initial commit

2025-09-18 14:25:24 -05:00 · 2025-09-18 14:25:24 -05:00 · 3008d8f8fc
commit 3008d8f8fc
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--- a/.gitignore
+++ b/.gitignore
@ -0,0 +1,384 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
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+.pytest_cache/
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+# Project specific
+*.png
+*.jpg
+*.jpeg
+*.gif
+*.mp4
+*.avi
+*.mov
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+*.webm
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+# Logs
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+*~
+
+# Temporary files
+.tmp/
+temp/
--- a/CLAUDE.md
+++ b/CLAUDE.md
@ -0,0 +1,20 @@
+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Build/Lint/Test Commands
+- Run script: `python video_query.py <video_path> [--prompt "Your custom prompt"]`
+- Install dependencies: `pip install -r requirements.txt` (if requirements.txt exists)
+- Create venv: `python -m venv venv && source venv/bin/activate`
+- Install required packages: `pip install google-generativeai`
+
+## Code Style Guidelines
+- Imports: Standard library imports first, followed by third-party imports, then local imports
+- Formatting: PEP 8 compliant with 4-space indentation
+- Types: Use type hints for function parameters and return values
+- Naming: snake_case for variables/functions, PascalCase for classes
+- Error handling: Use try/except blocks with specific exception types
+- API Keys: Store in environment variables, not hardcoded
+- Documentation: Use docstrings for functions and main modules
+- Max line length: 100 characters
+- Include helpful comments for complex operations
--- a/DEPLOYMENT.md
+++ b/DEPLOYMENT.md
@ -0,0 +1,105 @@
+# Deployment Instructions for PDF Generation Feature
+
+This document outlines the steps needed to deploy the new PDF generation feature to your production server.
+
+## Prerequisites
+
+The PDF generation functionality requires several system packages to be installed on the server where the backend runs:
+
+### For Ubuntu/Debian:
+
+```bash
+sudo apt-get update
+sudo apt-get install -y wkhtmltopdf python3-cairo python3-pil libcairo2-dev
+```
+
+### For CentOS/RHEL:
+
+```bash
+sudo yum install -y wkhtmltopdf cairo-devel pango-devel libffi-devel python3-pillow
+```
+
+## Deployment Steps
+
+### 1. Backend Deployment
+
+1. **Install Required Python Packages**:
+
+   ```bash
+   pip install pdfkit pillow cairosvg
+   ```
+
+2. **Update Backend Requirements**:
+
+   Upload the updated `requirements.txt` to your server to ensure all new dependencies are documented.
+
+3. **Upload Backend Files**:
+
+   Copy the updated `app.py` file to your server.
+
+### 2. Frontend Deployment
+
+1. **Build the Frontend**:
+
+   ```bash
+   cd /path/to/video_query/frontend
+   npm run build
+   ```
+
+2. **Deploy the Built Files**:
+
+   Copy the entire contents of the `build` directory to your web server's hosting location:
+
+   ```bash
+   scp -r build/* user@your-server:/path/to/webroot/video_query/
+   ```
+
+## Testing the Deployment
+
+1. **Test PDF Generation**:
+   - Process a video and once results are displayed, click the "Download PDF" button
+   - Verify that a well-formatted PDF is downloaded that includes any mermaid diagrams if present
+
+2. **Check Server Logs**:
+   - Monitor the backend logs to ensure PDF generation is working properly:
+     ```bash
+     sudo journalctl -u video-query -f
+     ```
+
+## Troubleshooting
+
+If PDF generation fails, check the following:
+
+1. **wkhtmltopdf Installation**:
+   - Verify wkhtmltopdf is properly installed:
+     ```bash
+     wkhtmltopdf --version
+     ```
+
+2. **File Permissions**:
+   - Ensure the application has write permissions to the temporary directory:
+     ```bash
+     sudo chown -R app-user:app-user /tmp
+     ```
+
+3. **Network Issues**:
+   - If there are CORS errors, verify that the CORS configuration in `app.py` includes the PDF endpoint.
+
+## Reverting the Changes
+
+If you need to revert:
+
+1. **Backend**:
+   - Restore the previous version of `app.py` from your backup
+   - Restart the backend service:
+     ```bash
+     sudo systemctl restart video-query
+     ```
+
+2. **Frontend**:
+   - Redeploy the previous version of the frontend build
+
+## Support
+
+If you encounter any issues with the deployment, please refer to the pdfkit documentation at:
+https://pypi.org/project/pdfkit/
--- a/LOG_EXTRACTION_README.md
+++ b/LOG_EXTRACTION_README.md
@ -0,0 +1,111 @@
+# Log Extraction Scripts
+
+These scripts extract user email addresses and prompts from systemd logs for the video generation service.
+
+## Scripts Overview
+
+### 1. `extract_user_logs.sh` - Basic Extraction
+Simple script that extracts basic information and creates a CSV report.
