains legible or clearly visible without straining. 3. Evaluate color and brightness contrast: a. Visually verify that each key element "pops" against the background. b. Pay special attention to the brand logo. Any condition that makes the logo less than immediately recognizable at a glance is grounds for failing. c. If any key element is difficult to distinguish, consider contrast insufficient. 4. Use a color‐contrast analyzer (e.g., APCA): a. Confirm that larger elements meet or exceed ±Lc45. b. Confirm that smaller text or detailed logos/icons meet or exceed ±Lc75. c. Specifically check the brand logo area to ensure it meets or exceeds the recommended contrast level, highlighting any saturation/brightness issues. Decide pass/fail: • "Pass" only if ALL key elements (logo, product, text) remain sharp, legible, and clearly distinct from the background. • "Fail" if any single critical element has low contrast, appears fuzzy, or is overshadowed or faded relative to the background. Where possible, recommend one or two succinct improvement strategies (e.g., darkening the background, adding an outline/shadow, increasing the saturation of the logo colors). YOUR OUTPUT: • State whether the design "passes" or "fails" the background contrast checkpoint. • If it fails, list one concise way to improve contrast. • Format your response as a JSON code block with the structure: { "background_contrast": "[Pass or Fail]", "recommended_adjustments": "[None or Improvement]" } SCORING INSTRUCTIONS: After your analysis, provide a numerical score from 1 to 10 (with 10 being perfect/excellent and 1 being poor/inadequate) based on how well the asset meets the criteria. YOUR OUTPUT FORMAT: Include a JSON code block with the following fields: - "score": a number from 1 to 10 - "explanation": your detailed reasoning for the score - "recommendations": specific suggestions for improvement if applicable Example: ```json { "score": 8, "explanation": "The logo is clearly visible and occupies approximately 12% of the advertisement area, which exceeds the minimum requirement. It has good contrast against the background and would be recognizable from the appropriate viewing distance.", "recommendations": "Consider increasing contrast around the edges of the logo for even better visibility in darker environments." } ``` DEBUG: Main asset loaded successfully - dimensions: (693, 1024), mode: RGB DEBUG: Calling Gemini API for visual analysis ================================================================================ DEBUG: Gemini Vision API Call ================================================================================ DEBUG: Main asset image dimensions: (693, 1024) DEBUG: Main asset image mode: RGB DEBUG: No reference image provided DEBUG: Total images being sent to LLM: 1 (main asset only) DEBUG: Original prompt being sent to LLM: ---------------------------------------- Before assessment: Users often upload complex print production files with multiple panels, cut lines, and technical markings. Your task: Focus ONLY on the primary consumer-facing visual - what the end customer would actually see after production. Key instructions: • Ignore cut lines, registration marks, and production guides • If multiple panels exist, assess the main/largest consumer-facing design • Disregard technical text, color bars, or printer information • Focus on the final visual as it would appear on shelf/in-market • Treat folded/die-cut layouts as the assembled final product STEPS TO EVALUATE: 1. Examine the design components (logos, pack shots, icons, images, text blocks, promotion badges, etc.). Each distinct object or text grouping counts as one visual element that the viewer must actively notice. 2. Count the total number of these distinct visual elements. 3. If the count is more than 4, the design fails this checkpoint, as it may be too cluttered or complex. You are performing a visual quality-control check on a Point of Sale (POS) advertisement. Your task is to determine whether non-product images complement or dramatize the core message of the advertisement. STEPS TO EVALUATE: 1. Examine the advertisement's overall layout and identify any images present. 2. Differentiate between product images (pack shots and logos) and non-product images. 3. Assess whether the non-product images help communicate key attributes of the product, such as flavor, texture, scent, enjoyment, confidence, or pride. 4. Determine if the non-product images evoke emotions or sensations that enhance the advertisement's message beyond what the product images convey. 5. Consider how the non-product images add to the persuasiveness and emotive appeal of the advertisement. PASS/FAIL CRITERIA: Pass: The advertisement includes non-product images that clearly enhance or dramatize the core message through attributes such as emotion, flavor, texture, etc. Fail: The advertisement contains only product images, such as pack shots and logos, without any non-product imagery contributing to the core message. YOUR OUTPUT: 1. State whether non-product images are present. 2. If non-product images are present, state whether the advertisement "passes" or "fails" based on the criteria above. 3. If it fails, provide 1–2 concise recommendations for improving the advertisement's emotive appeal using non-product images. Include a JSON code block with these fields: { "non_product_images_present": true or false, "advertisement_complements_core_message": "Pass" or "Fail" (only if non_product_images_present is true), "recommendations": ["Recommendation 1", "Recommendation 2"] (only if "Fail") } SCORING INSTRUCTIONS: After your analysis, provide a numerical score from 1 to 10 (with 10 being perfect/excellent and 1 being poor/inadequate) based on how well the asset meets the criteria. YOUR OUTPUT FORMAT: Include a JSON code block with the following fields: - "score": a number from 1 to 10 - "explanation": your detailed reasoning for the score - "recommendations": specific suggestions for improvement if applicable Example: ```json { "score": 8, "explanation": "The logo is clearly visible and occupies approximately 12% of the advertisement area, which exceeds the minimum requirement. It has good contrast against the background and would be recognizable from the appropriate viewing distance.", "recommendations": "Consider increasing contrast around the edges of the logo for even better visibility in darker environments." } ``` ---------------------------------------- DEBUG: Using Gemini model: gemini-2.5-pro-preview-03-25 DEBUG: Added main asset image to parts list (dimensions: (693, 1024)) DEBUG: Total parts being sent to Gemini: 2 (1 text + 1 images) DEBUG: Main asset loaded successfully - dimensions: (693, 1024), mode: RGB DEBUG: Calling Gemini API for visual analysis ================================================================================ DEBUG: Gemini Vision API Call ================================================================================ DEBUG: Main asset image dimensions: (693, 1024) DEBUG: Main asset image mode: RGB DEBUG: No reference image provided DEBUG: Total images being sent to LLM: 1 (main asset only) DEBUG: Original prompt being sent to LLM: ---------------------------------------- Before assessment: Users often upload complex print production files with multiple panels, cut lines, and technical markings. Your task: Focus ONLY on the primary consumer-facing visual - what the end customer would actually see after production. Key instructions: • Ignore cut lines, registration marks, and production guides • If multiple panels exist, assess the main/largest consumer-facing design • Disregard technical text, color bars, or printer information • Focus on the final visual as it would appear on shelf/in-market • Treat folded/die-cut layouts as the assembled final product STEPS TO EVALUATE: 1. Examine the design components (logos, pack shots, icons, images, text blocks, promotion badges, etc.). Each distinct object or text grouping counts as one visual element that the viewer must actively notice. 2. Count the total number of these distinct visual elements. 3. If the count is more than 4, the design fails this checkpoint, as it may be too cluttered or complex. You are conducting a visual quality-control check on a DIGITAL MATERIAL (web banner, social media post, display ad, email graphic, website hero image, etc.). Your task is to determine whether the digital advertisement utilizes curved outer edges instead of sharp angles, considering digital-specific design requirements. DIGITAL-SPECIFIC EVALUATION CRITERIA: 1. **Digital Display Considerations**: - Check if curved edges render properly on various screen sizes and resolutions - Ensure curved elements work across different digital platforms and browsers - Consider how curves appear on mobile vs desktop displays 2. **Digital Design Context**: - Evaluate curved edges in context of responsive design principles - Consider interaction design and user experience implications - Assess how curved edges work with digital typography and UI elements 3. **Platform Optimization**: - Consider specific platform requirements (social media dimensions, ad networks) - Evaluate loading performance impact of curved edge graphics - Check compatibility with various digital formats (PNG, JPG, SVG, etc.) 4. **User Interface Integration**: - Assess how curved edges integrate with clickable elements - Consider accessibility implications for screen readers - Evaluate visual hierarchy in digital context STEPS TO EVALUATE: 1. Identify the specific digital format (banner, social post, display ad, etc.) 2. Examine the advertisement's curved edge implementation 3. Assess technical feasibility across digital platforms 4. Evaluate user experience and accessibility considerations 5. Check for proper integration with digital design standards DIGITAL FORMAT CONSIDERATIONS: - **Web Banners**: CSS-compatible curves that load efficiently - **Social Media**: Platform-optimized curves that work in feeds - **Display Ads**: Programmatic-friendly curved elements - **Email Graphics**: Email client compatible curved designs YOUR OUTPUT: First, identify the digital format type. Confirm whether curved edges are present and optimized for digital display. State whether the advertisement "passes" or "fails" the digital-optimized curved-edge criteria. If it fails, provide 1-2 concise recommendations specific to digital implementation. Finally, include a JSON code block with these fields: { "digital_format_detected": "[detected digital format]", "curved_edges_present": true or false, "digital_platform_optimized": true or false, "responsive_design_compatible": true or false, "curved_edge_criteria": "Pass" or "Fail", "score": [1-10 rating], "recommendations": ["List of digital-specific recommendations if applicable, else an empty array"] } SCORING INSTRUCTIONS: After your analysis, provide a numerical score from 1 to 10 (with 10 being perfect/excellent and 1 being poor/inadequate) based on how well the asset meets the criteria. YOUR OUTPUT FORMAT: Include a JSON code block with the following fields: - "score": a number from 1 to 10 - "explanation": your detailed reasoning for the score - "recommendations": specific suggestions for improvement if applicable Example: ```json { "score": 8, "explanation": "The logo is clearly visible and occupies approximately 12% of the advertisement area, which exceeds the minimum requirement. It has good contrast against the background and would be recognizable from the appropriate viewing distance.", "recommendations": "Consider increasing contrast around the edges of the logo for even better visibility in darker environments." } ``` ---------------------------------------- DEBUG: Using Gemini model: gemini-2.5-pro-preview-03-25 DEBUG: Added main asset image to parts list (dimensions: (693, 1024)) DEBUG: Total parts being sent to Gemini: 2 (1 text + 1 images) DEBUG: Main asset loaded successfully - dimensions: (693, 1024), mode: RGB DEBUG: Calling Gemini API for visual analysis ================================================================================ DEBUG: Gemini Vision API Call ================================================================================ DEBUG: Main asset image dimensions: (693, 1024) DEBUG: Main asset image mode: RGB DEBUG: No reference image provided DEBUG: Total images being sent to LLM: 1 (main asset only) DEBUG: Original prompt being sent to LLM: ---------------------------------------- Before assessment: Users often upload complex print production files with multiple panels, cut lines, and technical markings. Your task: Focus ONLY on the primary consumer-facing visual - what the end customer would actually see after production. Key instructions: • Ignore cut lines, registration marks, and production guides • If multiple panels exist, assess the main/largest consumer-facing design • Disregard technical text, color bars, or printer information • Focus on the final visual as it would appear on shelf/in-market • Treat folded/die-cut layouts as the assembled final product STEPS TO EVALUATE: 1. Examine the design components (logos, pack shots, icons, images, text blocks, promotion badges, etc.). Each distinct object or text grouping counts as one visual element that the viewer must actively notice. 