3fec052c12
Organized the application into separate frontend and backend directories for cleaner deployment and better separation of concerns. Frontend Directory (frontend/): - index.html: Single-page web interface (renamed from web_ui.html) - README.md: Frontend deployment guide - Total size: ~113 KB (self-contained) - Smart base path detection (works at / or /ai_qc/) - No configuration changes required Backend Directory (backend/): - All Python files (api_server.py, llm_config.py, etc.) - visual_qc_apps/: 33 QC check modules - profiles/: 6 QC profile configurations - brand_guidelines/: Reference asset storage - config/: Environment configurations - scripts/: Deployment automation - uploads/, output/: Data directories - requirements.txt, ai_qc.service, apache_config.conf - Complete documentation New Documentation: - FOLDER_STRUCTURE.md: Comprehensive guide to new structure - frontend/README.md: Frontend deployment instructions - backend/BACKEND_README.md: Backend deployment guide Deployment Mapping: - frontend/ → /var/www/html/ai_qc/ (web root) - backend/ → /opt/ai_qc/ (application directory) Benefits: - Clear separation of concerns - Backend code not in web-accessible directory - Independent frontend/backend updates - Matches server's existing patterns (/opt/veo3, /opt/voice2text) - Industry-standard architecture - Easy to deploy and maintain Original files preserved in root directory for reference. Ready for production deployment following MIGRATION_GUIDE.md. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
54 lines
No EOL
2.3 KiB
Python
Executable file
54 lines
No EOL
2.3 KiB
Python
Executable file
import os
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import sys
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# Add parent directory to path to import shared modules
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sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
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from visual_qc_apps.flask_app_template import FlaskAppTemplate
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class ImageResolutionApp(FlaskAppTemplate):
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"""
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Image Resolution Check
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"""
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def __init__(self):
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# Define the hardcoded prompt
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prompt = """You are performing a visual quality-control check on a design to verify the resolution of images used. Your task is to determine whether the images in the design have appropriate resolution for the intended medium.
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RESOLUTION STANDARDS:
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1. For print designs: Images should be 300 DPI (dots per inch)
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2. For digital/display designs: Images should be 72 DPI (dots per inch)
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STEPS TO EVALUATE:
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1. First, determine if this is a print or digital design based on context clues or metadata.
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2. Look for any image resolution information in the screenshot, such as:
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a. Image properties or info panel showing resolution
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b. Document properties showing resolution
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c. Any visible DPI/PPI values
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3. If resolution information is visible, check if it meets the appropriate standard:
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- For print: 300 DPI or higher
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- For digital: 72-150 DPI is sufficient
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4. If no resolution information is visible, state that "Image resolution could not be verified from the screenshot."
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5. If low-resolution images are detected in a print design, this is a critical fail.
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YOUR OUTPUT:
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• State whether the design appears to be for print or digital (or if this can't be determined)
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• State whether resolution information was visible in the screenshot
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• If visible, state whether the image resolution "passes" or "fails" the check
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• If it fails, provide specific recommendations
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• Include a JSON code block with these fields:
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{
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"design_medium": "Print" or "Digital" or "Unknown",
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"resolution_info_visible": true or false,
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"detected_resolution": "300 DPI" or "72 DPI" or other specific value or "Unknown",
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"resolution_check": "Pass" or "Fail" or "Not applicable",
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"recommendations": ["List specific recommendations if applicable, else an empty array"]
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}"""
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# Initialize the Flask app with the prompt
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super().__init__(__name__, prompt)
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# Run the app if executed directly
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if __name__ == "__main__":
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app = ImageResolutionApp()
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app.run(debug=True) |