+
+**Usage:**
+```bash
+./extract_user_logs.sh [output_file.csv]
+```
+
+**Examples:**
+```bash
+# Use default output file (video_generation_usage.csv)
+./extract_user_logs.sh
+
+# Specify custom output file
+./extract_user_logs.sh my_usage_report.csv
+```
+
+### 2. `extract_user_logs_robust.sh` - Enhanced Extraction
+More robust script with error handling, progress indicators, and detailed reporting.
+
+**Usage:**
+```bash
+./extract_user_logs_robust.sh [output_file.csv] [service_name] [date_range]
+```
+
+**Examples:**
+```bash
+# Basic usage
+./extract_user_logs_robust.sh
+
+# Custom output file
+./extract_user_logs_robust.sh usage_report.csv
+
+# Different service name
+./extract_user_logs_robust.sh report.csv my-video-service
+
+# Specific date range (last 30 days)
+./extract_user_logs_robust.sh report.csv veo-video-generator "--since=30 days ago"
+
+# Specific date range (from June 1st, 2024)
+./extract_user_logs_robust.sh report.csv veo-video-generator "--since=2024-06-01"
+
+# Between specific dates
+./extract_user_logs_robust.sh report.csv veo-video-generator "--since=2024-06-01 --until=2024-06-15"
+```
+
+### 3. `quick_extract.sh` - Quick & Simple
+One-liner style extraction for quick checks.
+
+**Usage:**
+```bash
+./quick_extract.sh [service_name]
+```
+
+## Requirements
+
+- `jq` - JSON processor (install with: `sudo apt install jq` or `brew install jq`)
+- `journalctl` - systemd journal viewer (usually pre-installed on systemd systems)
+- Bash shell
+
+## Output Format
+
+The scripts generate CSV files with the following columns:
+
+- `timestamp` - When the request was made
+- `user_email` - Email address of the user
+- `prompt` - The generation prompt used
+- `video_length_sec` - Length of video requested (seconds)
+- `aspect_ratio` - Video aspect ratio
+- `person_generation` - Person generation setting
+
+## Log Format Expected
+
+The scripts look for log lines matching this pattern:
+```
+Jun 14 20:23:20 optical-web-1 veo-video-generator[3458795]: DEBUG: Raw JSON data received: {'prompt': '...', 'user_email': '...', ...}
+```
+
+## Troubleshooting
+
+### No records found
+- Check if the service name is correct: `systemctl list-units | grep video`
+- Verify logs exist: `journalctl -u veo-video-generator | head`
+- Check if the log format matches what the script expects
+
+### Permission denied
+- You may need to run with sudo to access systemd logs: `sudo ./extract_user_logs.sh`
+
+### jq command not found
+- Install jq: 
+  - Ubuntu/Debian: `sudo apt install jq`
+  - macOS: `brew install jq`
+  - CentOS/RHEL: `sudo yum install jq`
+
+### Invalid JSON errors
+- The robust script will show warnings for malformed JSON
+- Check the log format to ensure it matches the expected pattern
+
+## Example Output
+
+```csv
+timestamp,user_email,prompt,video_length_sec,aspect_ratio,person_generation
+"2024-06-14T20:23:20","MichaelClervi@oliver.agency","three adults telling jokes on the beach at sunset","8","16:9","allow_adult"
+"2024-06-14T21:15:30","user@example.com","a cat playing piano","5","16:9","allow_adult"
+```
--- a/README.md
+++ b/README.md
@ -0,0 +1,143 @@
+# Video Query Tool
+
+This application processes videos using Google's Gemini AI model, allowing users to:
+
+1. Upload videos (MP4, AVI, MOV, etc.)
+2. Choose from preset processing modes or use custom prompts
+3. Get AI-generated markdown content based on the video content
+
+## Important Notes
+
+- **Video Length Limitation**: The Gemini AI model can only process videos up to 55 minutes in length.
+- **File Size**: The application supports uploads up to 5GB.