2. Count the total number of these distinct visual elements. 3. If the count is more than 4, the design fails this checkpoint, as it may be too cluttered or complex. You are performing a visual quality-control check on a Point of Sale (POS) advertisement. Your task is to determine whether the text on the advertisement includes an effective call-to-action with the following criteria: 1. Uses an imperative verb (such as "Buy", "Try", "Get", "Discover", etc.) 2. Contains 10 words or less 3. Is free of spelling and grammar errors 4. Encourages the viewer to buy, use, or consume the product featured STEPS TO EVALUATE: 1. Examine the Text Content: Identify any text present on the advertisement, specifically focusing on the call-to-action. 2. Call-to-Action Analysis: a. Identify the main call-to-action text (if present) b. Check if it begins with or contains an imperative verb c. Count the number of words (must be 10 or fewer) d. Check for spelling and grammar errors 3. Effectiveness Check: a. Evaluate if the text includes clear persuasive language encouraging purchase, use, or consumption b. Assess whether it goes beyond merely being a product claim c. Determine if it inspires action YOUR OUTPUT: 1. State whether a call-to-action is present 2. If present: - Note if it uses an imperative verb - Count the words (should be 10 or fewer) - Verify it's free of spelling/grammar errors - Assess if it effectively encourages action 3. Provide an overall pass/fail assessment 4. If it fails, provide 1–2 concise recommendations for improvement 5. Include a JSON code block with these fields: { "cta_present": true or false, "uses_imperative_verb": true or false, "word_count": number, "word_count_acceptable": true or false, "spelling_grammar_correct": true or false, "encourages_action": true or false, "overall_assessment": "Pass" or "Fail", "recommendations": ["Recommendation 1", "Recommendation 2"] (if failed) } SCORING INSTRUCTIONS: After your analysis, provide a numerical score from 1 to 10 (with 10 being perfect/excellent and 1 being poor/inadequate) based on how well the asset meets the criteria. YOUR OUTPUT FORMAT: Include a JSON code block with the following fields: - "score": a number from 1 to 10 - "explanation": your detailed reasoning for the score - "recommendations": specific suggestions for improvement if applicable Example: ```json { "score": 8, "explanation": "The logo is clearly visible and occupies approximately 12% of the advertisement area, which exceeds the minimum requirement. It has good contrast against the background and would be recognizable from the appropriate viewing distance.", "recommendations": "Consider increasing contrast around the edges of the logo for even better visibility in darker environments." } ``` ---------------------------------------- DEBUG: Using Gemini model: gemini-2.5-pro-preview-03-25 DEBUG: Added main asset image to parts list (dimensions: (693, 1024)) DEBUG: Total parts being sent to Gemini: 2 (1 text + 1 images) ---------------------------------------- DEBUG: Using Gemini model: gemini-2.5-pro-preview-03-25 DEBUG: Added main asset image to parts list (dimensions: (693, 1024)) DEBUG: Total parts being sent to Gemini: 2 (1 text + 1 images) DEBUG: Main asset loaded successfully - dimensions: (693, 1024), mode: RGB DEBUG: Calling Gemini API for visual analysis ================================================================================ DEBUG: Gemini Vision API Call ================================================================================ DEBUG: Main asset image dimensions: (693, 1024) DEBUG: Main asset image mode: RGB DEBUG: No reference image provided DEBUG: Total images being sent to LLM: 1 (main asset only) DEBUG: Original prompt being sent to LLM: ---------------------------------------- Before assessment: Users often upload complex print production files with multiple panels, cut lines, and technical markings. Your task: Focus ONLY on the primary consumer-facing visual - what the end customer would actually see after production. Key instructions: • Ignore cut lines, registration marks, and production guides • If multiple panels exist, assess the main/largest consumer-facing design • Disregard technical text, color bars, or printer information • Focus on the final visual as it would appear on shelf/in-market • Treat folded/die-cut layouts as the assembled final product STEPS TO EVALUATE: 1. Examine the design components (logos, pack shots, icons, images, text blocks, promotion badges, etc.). Each distinct object or text grouping counts as one visual element that the viewer must actively notice. 2. Count the total number of these distinct visual elements. 3. If the count is more than 4, the design fails this checkpoint, as it may be too cluttered or complex. You are performing a visual quality-control check on a Point of Sale (POS) advertisement. Your task is to determine whether the face (with eyes) meets these criteria, based on its intended display size: • For large media (Floor-Standing Display Units, posters, etc.): The face (including eyes) must be clearly visible from about 3 meters away. • For smaller media (wobblers, shelf talkers, etc.): The face must be clearly visible from about 1 meter away. STEPS TO EVALUATE: Examine the advertisement's overall layout. Check whether at least one face is large enough and has sufficient contrast so that its eyes are distinguishable from the stated distance. Imagine viewing the design at a reduced scale or slightly out of focus—details of the eyes should still be discernible. If you can identify not just that it's a face but also clearly detect the eye region (i.e., tell where the eyes are) at the appropriate viewing distance, it passes this checkpoint. A face whose total area (or coverage) is below 8% of the entire ad might not be clearly visible. Consider this a FAIL unless the face's positioning or contrast compensates for the lower coverage. YOUR OUTPUT: • First, clearly state whether a face is present at all in the advertisement. • If NO face is present, state "Fail - No visible face detected in the image to evaluate for visibility at distance." • If a face IS present: - Describe the face's size and prominence in the design - State whether the design "passes" or "fails" the face‐visibility‐at‐distance checkpoint - Explain why it passes or fails based on the visibility criteria - If it fails, provide 1–2 concise recommendations for improving eye/face visibility • Finally, include a JSON code block with these fields: { "face_present": true or false, "face_visibility_at_distance": "Pass" or "Fail" (only if face_present is true), "face_coverage_percentage": (numerical estimate of the fraction of the total ad canvas occupied by the face, only if face_present is true) } SCORING INSTRUCTIONS: After your analysis, provide a numerical score from 1 to 10 (with 10 being perfect/excellent and 1 being poor/inadequate) based on how well the asset meets the criteria. YOUR OUTPUT FORMAT: Include a JSON code block with the following fields: - "score": a number from 1 to 10 - "explanation": your detailed reasoning for the score - "recommendations": specific suggestions for improvement if applicable Example: ```json { "score": 8, "explanation": "The logo is clearly visible and occupies approximately 12% of the advertisement area, which exceeds the minimum requirement. It has good contrast against the background and would be recognizable from the appropriate viewing distance.", "recommendations": "Consider increasing contrast around the edges of the logo for even better visibility in darker environments." } ``` ---------------------------------------- DEBUG: Using Gemini model: gemini-2.5-pro-preview-03-25 DEBUG: Added main asset image to parts list (dimensions: (693, 1024)) DEBUG: Total parts being sent to Gemini: 2 (1 text + 1 images) DEBUG: Main asset loaded successfully - dimensions: (693, 1024), mode: RGB DEBUG: Calling Gemini API for visual analysis ================================================================================ DEBUG: Gemini Vision API Call ================================================================================ DEBUG: Main asset image dimensions: (693, 1024) DEBUG: Main asset image mode: RGB DEBUG: No reference image provided DEBUG: Total images being sent to LLM: 1 (main asset only) DEBUG: Original prompt being sent to LLM: ---------------------------------------- Before assessment: Users often upload complex print production files with multiple panels, cut lines, and technical markings. Your task: Focus ONLY on the primary consumer-facing visual - what the end customer would actually see after production. Key instructions: • Ignore cut lines, registration marks, and production guides • If multiple panels exist, assess the main/largest consumer-facing design • Disregard technical text, color bars, or printer information • Focus on the final visual as it would appear on shelf/in-market • Treat folded/die-cut layouts as the assembled final product STEPS TO EVALUATE: 1. Examine the design components (logos, pack shots, icons, images, text blocks, promotion badges, etc.). Each distinct object or text grouping counts as one visual element that the viewer must actively notice. 2. Count the total number of these distinct visual elements. 3. If the count is more than 4, the design fails this checkpoint, as it may be too cluttered or complex. You are performing a visual quality-control check on a Point of Sale (POS) advertisement. Your task is to determine whether more than 50% of the visible words on the advertisement are in lowercase (or mixed case with predominantly lowercase letters). Do not consider logos, the word "NEW," or any text on product packaging in your assessment. STEPS TO EVALUATE: 1. Identify Words: Scan the advertisement to identify and count all visible, legible words excluding logos, the word "NEW," and text on packaging. 2. Classify Words: Count the number of words that are entirely in uppercase and the number of words that are in lowercase or sentence case (first letter capitalized). 3. Calculate Percentage: Determine if the percentage of lowercase/sentence case words is greater than 50% of the total count of words identified. 4. Lowercase Criteria: Number of lowercase/mixed case words divided by total number of words, multiplied by 100. Evaluate: 5. If more than 50% of the words are lowercase or mixed case with predominantly lowercase letters, the check is a "Pass." Otherwise, the check is a "Fail." YOUR OUTPUT: Conclude with a JSON object indicating the result of the lowercase validation: { "lowercase_percentage": , "validation_result": "Pass" or "Fail" } SCORING INSTRUCTIONS: After your analysis, provide a numerical score from 1 to 10 (with 10 being perfect/excellent and 1 being poor/inadequate) based on how well the asset meets the criteria. YOUR OUTPUT FORMAT: Include a JSON code block with the following fields: - "score": a number from 1 to 10 - "explanation": your detailed reasoning for the score - "recommendations": specific suggestions for improvement if applicable Example: ```json { "score": 8, "explanation": "The logo is clearly visible and occupies approximately 12% of the advertisement area, which exceeds the minimum requirement. It has good contrast against the background and would be recognizable from the appropriate viewing distance.", "recommendations": "Consider increasing contrast around the edges of the logo for even better visibility in darker environments." } ``` ---------------------------------------- DEBUG: Using Gemini model: gemini-2.5-pro-preview-03-25 DEBUG: Added main asset image to parts list (dimensions: (693, 1024)) DEBUG: Total parts being sent to Gemini: 2 (1 text + 1 images) DEBUG: Main asset loaded successfully - dimensions: (693, 1024), mode: RGB DEBUG: Calling Gemini API for visual analysis ================================================================================ DEBUG: Gemini Vision API Call ================================================================================ DEBUG: Main asset image dimensions: (693, 1024) DEBUG: Main asset image mode: RGB DEBUG: No reference image provided DEBUG: Total images being sent to LLM: 1 (main asset only) DEBUG: Original prompt being sent to LLM: ---------------------------------------- Before assessment: Users often upload complex print production files with multiple panels, cut lines, and technical markings. Your task: Focus ONLY on the primary consumer-facing visual - what the end customer would actually see after production. Key instructions: • Ignore cut lines, registration marks, and production guides • If multiple panels exist, assess the main/largest consumer-facing design • Disregard technical text, color bars, or printer information • Focus on the final visual as it would appear on shelf/in-market • Treat folded/die-cut layouts as the assembled final product STEPS TO EVALUATE: 1. Examine the design components (logos, pack shots, icons, images, text blocks, promotion badges, etc.). Each distinct object or text grouping counts as one visual element that the viewer must actively notice. 2. Count the total number of these distinct visual elements. 3. If the count is more than 4, the design fails this checkpoint, as it may be too cluttered or complex. You are conducting a visual quality-control check on a Point of Sale (POS) advertisement. Your task is to determine whether the advertisement utilizes curved outer edges instead of sharp angles. STEPS TO EVALUATE: Examine the advertisement's overall design. Identify any external edges in the advertisement. Determine if these edges are curved rather than sharp. Consider both structural and graphical aspects: Structural: Assure that any sharp-angled edges are replaced by curved edges. Graphical Design: Curved edges should direct attention inward and replace hard lines wherever possible. YOUR OUTPUT: First, confirm whether curved edges are present in the design. State whether the advertisement "passes" or "fails" the curved-edge criteria. If it fails, provide 1-2 concise recommendations for using curved edges in the design. Finally, include a JSON code block with these fields: { "curved_edges_present": true or false, "curved_edge_criteria": "Pass" or "Fail", "recommendations": ["List of recommendations if applicable, else an empty array"] } SCORING INSTRUCTIONS: After your analysis, provide a numerical score from 1 to 10 (with 10 being perfect/excellent and 1 being poor/inadequate) based on how well the asset meets the criteria. YOUR OUTPUT FORMAT: Include a JSON code block with the following fields: - "score": a number from 1 to 10 - "explanation": your detailed reasoning for the score - "recommendations": specific suggestions for improvement if applicable Example: ```json { "score": 8, "explanation": "The logo is clearly visible and occupies approximately 12% of the advertisement area, which exceeds the minimum requirement. It has good contrast against the background and would be recognizable from the appropriate viewing distance.", "recommendations": "Consider increasing contrast around the edges of the logo for even better visibility in darker environments." } ``` ---------------------------------------- DEBUG: Using Gemini model: gemini-2.5-pro-preview-03-25 DEBUG: Added main asset image to parts list (dimensions: (693, 1024)) DEBUG: Total parts being sent to Gemini: 2 (1 text + 1 images) DEBUG: Main asset loaded successfully - dimensions: (693, 1024), mode: RGB DEBUG: Calling Gemini API for visual analysis ================================================================================ DEBUG: Gemini Vision API Call ================================================================================ DEBUG: Main asset image dimensions: (693, 1024) DEBUG: Main asset image mode: RGB DEBUG: No reference image provided DEBUG: Total images being sent to LLM: 1 (main asset only) DEBUG: Original prompt being sent to LLM: ---------------------------------------- Before assessment: Users often upload complex print production files with multiple panels, cut lines, and technical markings. Your task: Focus ONLY on the primary consumer-facing visual - what the end customer would actually see after production. Key instructions: • Ignore cut lines, registration marks, and production guides • If multiple panels exist, assess the main/largest consumer-facing design • Disregard technical text, color bars, or printer information • Focus on the final visual as it would appear on shelf/in-market • Treat folded/die-cut layouts as the assembled final product STEPS TO EVALUATE: 1. Examine the design components (logos, pack shots, icons, images, text blocks, promotion badges, etc.). Each distinct object or text grouping counts as one visual element that the viewer must actively notice. 2. Count the total number of these distinct visual elements. 3. If the count is more than 4, the design fails this checkpoint, as it may be too cluttered or complex. You are performing a visual quality-control check on a Point of Sale (POS) advertisement. Your task is to determine whether the "NEW" graphic element meets these criteria, based on the display size: IMPORTANT: If there is no word containing "New" (in any case variation like "NEW", "New", "new") in the advertisement, then this QC check is not relevant to this file. In such cases, return a full score (Pass) for this app with appropriate JSON indicating no "NEW" element is present. • For large media (Floor-Standing Display Units, posters, etc.): The "NEW" graphic must be clearly visible and legible from a minimum distance of 3 meters. • For smaller media (wobblers, shelf talkers, etc.): The "NEW" graphic must be clearly visible and legible from a minimum distance of 1 meter. STEPS TO EVALUATE: 1. First, carefully scan the entire advertisement for any text containing "New" (case-insensitive: "NEW", "New", "new", etc.). 2. If NO "New" text is found anywhere in the advertisement: - This check is not applicable to this file - Return "Pass" with "new_present": false - No further evaluation needed 3. If "New" text IS found, proceed with the following steps: 4. Visibility and Legibility: Examine the advertisement's overall layout. 5. Check whether the "NEW" graphic is large enough and has sufficient contrast to be readable from the specified distance. 6. Consider if viewing the design at a reduced scale or slightly out of focus affects legibility. If you can clearly read the "NEW" text at the required distance, it passes this checkpoint. 7. Coverage Assessment: Estimate the area occupied by the "NEW" graphic as a percentage of the total ad space. A "NEW" graphic occupying less than 5% of the ad might not be clear. Consider this a fail unless the positioning or contrast compensates. 8. Recommendations: If the "NEW" graphic fails any criteria, provide 1–2 concise recommendations for improving its visibility and legibility. YOUR OUTPUT: State whether the "NEW" graphic is present. Visibility and Legibility Result: - If NO "New" text found: "Pass" (check not applicable) - If "New" text found: "Pass" or "Fail" based on visibility criteria Recommendations: If it fails, provide 1–2 concise recommendations for improvement. If no "New" text found, state "Check not applicable - no NEW element present." JSON Output: Include a JSON code block with the following fields: { "new_present": false (if no "New" text found) or true (if "New" text found), "new_visibility_legibility": "Pass" (always Pass if new_present is false, otherwise Pass or Fail), "new_coverage_percentage": "0" (if no "New" text found) or "XX" (actual percentage if found) } SCORING INSTRUCTIONS: After your analysis, provide a numerical score from 1 to 10 (with 10 being perfect/excellent and 1 being poor/inadequate) based on how well the asset meets the criteria. YOUR OUTPUT FORMAT: Include a JSON code block with the following fields: - "score": a number from 1 to 10 - "explanation": your detailed reasoning for the score - "recommendations": specific suggestions for improvement if applicable Example: ```json { "score": 8, "explanation": "The logo is clearly visible and occupies approximately 12% of the advertisement area, which exceeds the minimum requirement. It has good contrast against the background and would be recognizable from the appropriate viewing distance.", "recommendations": "Consider increasing contrast around the edges of the logo for even better visibility in darker environments." } ``` ---------------------------------------- DEBUG: Using Gemini model: gemini-2.5-pro-preview-03-25 DEBUG: Added main asset image to parts list (dimensions: (693, 1024)) DEBUG: Total parts being sent to Gemini: 2 (1 text + 1 images) DEBUG: Gemini Response received successfully: ---------------------------------------- ### Visual Element Count Assessment Based on the consumer-facing visual, the distinct elements are: 1. The burger (serving suggestion image). 2. The headline text block ("Make it irresistibly RICH & CREAMY"). 3. The small bowtie graphic element. 4. The Hellmann's mayonnaise jar (product shot). The total count of distinct visual elements is **4**. As this is not more than 4, the design **passes** this checkpoint. --- ### Product Visibility & Accuracy Assessment The product is clearly visible in the advertisement. The Hellmann's Real Mayonnaise jar is prominently displayed on the right side, with a clear and legible label featuring the brand name and product type. The product appears to be accurate and correctly represented. The packaging shown is the standard Hellmann's Real Mayonnaise jar, and the serving suggestion (the burger with mayonnaise) effectively and appealingly showcases the product in use, aligning with the "rich & creamy" headline. The overall presentation is high-quality and makes the product look desirable. **Overall Assessment: Pass** ```json { "product_clearly_visible": true, "product_correctly_represented": true, "product_check": "Pass", "recommendations": [] } ``` --- ### Scoring ```json { "score": 10, "explanation": "The advertisement perfectly meets all criteria. The product, a jar of Hellmann's Real Mayonnaise, is exceptionally clear, prominent, and occupies a significant portion of the visual space. The packaging details are sharp and legible. The product is shown accurately and is complemented by a highly appealing serving suggestion (the burger) that effectively communicates the product's key attributes of being 'rich & creamy'. The overall image quality, lighting, and composition are excellent, making the product look very desirable.", "recommendations": [] } ``` ---------------------------------------- DEBUG: Response length: 1846 chars ================================================================================ DEBUG: Visual QC analysis completed successfully ==================================================================================================== Extracted score from JSON block: 10 Completed check: product_visibility (3/25) DEBUG: Gemini Response received successfully: ---------------------------------------- There are 4 distinct visual elements: 1. The burger image. 2. The Hellmann's mayonnaise jar (product shot). 3. The headline text block ("Make it irresistibly RICH & CREAMY"). 4. The bow tie icon. The total count is 4. Since this is not more than 4, the design passes the visual element count check. No face is present in the advertisement. ```json { "face_present": false } ``` ```json { "score": 9, "explanation": "The advertisement effectively communicates its message without being cluttered. It features exactly 4 key visual elements (burger, product jar, headline, brand icon), which adheres to the maximum limit for a clear and focused design. The hierarchy is strong, with the appetizing food shot and the product itself serving as the primary heroes. The absence of a human face is a valid creative choice for this type of product, relying on appetite appeal rather than direct human engagement.", "recommendations": "No recommendations are necessary. The design is well-balanced and effectively uses a 'product as hero' strategy." } ``` ---------------------------------------- DEBUG: Response length: 1061 chars ================================================================================ DEBUG: Visual QC analysis completed successfully ==================================================================================================== Extracted score from JSON block: 9 Completed check: face_gaze_direction (4/25) DEBUG: Gemini Response received successfully: ---------------------------------------- Based on the first assessment for visual element count: 1. **Burger hero image:** The large, appetizing burger on the left. 2. **Hellmann's Mayonnaise pack shot:** The product jar on the right. 3. **Headline text block:** "Make it irresistibly RICH & CREAMY". 4. **Brand icon:** The small ribbon/bow tie icon below the headline. The total count of distinct visual elements is 4. Since this number is not more than 4, the design **passes** the visual complexity checkpoint. *** A face is **NOT** present in the advertisement. Fail - No visible face detected in the image to evaluate for visibility at distance. ```json { "face_present": false } ``` *** ```json { "score": 5, "explanation": "The asset performs well on one of the two key criteria but fails the other completely. It successfully maintains a clean, uncluttered layout with exactly 4 primary visual elements, passing the complexity check. However, it completely fails the face-visibility checkpoint as there is no human face included in the design to evaluate. The score of 5 reflects this mixed result: a pass on design simplicity and a fail on the required human element.", "recommendations": "To meet all evaluation criteria, the design would need to incorporate a human face. For example, showing a person enjoying the burger could fulfill this requirement, provided the face is large and clear enough to be visible from a distance." } ``` ---------------------------------------- DEBUG: Response length: 1424 chars ================================================================================ DEBUG: Visual QC analysis completed successfully ==================================================================================================== Extracted score from JSON block: 5 Completed check: face_visibility (5/25) DEBUG: Gemini Response received successfully: ---------------------------------------- ```json { "lowercase_percentage": 60.0, "validation_result": "Pass" } ``` ```json { "score": 10, "explanation": "The advertisement perfectly meets the criteria by featuring exactly four distinct visual elements: the hero burger image, the product packshot, the main headline text block ('Make it irresistibly RICH & CREAMY'), and the brand's bowtie icon. This count of 4 adheres to the guideline of not exceeding four elements, resulting in a clean, uncluttered, and highly effective design. The visual hierarchy is excellent, guiding the viewer's eye logically from the appetizing food to the product and key message without distraction.", "recommendations": "No recommendations are necessary. The asset is exceptionally well-balanced and effectively communicates its message while maintaining visual simplicity." } ``` ---------------------------------------- DEBUG: Response length: 835 chars ================================================================================ DEBUG: Visual QC analysis completed successfully ==================================================================================================== Extracted score from JSON block: 10 Completed check: lowercase_text (6/25) DEBUG: Gemini Response received successfully: ---------------------------------------- ```json { "imperative_verb_present": true, "detected_imperative_verbs": [ "Make" ], "checkpoint_result": "Pass" } ``` ```json { "score": 10, "explanation": "The design is exceptionally well-balanced and focused, containing exactly four distinct visual elements: 1. The hero shot of the burger, 2. The Hellmann's mayonnaise product shot, 3. The primary headline text ('Make it...'), and 4. The small ribbon icon. With a total count of 4, the asset meets the checkpoint criteria of being less than or equal to 4 elements, resulting in a clean, uncluttered, and highly effective visual. The hierarchy is clear, guiding the viewer's eye from the appetizing food to the product and its key benefit.", "recommendations": "No recommendations are necessary. The asset is an excellent example of adhering to the principle of visual simplicity for maximum impact." } ``` ---------------------------------------- DEBUG: Response length: 879 chars ================================================================================ DEBUG: Visual QC analysis completed successfully ==================================================================================================== Extracted score from JSON block: 10 Completed check: imperative_verb (7/25) DEBUG: Gemini Response received successfully: ---------------------------------------- Based on the visual element count, the design passes the initial checkpoint. * **Element 1:** Burger * **Element 2:** Headline text block ("Make it irresistibly RICH & CREAMY") * **Element 3:** Ribbon icon * **Element 4:** Hellmann's Mayonnaise jar The total count is 4, which does not exceed the maximum threshold. *** All elements appear properly aligned. The main text block ("Make it irresistibly RICH & CREAMY") and the ribbon icon below it are optically centered within the negative space between the burger and the product jar. This central alignment creates a strong, stable focal point for the advertisement. The alignment choices within the text block itself (e.g., the left-alignment of different lines) are stylistic and deliberate, contributing to the design's dynamic feel without appearing erroneous. The overall composition is balanced and professional. ```json { "all_elements_properly_aligned": true, "alignment_check": "Pass", "misaligned_elements": [], "recommendations": [] } ``` *** ```json { "score": 10, "explanation": "The advertisement demonstrates perfect alignment and composition. The design passes the complexity check with exactly 4 distinct visual elements (burger, headline, ribbon, product jar), which is the maximum acceptable count. The complexity is handled well due to excellent visual hierarchy. The central text block and ribbon are perfectly centered, creating a strong visual anchor. The overall layout is balanced and guides the viewer's eye effectively. There are no visible alignment errors.", "recommendations": [] } ``` ---------------------------------------- DEBUG: Response length: 1593 chars ================================================================================ DEBUG: Visual QC analysis completed successfully ==================================================================================================== Extracted score from JSON block: 10 Completed check: element_alignment (8/25) DEBUG: Gemini Response received successfully: ---------------------------------------- **"NEW" Graphic Present:** No **Visibility and Legibility Result:** Pass (check not applicable) **Recommendations:** Check not applicable - no NEW element present. **JSON Output:** ```json { "new_present": false, "new_visibility_legibility": "Pass", "new_coverage_percentage": "0" } ``` ```json { "score": 10, "explanation": "The advertisement was scanned for any text containing 'New' (case-insensitive). No 'NEW' graphic or text element was found on the poster or the product packaging. As per the instructions, since this check is not relevant to the provided file, a full score is awarded.", "recommendations": "No recommendations are needed as the 'NEW' graphic check is not applicable to this asset." } ``` ---------------------------------------- DEBUG: Response length: 728 chars ================================================================================ DEBUG: Visual QC analysis completed successfully ==================================================================================================== Extracted score from JSON block: 10 Completed check: new_visibility (9/25)127.0.0.1 - - [07/Aug/2025 20:33:36] "GET / HTTP/1.1" 200 - 127.0.0.1 - - [07/Aug/2025 20:33:36] "GET /api/brand_guidelines HTTP/1.1" 200 - 127.0.0.1 - - [07/Aug/2025 20:33:36] "GET /api/profiles HTTP/1.1" 200 - 127.0.0.1 - - [07/Aug/2025 20:33:36] "GET /api/output_files HTTP/1.1" 200 - 127.0.0.1 - - [07/Aug/2025 20:33:37] "GET / HTTP/1.1" 200 - 127.0.0.1 - - [07/Aug/2025 20:33:37] "GET /api/brand_guidelines HTTP/1.1" 200 - DEBUG: Gemini Response received successfully: ---------------------------------------- ```json { "background_contrast": "Pass", "recommended_adjustments": "None" } ``` ```json { "score": 10, "explanation": "The asset is exceptionally well-designed for a Point of Sale context. All key elements have outstanding contrast and clarity. The main headline ('Make it irresistibly RICH & CREAMY') uses a light color on a dark blue background, ensuring maximum legibility from a distance. Both the product shot (Hellmann's jar) and the serving suggestion (burger) are well-lit and pop against their respective backgrounds. The Hellmann's brand logo on the jar is perfectly clear and recognizable due to its high-contrast native design (dark blue on yellow), reinforcing brand identity effectively. The design is clean, uncluttered, and meets all criteria for visibility and impact at the specified viewing distance.", "recommendations": "None. The asset excels in clarity and contrast." } ``` ---------------------------------------- DEBUG: Response length: 903 chars ================================================================================ DEBUG: Visual QC analysis completed successfully ==================================================================================================== Extracted score from JSON block: 10 Completed check: background_contrast (10/25) DEBUG: Gemini Response received successfully: ---------------------------------------- Non-product images are present. Pass { "non_product_images_present": true, "advertisement_complements_core_message": "Pass" } ```json { "score": 10, "explanation": "The advertisement perfectly uses a non-product image (a large, appetizing burger) to dramatize the core message: 'Make it irresistibly RICH & CREAMY.' The image of the mayonnaise generously applied to the burger visually communicates the key product attributes of taste, texture, and richness. This serving suggestion evokes a strong sensory and emotional response (hunger, desire), making the advertisement highly persuasive and effective in a way that the product shot alone could not.", "recommendations": [] } ``` ---------------------------------------- DEBUG: Response length: 698 chars ================================================================================ DEBUG: Visual QC analysis completed successfully ==================================================================================================== Extracted score from JSON block: 10 Completed check: supporting_images (11/25) DEBUG: Gemini Response received successfully: ---------------------------------------- Based on the visual element count: 1. **Burger Image:** One element. 2. **Hellmann's Mayonnaise Jar (Product Shot):** One element. 3. **Headline Text ("Make it irresistibly RICH & CREAMY"):** One element. 4. **Ribbon Icon:** One element. The total count is 4 distinct visual elements. Since this is not more than 4, the design **passes** this checkpoint. *** Curved edges are not present in the design. The advertisement **fails** the curved-edge criteria. Recommendations: 1. Round the corners of the rectangular advertisement to soften its appearance and eliminate sharp points. 2. Consider using a die-cut shape that follows the natural curve of the burger bun on the left to create a more dynamic and engaging outline. ```json { "curved_edges_present": false, "curved_edge_criteria": "Fail", "recommendations": [ "Round the corners of the rectangular advertisement to soften its appearance and eliminate sharp points.", "Consider using a die-cut shape that follows the natural curve of the burger bun on the left to create a more dynamic and engaging outline." ] } ``` ```json { "score": 9, "explanation": "The design is well-balanced with exactly 4 primary visual elements: the hero burger image, the Hellmann's product shot, the main headline text, and the small ribbon icon. This count adheres to the '4 or fewer' rule, ensuring the design is focused and not cluttered.", "recommendations": [] } ``` ---------------------------------------- DEBUG: Response length: 1439 chars ================================================================================ DEBUG: Visual QC analysis completed successfully ==================================================================================================== Extracted score from JSON block: 9 Completed check: curved_edges (12/25) Loaded profile 'Unilever Key Visual' from /Users/nickviljoen/Desktop/Visual_AI_QC/profiles/unilever_key_visual.json Loaded profile 'Diageo Packaging' from /Users/nickviljoen/Desktop/Visual_AI_QC/profiles/diageo_packaging.json Loaded profile 'General Packaging' from /Users/nickviljoen/Desktop/Visual_AI_QC/profiles/general_packaging.json Loaded profile 'Inclusive Accessibility Profile' from /Users/nickviljoen/Desktop/Visual_AI_QC/profiles/inclusive_accessibility.json Loaded profile 'General Key Visual' from /Users/nickviljoen/Desktop/Visual_AI_QC/profiles/general_key_visual.json Loaded profile 'Unilever Packaging' from /Users/nickviljoen/Desktop/Visual_AI_QC/profiles/unilever_packaging.json Loaded profile 'All Checks' from /Users/nickviljoen/Desktop/Visual_AI_QC/profiles/default.json Loaded profile 'Diageo Key Visual' from /Users/nickviljoen/Desktop/Visual_AI_QC/profiles/diageo_key_visual.json DEBUG: Gemini Response received successfully: ---------------------------------------- **Call-to-Action Assessment** 1. **Call-to-Action Presence**: A call-to-action is present on the advertisement. 2. **Analysis**: * The primary call-to-action is: "Make it irresistibly RICH & CREAMY". * **Imperative Verb**: It uses the clear imperative verb "Make". * **Word Count**: The CTA contains 6 words, which is well under the 10-word limit. * **Spelling & Grammar**: The text is free of any spelling or grammatical errors. * **Effectiveness**: The CTA is highly effective. It directly instructs the consumer on how to use the product ("Make it...") to achieve a desirable outcome ("irresistibly rich & creamy"), which is visually reinforced by the delicious-looking burger. It successfully encourages use and consumption. 3. **Overall Assessment**: Pass. The call-to-action meets all the specified criteria for an effective CTA. ```json { "cta_present": true, "uses_imperative_verb": true, "word_count": 6, "word_count_acceptable": true, "spelling_grammar_correct": true, "encourages_action": true, "overall_assessment": "Pass", "recommendations": [] } ``` **Scoring** ```json { "score": 10, "explanation": "The advertisement features a highly effective call-to-action: 'Make it irresistibly RICH & CREAMY'. It perfectly meets all criteria: it begins with a strong imperative verb ('Make'), is concise at 6 words, is free of errors, and clearly encourages the consumer to use the product to enhance their food. The CTA is seamlessly integrated with the visual, creating a compelling message that inspires action.", "recommendations": null } ``` ---------------------------------------- DEBUG: Response length: 1610 chars ================================================================================ DEBUG: Visual QC analysis completed successfully ==================================================================================================== Extracted score from JSON block: 10 Completed check: call_to_action (13/25) Loaded profile 'Unilever Key Visual' from /Users/nickviljoen/Desktop/Visual_AI_QC/profiles/unilever_key_visual.json Loaded profile 'Diageo Packaging' from /Users/nickviljoen/Desktop/Visual_AI_QC/profiles/diageo_packaging.json Loaded profile 'General Packaging' from /Users/nickviljoen/Desktop/Visual_AI_QC/profiles/general_packaging.json Loaded profile 'Inclusive Accessibility Profile' from /Users/nickviljoen/Desktop/Visual_AI_QC/profiles/inclusive_accessibility.json Loaded profile 'General Key Visual' from /Users/nickviljoen/Desktop/Visual_AI_QC/profiles/general_key_visual.json Loaded profile 'Unilever Packaging' from /Users/nickviljoen/Desktop/Visual_AI_QC/profiles/unilever_packaging.json Loaded profile 'All Checks' from /Users/nickviljoen/Desktop/Visual_AI_QC/profiles/default.json127.0.0.1 - - [07/Aug/2025 20:33:37] "GET /api/profiles HTTP/1.1" 200 - 127.0.0.1 - - [07/Aug/2025 20:33:37] "GET /api/output_files HTTP/1.1" 200 - Loaded profile 'Diageo Key Visual' from /Users/nickviljoen/Desktop/Visual_AI_QC/profiles/diageo_key_visual.json DEBUG: Gemini Response received successfully: ---------------------------------------- Digital Format: Social Media Story Ad / Mobile Display Banner Curved edges are not present. The advertisement is framed by a standard rectangle with sharp 90-degree corners. As there are no curved edges, their optimization for digital display cannot be assessed. Fail. The advertisement does not meet the specified criteria of utilizing curved outer edges instead of sharp angles. Recommendations: 1. To align with modern UI trends often seen in social media feeds and web design, consider applying subtle rounded corners to the ad's main container. 2. If this were a web banner, this effect should be implemented using CSS `border-radius` to ensure it is lightweight, responsive, and renders cleanly across devices without image-based masking. ```json { "score": 2, "explanation": "The advertisement fails the specific check because it does not utilize curved outer edges, instead opting for sharp 90-degree corners. While a standard rectangle is universally compatible and technically sound, it does not fulfill the core requirement of this evaluation. The score is low as the primary design feature being assessed is entirely absent.", "recommendations": [ "Incorporate subtle rounded corners to the outer frame to give the ad a more modern feel that integrates better with current digital interface designs.", "For implementation in web or display advertising, use CSS `border-radius` rather than an image mask to apply curves, ensuring faster load times and perfect rendering at any resolution." ] } ``` ---------------------------------------- DEBUG: Response length: 1528 chars ================================================================================ DEBUG: Visual QC analysis completed successfully ==================================================================================================== Extracted score from JSON block: 2 Completed check: curved_edges_digital (14/25) DEBUG: Gemini Response received successfully: ---------------------------------------- Print Format: Magazine Ad / Poster Curved outer edges are not present in this advertisement's design. The layout is a standard rectangle, indicated by the corner trim marks, which is intended for a traditional straight-cut finish. This approach is highly appropriate for the print medium (magazine or poster) as it is production-friendly and aligns with standard publication formats. The advertisement passes the print-optimized curved-edge criteria. The choice to use a conventional rectangular shape is optimal for this context, ensuring cost-effective and trouble-free printing and finishing without the need for complex die-cutting. ```json { "print_format_detected": "Magazine Ad / Poster", "curved_edges_present": false, "print_production_compatible": true, "curved_edge_criteria": "Pass", "score": 9, "recommendations": [] } ``` ---------------------------------------- DEBUG: Response length: 850 chars ================================================================================ DEBUG: Visual QC analysis completed successfully ==================================================================================================== Extracted score from JSON block: 9 Completed check: curved_edges_print (15/25) Completed batch 1/2 Processing batch 2/2 (10 checks) Running check 16/25: text_readability ==================================================================================================== DEBUG: Visual QC Analysis Starting ==================================================================================================== DEBUG: Model selected: Gemini DEBUG: Asset file path: uploads/20250807_203255/image (1) 2.png DEBUG: No reference file provided DEBUG: Prompt length: 3563 characters Running check 17/25: visual_elements_count ==================================================================================================== DEBUG: Visual QC Analysis Starting ==================================================================================================== DEBUG: Model selected: Gemini DEBUG: Asset file path: uploads/20250807_203255/image (1) 2.png DEBUG: No reference file provided DEBUG: Prompt length: 3714 characters DEBUG: File validation passed - both files exist DEBUG: Loading main asset image from: uploads/20250807_203255/image (1) 2.png DEBUG: File validation passed - both files exist DEBUG: Loading main asset image from: uploads/20250807_203255/image (1) 2.png Running check 20/25: word_count ==================================================================================================== DEBUG: Visual QC Analysis Starting ==================================================================================================== DEBUG: Model selected: Gemini DEBUG: Asset file path: uploads/20250807_203255/image (1) 2.png DEBUG: No reference file provided DEBUG: Prompt length: 2700 characters Running check 19/25: visuals_left_text_right ==================================================================================================== DEBUG: Visual QC Analysis Starting ==================================================================================================== DEBUG: Model selected: Gemini DEBUG: Asset file path: uploads/20250807_203255/image (1) 2.png DEBUG: No reference file provided DEBUG: Prompt length: 2856 characters DEBUG: File validation passed - both files exist DEBUG: Loading main asset image from: uploads/20250807_203255/image (1) 2.png DEBUG: File validation passed - both files exist DEBUG: Loading main asset image from: uploads/20250807_203255/image (1) 2.png Running check 21/25: responsiveness ==================================================================================================== DEBUG: Visual QC Analysis Starting ==================================================================================================== DEBUG: Model selected: Gemini DEBUG: Asset file path: uploads/20250807_203255/image (1) 2.png DEBUG: No reference file provided DEBUG: Prompt length: 3894 characters DEBUG: File validation passed - both files exist DEBUG: Loading main asset image from: uploads/20250807_203255/image (1) 2.png Running check 22/25: dark_mode_legibility ==================================================================================================== DEBUG: Visual QC Analysis Starting ==================================================================================================== DEBUG: Model selected: Gemini DEBUG: Asset file path: uploads/20250807_203255/image (1) 2.png DEBUG: No reference file provided DEBUG: Prompt length: 3716 characters DEBUG: File validation passed - both files exist DEBUG: Loading main asset image from: uploads/20250807_203255/image (1) 2.png Running check 23/25: file_naming ==================================================================================================== DEBUG: Visual QC Analysis Starting ==================================================================================================== DEBUG: Model selected: Gemini DEBUG: Asset file path: uploads/20250807_203255/image (1) 2.png DEBUG: No reference file provided DEBUG: Prompt length: 3404 characters DEBUG: File validation passed - both files exist DEBUG: Loading main asset image from: uploads/20250807_203255/image (1) 2.png Running check 24/25: print_bleed ==================================================================================================== DEBUG: Visual QC Analysis Starting ==================================================================================================== DEBUG: Model selected: Gemini DEBUG: Asset file path: uploads/20250807_203255/image (1) 2.png DEBUG: No reference file provided DEBUG: Prompt length: 3636 characters DEBUG: File validation passed - both files exist DEBUG: Loading main asset image from: uploads/20250807_203255/image (1) 2.png Running check 25/25: crop_marks ==================================================================================================== DEBUG: Visual QC Analysis StartingCompleted check: visual_hierarchy (16/25) ==================================================================================================== DEBUG: Model selected: Gemini DEBUG: Asset file path: uploads/20250807_203255/image (1) 2.png DEBUG: No reference file provided DEBUG: Prompt length: 3594 characters DEBUG: File validation passed - both files exist DEBUG: Loading main asset image from: uploads/20250807_203255/image (1) 2.png DEBUG: Main asset loaded successfully - dimensions: (693, 1024), mode: RGB DEBUG: Calling Gemini API for visual analysis ================================================================================ DEBUG: Gemini Vision API Call ================================================================================ DEBUG: Main asset image dimensions: (693, 1024) DEBUG: Main asset image mode: RGB DEBUG: No reference image provided DEBUG: Total images being sent to LLM: 1 (main asset only) DEBUG: Original prompt being sent to LLM: ---------------------------------------- Before assessment: Users often upload complex print production files with multiple panels, cut lines, and technical markings. Your task: Focus ONLY on the primary consumer-facing visual - what the end customer would actually see after production. Key instructions: • Ignore cut lines, registration marks, and production guides • If multiple panels exist, assess the main/largest consumer-facing design • Disregard technical text, color bars, or printer information • Focus on the final visual as it would appear on shelf/in-market • Treat folded/die-cut layouts as the assembled final product STEPS TO EVALUATE: 1. Examine the design components (logos, pack shots, icons, images, text blocks, promotion badges, etc.). Each distinct object or text grouping counts as one visual element that the viewer must actively notice. 2. Count the total number of these distinct visual elements. 3. If the count is more than 4, the design fails this checkpoint, as it may be too cluttered or complex. You are performing a visual quality-control check on a POS advertisement. The brand wants to limit the design to no more than 4 distinct visual elements so that the message is clear and quick to process. IMPORTANT PRE-ASSESSMENT INSTRUCTIONS: Before assessment: Users often upload complex print production files with multiple panels, cut lines, and technical markings. Your task: Focus ONLY on the primary consumer-facing visual - what the end customer would actually see after production. Key instructions: - Ignore cut lines, registration marks, and production guides - If multiple panels exist, assess the main/largest consumer-facing design - Disregard technical text, color bars, or printer information - Focus on the final visual as it would appear on shelf/in-market - Treat folded/die-cut layouts as the assembled final product STEPS TO EVALUATE: 1. Examine the design components (logos, pack shots, icons, images, text blocks, promotion badges, etc.). Each distinct object or text grouping counts as one visual element that the viewer must actively notice. 2. Count the total number of these distinct visual elements. 3. If the count is more than 4, the design fails this checkpoint, as it may be too cluttered or complex. Otherwise, it passes. YOUR OUTPUT: • List all distinct visual elements you identified in the design (e.g., "1. Logo, 2. Product image, 3. Main headline...). • State the total count of visual elements found. • State whether the design "passes" or "fails" the simplified-design checkpoint. • Provide a clear explanation of why it passes or fails based on the element count. • If it fails, provide 1–2 concise recommendations for reducing or combining elements to achieve clarity. • Include a JSON code block with two fields: "visual_element_count": the total number of visual elements "compliance_status": "Pass" or "Fail" SCORING INSTRUCTIONS: After your analysis, provide a numerical score from 1 to 10 (with 10 being perfect/excellent and 1 being poor/inadequate) based on how well the asset meets the criteria. YOUR OUTPUT FORMAT: Include a JSON code block with the following fields: - "score": a number from 1 to 10 - "explanation": your detailed reasoning for the score - "recommendations": specific suggestions for improvement if applicable Example: ```json { "score": 8, "explanation": "The logo is clearly visible and occupies approximately 12% of the advertisement area, which exceeds the minimum requirement. It has good contrast against the background and would be recognizable from the appropriate viewing distance.", "recommendations": "Consider increasing contrast around the edges of the logo for even better visibility in darker environments." } ``` ---------------------------------------- DEBUG: Using Gemini model: gemini-2.5-pro-preview-03-25 DEBUG: Added main asset image to parts list (dimensions: (693, 1024)) DEBUG: Total parts being sent to Gemini: 2 (1 text + 1 images) DEBUG: Main asset loaded successfully - dimensions: (693, 1024), mode: RGB DEBUG: Calling Gemini API for visual analysis ================================================================================ DEBUG: Gemini Vision API Call ================================================================================ DEBUG: Main asset image dimensions: (693, 1024) DEBUG: Main asset image mode: RGB DEBUG: No reference image provided DEBUG: Total images being sent to LLM: 1 (main asset only) DEBUG: Original prompt being sent to LLM: ---------------------------------------- Before assessment: Users often upload complex print production files with multiple panels, cut lines, and technical markings. Your task: Focus ONLY on the primary consumer-facing visual - what the end customer would actually see after production. Key instructions: • Ignore cut lines, registration marks, and production guides • If multiple panels exist, assess the main/largest consumer-facing design • Disregard technical text, color bars, or printer information • Focus on the final visual as it would appear on shelf/in-market • Treat folded/die-cut layouts as the assembled final product STEPS TO EVALUATE: 1. Examine the design components (logos, pack shots, icons, images, text blocks, promotion badges, etc.). Each distinct object or text grouping counts as one visual element that the viewer must actively notice. 2. Count the total number of these distinct visual elements. 3. If the count is more than 4, the design fails this checkpoint, as it may be too cluttered or complex. You are performing a visual quality-control check on digital content to verify its legibility in dark mode. Your task is to determine whether all elements and copy remain legible when viewed in dark mode. DARK MODE LEGIBILITY CRITERIA: 1. All text should maintain sufficient contrast against dark backgrounds 2. Logos and brand elements should remain visible and clear 3. Visual hierarchy should be maintained in dark mode 4. Images and graphics should have appropriate brightness/contrast 5. Interactive elements should be clearly distinguishable 6. No elements should "disappear" or blend into the background 7. Color meaning and brand identity should be preserved STEPS TO EVALUATE: 1. Determine if the image shows content in dark mode 2. If dark mode is visible, assess: a. Text contrast and legibility b. Logo and brand element visibility c. Visual hierarchy maintenance d. Image and graphic clarity e. Interactive element distinguishability 3. If dark mode is not visible, state that "Dark mode legibility could not be assessed from this image." YOUR OUTPUT: • State whether dark mode content is visible in the image • If visible, evaluate each legibility criterion • State whether the design "passes" or "fails" the dark mode legibility check • Include a JSON code block with these fields: { "dark_mode_visible": true or false, "text_legibility": "Pass" or "Fail" or "Not applicable", "brand_element_visibility": "Pass" or "Fail" or "Not applicable", "visual_hierarchy_maintained": "Pass" or "Fail" or "Not applicable", "interactive_element_clarity": "Pass" or "Fail" or "Not applicable", "dark_mode_legibility_check": "Pass" or "Fail" or "Not applicable", "issues": ["List specific issues identified, if any, else an empty array"], "recommendations": ["List specific recommendations if applicable, else an empty array"] } SCORING INSTRUCTIONS: After your analysis, provide a numerical score from 1 to 10 (with 10 being perfect/excellent and 1 being poor/inadequate) based on how well the asset meets the criteria. YOUR OUTPUT FORMAT: Include a JSON code block with the following fields: - "score": a number from 1 to 10 - "explanation": your detailed reasoning for the score - "recommendations": specific suggestions for improvement if applicable Example: ```json { "score": 8, "explanation": "The logo is clearly visible and occupies approximately 12% of the advertisement area, which exceeds the minimum requirement. It has good contrast against the background and would be recognizable from the appropriate viewing distance.", "recommendations": "Consider increasing contrast around the edges of the logo for even better visibility in darker environments." } ``` DEBUG: Main asset loaded successfully - dimensions: (693, 1024), mode: RGB DEBUG: Calling Gemini API for visual analysis ================================================================================ DEBUG: Gemini Vision API Call ================================================================================ DEBUG: Main asset image dimensions: (693, 1024) DEBUG: Main asset image mode: RGB DEBUG: No reference image provided DEBUG: Total images being sent to LLM: 1 (main asset only) DEBUG: Original prompt being sent to LLM: ---------------------------------------- Before assessment: Users often upload complex print production files with multiple panels, cut lines, and technical markings. Your task: Focus ONLY on the primary consumer-facing visual - what the end customer would actually see after production. Key instructions: • Ignore cut lines, registration marks, and production guides • If multiple panels exist, assess the main/largest consumer-facing design • Disregard technical text, color bars, or printer information • Focus on the final visual as it would appear on shelf/in-market • Treat folded/die-cut layouts as the assembled final product STEPS TO EVALUATE: 1. Examine the design components (logos, pack shots, icons, images, text blocks, promotion badges, etc.). Each distinct object or text grouping counts as one visual element that the viewer must actively notice. 2. Count the total number of these distinct visual elements. 3. If the count is more than 4, the design fails this checkpoint, as it may be too cluttered or complex. You are performing a visual quality-control check on a file to verify its naming convention. Your task is to determine whether the filename follows the required naming pattern. REQUIRED NAMING PATTERN: The filename should follow this convention: JobNumber_Brand_DeliverableType_Location_Publication_Spec_NumberOfDeliverables For example: 12345_CocaCola_Billboard_NYC_TimesSquare_8x10_1of3 STEPS TO EVALUATE: 1. Identify the filename from the image (look for any text showing filenames or system paths/locations). 2. If no filename is visible in the image, state that "The filename could not be identified in the image." 3. If a filename is visible, check if it follows the required naming pattern with underscores separating each section. 4. Check for scaling information if applicable (e.g., AW_at_50pc) - note that percentages should be specified as "pc" not "%". 5. Verify that all required parts are present (JobNumber, Brand, DeliverableType, Location, Publication, Spec, NumberOfDeliverables). YOUR OUTPUT: • State whether the filename was visible in the image. • If visible, state whether the filename "passes" or "fails" the naming convention check. • If it fails, provide specific recommendations for correcting the filename. • Include a JSON code block with these fields: { "filename_visible": true or false, "filename_detected": "the detected filename or 'Not visible'", "naming_convention_check": "Pass" or "Fail" or "Not applicable", "recommendations": ["List of specific recommendations if applicable, else an empty array"] } SCORING INSTRUCTIONS: After your analysis, provide a numerical score from 1 to 10 (with 10 being perfect/excellent and 1 being poor/inadequate) based on how well the asset meets the criteria. YOUR OUTPUT FORMAT: Include a JSON code block with the following fields: - "score": a number from 1 to 10 - "explanation": your detailed reasoning for the score - "recommendations": specific suggestions for improvement if applicable Example: ```json { "score": 8, "explanation": "The logo is clearly visible and occupies approximately 12% of the advertisement area, which exceeds the minimum requirement. It has good contrast against the background and would be recognizable from the appropriate viewing distance.", "recommendations": "Consider increasing contrast around the edges of the logo for even better visibility in darker environments." } ``` ---------------------------------------- DEBUG: Using Gemini model: gemini-2.5-pro-preview-03-25 DEBUG: Added main asset image to parts list (dimensions: (693, 1024)) DEBUG: Total parts being sent to Gemini: 2 (1 text + 1 images) DEBUG: Main asset loaded successfully - dimensions: (693, 1024), mode: RGB DEBUG: Calling Gemini API for visual analysis ================================================================================ DEBUG: Gemini Vision API Call ================================================================================ DEBUG: Main asset image dimensions: (693, 1024) DEBUG: Main asset image mode: RGB DEBUG: No reference image provided DEBUG: Total images being sent to LLM: 1 (main asset only) DEBUG: Original prompt being sent to LLM: ---------------------------------------- Before assessment: Users often upload complex print production files with multiple panels, cut lines, and technical markings. Your task: Focus ONLY on the primary consumer-facing visual - what the end customer would actually see after production. Key instructions: • Ignore cut lines, registration marks, and production guides • If multiple panels exist, assess the main/largest consumer-facing design • Disregard technical text, color bars, or printer information • Focus on the final visual as it would appear on shelf/in-market • Treat folded/die-cut layouts as the assembled final product STEPS TO EVALUATE: 1. Examine the design components (logos, pack shots, icons, images, text blocks, promotion badges, etc.). Each distinct object or text grouping counts as one visual element that the viewer must actively notice. 2. Count the total number of these distinct visual elements. 3. If the count is more than 4, the design fails this checkpoint, as it may be too cluttered or complex. You are performing a visual quality-control check on a print design to verify if crop marks are present and correct. Your task is to determine whether the design has appropriate crop marks for printing. CROP MARKS CRITERIA: 1. Crop marks should be present in print-ready files 2. Crop marks should indicate the trim area of the document 3. Crop marks should be positioned outside the bleed area 4. Crop marks should be thin lines and not intrude into the design 5. Crop marks should appear at each corner of the document 6. For some specialized printing, registration marks, color bars, and other print marks may also be required STEPS TO EVALUATE: 1. Look for visible crop marks in the design (typically thin lines at the corners) 2. Check if the crop marks correctly indicate the trim area 3. Verify the crop marks are positioned outside the bleed area 4. Check if crop marks appear at all four corners 5. If no crop marks are visible in a print design, this is generally a fail 6. If the design is not intended for print, state that "Crop marks are not applicable for this design" YOUR OUTPUT: • State whether crop marks are visible in the design • If visible, state whether they appear correct and properly positioned • State whether the design "passes" or "fails" the crop marks check • Include a JSON code block with these fields: { "crop_marks_visible": true or false, "crop_marks_at_all_corners": true or false or "Not applicable", "crop_marks_outside_bleed": true or false or "Not applicable", "crop_marks_check": "Pass" or "Fail" or "Not applicable", "issues": ["List specific issues identified, if any, else an empty array"], "recommendations": ["List specific recommendations if applicable, else an empty array"] } SCORING INSTRUCTIONS: After your analysis, provide a numerical score from 1 to 10 (with 10 being perfect/excellent and 1 being poor/inadequate) based on how well the asset meets the criteria. YOUR OUTPUT FORMAT: Include a JSON code block with the following fields: - "score": a number from 1 to 10 - "explanation": your detailed reasoning for the score - "recommendations": specific suggestions for improvement if applicable Example: ```json { "score": 8, "explanation": "The logo is clearly visible and occupies approximately 12% of the advertisement area, which exceeds the minimum requirement. It has good contrast against the background and would be recognizable from the appropriate viewing distance.", "recommendations": "Consider increasing contrast around the edges of the logo for even better visibility in darker environments." } ``` DEBUG: Main asset loaded successfully - dimensions: (693, 1024), mode: RGB DEBUG: Calling Gemini API for visual analysis ================================================================================ DEBUG: Gemini Vision API Call ================================================================================ DEBUG: Main asset image dimensions: (693, 1024) DEBUG: Main asset image mode: RGB DEBUG: No reference image provided DEBUG: Total images being sent to LLM: 1 (main asset only) DEBUG: Original prompt being sent to LLM: ---------------------------------------- Before assessment: Users often upload complex print production files with multiple panels, cut lines, and technical markings. Your task: Focus ONLY on the primary consumer-facing visual - what the end customer would actually see after production. Key instructions: • Ignore cut lines, registration marks, and production guides • If multiple panels exist, assess the main/largest consumer-facing design • Disregard technical text, color bars, or printer information • Focus on the final visual as it would appear on shelf/in-market • Treat folded/die-cut layouts as the assembled final product STEPS TO EVALUATE: 1. Examine the design components (logos, pack shots, icons, images, text blocks, promotion badges, etc.). Each distinct object or text grouping counts as one visual element that the viewer must actively notice. 2. Count the total number of these distinct visual elements. 3. If the count is more than 4, the design fails this checkpoint, as it may be too cluttered or complex. You are performing an evaluation on a Point of Sale (POS) advertisement. Your task is to determine whether the advertisement layout follows these criteria: 1. The visual elements are positioned on the left side of the advertisement. 2. The text elements are positioned on the right side of the advertisement. STEPS TO EVALUATE: 1. Examine the overall layout of the advertisement. 2. Check if the visual elements are located primarily on the left side of the layout. 3. Check if the text elements are located primarily on the right side of the layout. 4. Consider any overlapping elements as part of the category that occupies the majority of their space. 5. If the visuals and text meet the criteria, mark the ad as "Pass." If not, mark as "Fail." YOUR OUTPUT: State whether the layout passes or fails the visual and text placement check. Finally, include a JSON code block with these fields: { "visual_on_left": true or false, "text_on_right": true or false, "layout_check": "Pass" or "Fail" } SCORING INSTRUCTIONS: After your analysis, provide a numerical score from 1 to 10 (with 10 being perfect/excellent and 1 being poor/inadequate) based on how well the asset meets the criteria. YOUR OUTPUT FORMAT: Include a JSON code block with the following fields: - "score": a number from 1 to 10 - "explanation": your detailed reasoning for the score - "recommendations": specific suggestions for improvement if applicable Example: ```json { "score": 8, "explanation": "The logo is clearly visible and occupies approximately 12% of the advertisement area, which exceeds the minimum requirement. It has good contrast against the background and would be recognizable from the appropriate viewing distance.", "recommendations": "Consider increasing contrast around the edges of the logo for even better visibility in darker environments." } ``` ---------------------------------------- DEBUG: Using Gemini model: gemini-2.5-pro-preview-03-25 DEBUG: Added main asset image to parts list (dimensions: (693, 1024)) DEBUG: Total parts being sent to Gemini: 2 (1 text + 1 images) DEBUG: Main asset loaded successfully - dimensions: (693, 1024), mode: RGB DEBUG: Calling Gemini API for visual analysis ================================================================================ DEBUG: Gemini Vision API Call ================================================================================ DEBUG: Main asset image dimensions: (693, 1024) DEBUG: Main asset image mode: RGB DEBUG: No reference image provided DEBUG: Total images being sent to LLM: 1 (main asset only) DEBUG: Original prompt being sent to LLM: ---------------------------------------- Before assessment: Users often upload complex print production files with multiple panels, cut lines, and technical markings. Your task: Focus ONLY on the primary consumer-facing visual - what the end customer would actually see after production. Key instructions: • Ignore cut lines, registration marks, and production guides • If multiple panels exist, assess the main/largest consumer-facing design • Disregard technical text, color bars, or printer information • Focus on the final visual as it would appear on shelf/in-market • Treat folded/die-cut layouts as the assembled final product STEPS TO EVALUATE: 1. Examine the design components (logos, pack shots, icons, images, text blocks, promotion badges, etc.). Each distinct object or text grouping counts as one visual element that the viewer must actively notice. 2. Count the total number of these distinct visual elements. 3. If the count is more than 4, the design fails this checkpoint, as it may be too cluttered or complex. You are performing a visual quality-control check on a Point of Sale (POS) advertisement. Your task is to determine whether the advertisement contains 7 words or fewer. Examine the advertisement carefully and follow these steps: STEPS TO EVALUATE: 1. Identify all textual elements in the advertisement, excluding any terms and conditions, the word "NEW," brand logos, and any text present on product packaging. 2. Count the total number of words in these textual elements. 3. Determine if the count is 7 words or fewer. YOUR OUTPUT: Based on the evaluation, confirm whether the advertisement passes the word-count checkpoint, stating "Pass" if it contains 7 words or fewer, or "Fail" if it contains more. Create a JSON code block with these fields: { "word_count": (total number of words counted), "checkpoint_result": "Pass" or "Fail" } SCORING INSTRUCTIONS: After your analysis, provide a numerical score from 1 to 10 (with 10 being perfect/excellent and 1 being poor/inadequate) based on how well the asset meets the criteria. YOUR OUTPUT FORMAT: Include a JSON code block with the following fields: - "score": a number from 1 to 10 - "explanation": your detailed reasoning for the score - "recommendations": specific suggestions for improvement if applicable Example: ```json { "score": 8, "explanation": "The logo is clearly visible and occupies approximately 12% of the advertisement area, which exceeds the minimum requirement. It has good contrast against the background and would be recognizable from the appropriate viewing distance.", "recommendations": "Consider increasing contrast around the edges of the logo for even better visibility in darker environments." } ``` ---------------------------------------- DEBUG: Using Gemini model: gemini-2.5-pro-preview-03-25 DEBUG: Added main asset image to parts list (dimensions: (693, 1024)) DEBUG: Total parts being sent to Gemini: 2 (1 text + 1 images) ---------------------------------------- DEBUG: Using Gemini model: gemini-2.5-pro-preview-03-25 DEBUG: Added main asset image to parts list (dimensions: (693, 1024)) DEBUG: Total parts being sent to Gemini: 2 (1 text + 1 images) DEBUG: Main asset loaded successfully - dimensions: (693, 1024), mode: RGB DEBUG: Calling Gemini API for visual analysis ================================================================================ DEBUG: Gemini Vision API Call ================================================================================ DEBUG: Main asset image dimensions: (693, 1024) DEBUG: Main asset image mode: RGB DEBUG: No reference image provided DEBUG: Total images being sent to LLM: 1 (main asset only) DEBUG: Original prompt being sent to LLM: ---------------------------------------- Before assessment: Users often upload complex print production files with multiple panels, cut lines, and technical markings. Your task: Focus ONLY on the primary consumer-facing visual - what the end customer would actually see after production. Key instructions: • Ignore cut lines, registration marks, and production guides • If multiple panels exist, assess the main/largest consumer-facing design • Disregard technical text, color bars, or printer information • Focus on the final visual as it would appear on shelf/in-market • Treat folded/die-cut layouts as the assembled final product STEPS TO EVALUATE: 1. Examine the design components (logos, pack shots, icons, images, text blocks, promotion badges, etc.). Each distinct object or text grouping counts as one visual element that the viewer must actively notice. 2. Count the total number of these distinct visual elements. 3. If the count is more than 4, the design fails this checkpoint, as it may be too cluttered or complex. You are performing a visual quality-control check on a print design to verify if bleed is correctly applied. Your task is to determine whether the design has proper bleed for printing. PRINT BLEED CRITERIA: 1. Standard bleed is typically 3mm (1/8 inch) on all sides 2. Background elements, images, or colors that touch the edge of the document should extend into the bleed area 3. Bleed should be consistently applied on all sides that require it 4. Important design elements should not be placed in or near the bleed area 5. Bleed marks or crop marks should be visible in print-ready files STEPS TO EVALUATE: 1. Look for visible bleed area or bleed marks in the design 2. Check if background elements extend into the bleed area 3. Verify the bleed size (typically 3mm/1/8 inch) 4. Check if important elements are safely away from the bleed area 5. If no bleed is visible but the design has elements that touch the edge, this is a fail 6. If no bleed is visible and no elements touch the edge, state that "Bleed assessment is not applicable for this design" YOUR OUTPUT: • State whether bleed area or marks are visible in the design • If visible, state the bleed size if it can be determined • State whether elements that touch the edge extend properly into the bleed area • State whether the design "passes" or "fails" the bleed check • Include a JSON code block with these fields: { "bleed_visible": true or false, "bleed_size": "3mm" or other specific measurement or "Unknown", "elements_extend_to_bleed": true or false or "Not applicable", "print_bleed_check": "Pass" or "Fail" or "Not applicable", "issues": ["List specific issues identified, if any, else an empty array"], "recommendations": ["List specific recommendations if applicable, else an empty array"] } SCORING INSTRUCTIONS: After your analysis, provide a numerical score from 1 to 10 (with 10 being perfect/excellent and 1 being poor/inadequate) based on how well the asset meets the criteria. YOUR OUTPUT FORMAT: Include a JSON code block with the following fields: - "score": a number from 1 to 10 - "explanation": your detailed reasoning for the score - "recommendations": specific suggestions for improvement if applicable Example: ```json { "score": 8, "explanation": "The logo is clearly visible and occupies approximately 12% of the advertisement area, which exceeds the minimum requirement. It has good contrast against the background and would be recognizable from the appropriate viewing distance.", "recommendations": "Consider increasing contrast around the edges of the logo for even better visibility in darker environments." } ``` DEBUG: Main asset loaded successfully - dimensions: (693, 1024), mode: RGB DEBUG: Calling Gemini API for visual analysis ================================================================================ DEBUG: Gemini Vision API Call ================================================================================ DEBUG: Main asset image dimensions: (693, 1024) DEBUG: Main asset image mode: RGB DEBUG: No reference image provided DEBUG: Total images being sent to LLM: 1 (main asset only) DEBUG: Original prompt being sent to LLM: ---------------------------------------- Before assessment: Users often upload complex print production files with multiple panels, cut lines, and technical markings. Your task: Focus ONLY on the primary consumer-facing visual - what the end customer would actually see after production. Key instructions: • Ignore cut lines, registration marks, and production guides • If multiple panels exist, assess the main/largest consumer-facing design • Disregard technical text, color bars, or printer information • Focus on the final visual as it would appear on shelf/in-market • Treat folded/die-cut layouts as the assembled final product STEPS TO EVALUATE: 1. Examine the design components (logos, pack shots, icons, images, text blocks, promotion badges, etc.). Each distinct object or text grouping counts as one visual element that the viewer must actively notice. 2. Count the total number of these distinct visual elements. 3. If the count is more than 4, the design fails this checkpoint, as it may be too cluttered or complex. You are performing a visual quality‐control check on a Point of Sale (POS) advertisement. Your task is to determine whether the text on the advertisement is readable from the appropriate viewing distance: • For large media (Floor‐Standing Display Units, posters, etc.): The text must be clearly readable from about 3 meters away. • For smaller media (wobblers, shelf talkers, etc.): The text must be clearly readable from about 1 meter away. STEPS TO EVALUATE: 1. Identify all text elements in the advertisement, excluding any terms and conditions. 2. Examine the text size, font style, and contrast with the background. 3. For each text element, assess whether it would be readable from the appropriate viewing distance. 4. Pay special attention to: a. Text size - Is it large enough to be read from a distance? b. Font choice - Is the font clear and easily readable? c. Contrast - Does the text stand out clearly against its background? d. Spacing - Is there sufficient spacing between letters, words, and lines? 5. Consider any potential obstacles to readability such as: a. Decorative fonts that sacrifice legibility for style b. Overlapping elements that obscure text c. Poor contrast that makes text blend into the background d. Text that is too small relative to viewing distance YOUR OUTPUT: • State whether the text elements "pass" or "fail" the readability checkpoint. • If they fail, provide 1–2 concise recommendations for improving text readability. • Include a JSON code block with these fields: { "text_readability": "Pass" or "Fail", "readability_score": "High", "Medium", or "Low", "recommendations": ["Recommendation 1", "Recommendation 2"] (only if "Fail") } SCORING INSTRUCTIONS: After your analysis, provide a numerical score from 1 to 10 (with 10 being perfect/excellent and 1 being poor/inadequate) based on how well the asset meets the criteria. YOUR OUTPUT FORMAT: Include a JSON code block with the following fields: - "score": a number from 1 to 10 - "explanation": your detailed reasoning for the score - "recommendations": specific suggestions for improvement if applicable Example: ```json { "score": 8, "explanation": "The logo is clearly visible and occupies approximately 12% of the advertisement area, which exceeds the minimum requirement. It has good contrast against the background and would be recognizable from the appropriate viewing distance.", "recommendations": "Consider increasing contrast around the edges of the logo for even better visibility in darker environments." } ``` ---------------------------------------- DEBUG: Using Gemini model: gemini-2.5-pro-preview-03-25 DEBUG: Added main asset image to parts list (dimensions: (693, 1024)) DEBUG: Total parts being sent to Gemini: 2 (1 text + 1 images) DEBUG: Main asset loaded successfully - dimensions: (693, 1024), mode: RGB DEBUG: Calling Gemini API for visual analysis ================================================================================ DEBUG: Gemini Vision API Call ================================================================================ DEBUG: Main asset image dimensions: (693, 1024) DEBUG: Main asset image mode: RGB DEBUG: No reference image provided DEBUG: Total images being sent to LLM: 1 (main asset only) DEBUG: Original prompt being sent to LLM: ---------------------------------------- Before assessment: Users often upload complex print production files with multiple panels, cut lines, and technical markings. Your task: Focus ONLY on the primary consumer-facing visual - what the end customer would actually see after production. Key instructions: • Ignore cut lines, registration marks, and production guides • If multiple panels exist, assess the main/largest consumer-facing design • Disregard technical text, color bars, or printer information • Focus on the final visual as it would appear on shelf/in-market • Treat folded/die-cut layouts as the assembled final product STEPS TO EVALUATE: 1. Examine the design components (logos, pack shots, icons, images, text blocks, promotion badges, etc.). Each distinct object or text grouping counts as one visual element that the viewer must actively notice. 2. Count the total number of these distinct visual elements. 3. If the count is more than 4, the design fails this checkpoint, as it may be too cluttered or complex. You are performing a visual quality-control check on digital content (website, email, or app) to verify its responsiveness across different screen sizes. Your task is to determine whether the design maintains visual and functional consistency between desktop and mobile versions. RESPONSIVENESS CRITERIA: 1. All content present in desktop should be accessible on mobile 2. Text should be legible on both desktop and mobile 3. Layout should adapt appropriately to screen size 4. Visual hierarchy should be maintained across screen sizes 5. Navigation should be usable on both desktop and mobile 6. No horizontal scrolling should be needed on mobile 7. Touch targets (buttons, links) should be appropriately sized on mobile 8. Brand consistency should be maintained across screen sizes STEPS TO EVALUATE: 1. Determine if both desktop and mobile versions are visible in the image 2. If both versions are visible, compare: a. Content completeness (all key content present on both) b. Text legibility c. Layout adaptation d. Visual hierarchy e. Navigation accessibility f. Touch target sizing g. Brand consistency 3. If only one version is visible, state that "Responsiveness could not be fully assessed from this image." YOUR OUTPUT: • State whether both desktop and mobile versions are visible in the image • If both are visible, evaluate each responsiveness criterion • State whether the design "passes" or "fails" the responsiveness check • Include a JSON code block with these fields: { "both_versions_visible": true or false, "content_completeness": "Pass" or "Fail" or "Not applicable", "text_legibility": "Pass" or "Fail" or "Not applicable", "layout_adaptation": "Pass" or "Fail" or "Not applicable", "visual_hierarchy_maintained": "Pass" or "Fail" or "Not applicable", "responsiveness_check": "Pass" or "Fail" or "Not applicable", "issues": ["List specific issues identified, if any, else an empty array"], "recommendations": ["List specific recommendations if applicable, else an empty array"] } SCORING INSTRUCTIONS: After your analysis, provide a numerical score from 1 to 10 (with 10 being perfect/excellent and 1 being poor/inadequate) based on how well the asset meets the criteria. YOUR OUTPUT FORMAT: Include a JSON code block with the following fields: - "score": a number from 1 to 10 - "explanation": your detailed reasoning for the score - "recommendations": specific suggestions for improvement if applicable Example: ```json { "score": 8, "explanation": "The logo is clearly visible and occupies approximately 12% of the advertisement area, which exceeds the minimum requirement. It has good contrast against the background and would be recognizable from the appropriate viewing distance.", "recommendations": "Consider increasing contrast around the edges of the logo for even better visibility in darker environments." } ```