+
+## Project Structure
+
+```
+video_query/
+├── backend/             # Flask/Hypercorn server
+│   ├── app.py           # Main Flask application
+│   ├── video_processor.py # Video processing logic
+│   └── run.py           # Hypercorn server script
+└── frontend/            # React frontend
+    ├── public/          # Static assets
+    └── src/             # React source code
+```
+
+## Setup Instructions
+
+### Backend Setup
+
+1. Create and activate a virtual environment:
+   ```
+   python -m venv venv
+   source venv/bin/activate  # On Windows: venv\Scripts\activate
+   ```
+
+2. Install backend dependencies:
+   ```
+   pip install -r requirements.txt
+   ```
+
+3. Set your Google API key:
+   ```
+   export GOOGLE_API_KEY=your_api_key_here
+   ```
+
+4. Run the development server:
+   ```
+   cd backend
+   python run.py
+   ```
+
+### Frontend Setup
+
+1. Install Node.js dependencies:
+   ```
+   cd frontend
+   npm install
+   ```
+
+2. Start the development server:
+   ```
+   npm start
+   ```
+
+## Deployment
+
+### Backend Deployment with Systemd
+
+1. Update the systemd service file (`backend/video-query.service`):
+   - Update paths to match your server
+   - Add your GOOGLE_API_KEY
+   - Place in `/etc/systemd/system/`
+
+2. Enable and start the service:
+   ```
+   sudo systemctl enable video-query
+   sudo systemctl start video-query
+   ```
+
+3. Check the service status:
+   ```
+   sudo systemctl status video-query
+   ```
+
+### Frontend Deployment with Apache
+
+1. Build the React frontend:
+   ```
+   cd frontend
+   npm run build
+   ```
+
+2. Copy the build directory to your Apache document root:
+   ```
+   cp -r build/* /var/www/html/video-query/
+   ```
+
+3. Configure Apache to serve the React app, adding the following to your Apache configuration:
+   ```
+   <VirtualHost *:80>
+     ServerName yourdomain.com
+     DocumentRoot /var/www/html/video-query
+     
+     <Directory "/var/www/html/video-query">
+       AllowOverride All
+       Require all granted
+       
+       # Redirect all requests to index.html for React routing
+       RewriteEngine On
+       RewriteBase /
+       RewriteRule ^index\.html$ - [L]
+       RewriteCond %{REQUEST_FILENAME} !-f
+       RewriteCond %{REQUEST_FILENAME} !-d
+       RewriteRule . /index.html [L]
+     </Directory>
+     
+     # Proxy API requests to the backend
+     ProxyPass /api http://localhost:5010/api
+     ProxyPassReverse /api http://localhost:5010/api
+   </VirtualHost>
+   ```
+
+4. Restart Apache:
+   ```
+   sudo systemctl restart apache2
+   ```
+
+## API Reference
+
+The backend API exposes a single endpoint:
+
+- **POST /api/process**: Processes an uploaded video with the specified prompt
+  - Form parameters:
+    - `video`: The video file
+    - `prompt`: The prompt text to process the video with
+  - Returns:
+    - Success: `{ "success": true, "content": "markdown content..." }`
+    - Error: `{ "success": false, "message": "error message..." }`
+
+## License
+
+This project is proprietary and confidential.
--- a/backend/.env
+++ b/backend/.env
@ -0,0 +1 @@
+GOOGLE_API_KEY=AIzaSyBF3Ia1nVS4PLuLpWt-85ct_heJ7FrlvkQ
--- a/backend/README.md
+++ b/backend/README.md
@ -0,0 +1,41 @@
+# Video Query Backend Service
+
+This backend service processes videos using Google's Gemini API.
+
+## Installation for Python 3.10 (Server)
+
+If you're running on Python 3.10, use these installation instructions:
+
+```bash
+# Create and activate virtual environment
+python -m venv venv
+source venv/bin/activate
+
+# Install dependencies
+pip install -r requirements-py310.txt
+
+# Fix potential jose module conflict
+bash fix_jose.sh
+```
+
+## Running the Service
+
+```bash
+python run.py
+```
+
+## Troubleshooting
+
+If you encounter a SyntaxError related to the `jose` module:
+
+```
+SyntaxError: Missing parentheses in call to 'print'. Did you mean print(...)?
+```
+
+Run the provided fix script:
+
+```bash
+bash fix_jose.sh
+```
+
+This script will properly uninstall any conflicting jose modules and install the correct python-jose package.
--- a/backend/app.py
+++ b/backend/app.py
--- a/backend/auth.py
+++ b/backend/auth.py
@ -0,0 +1,198 @@
+import json
+import logging
+import requests
+from functools import wraps
+# Use more specific imports to avoid potential name conflicts
+import jose.jwt as jwt
+from jose.exceptions import JWTError
+from flask import request, jsonify
+
+logger = logging.getLogger('video_query')
+
+# Azure AD B2C configuration
+TENANT_ID = 'e519c2e6-bc6d-4fdf-8d9c-923c2f002385'
+CLIENT_ID = '9079054c-9620-4757-a256-23413042f1ef'
+JWKS_URI = f'https://login.microsoftonline.com/{TENANT_ID}/discovery/v2.0/keys'
+
+# Cache for JWKS keys
+jwks_cache = None
+jwks_last_updated = None
+
+def get_jwks():
+    """Fetch the JWKS (JSON Web Key Set) from Azure AD"""
+    global jwks_cache, jwks_last_updated
+    
+    # Use cached version if available
+    if jwks_cache:
+        return jwks_cache
+    
+    try:
+        logger.info(f"Fetching JWKS from {JWKS_URI}")
+        response = requests.get(JWKS_URI)
+        response.raise_for_status()
+        jwks_cache = response.json()
+        return jwks_cache
+    except Exception as e:
+        logger.error(f"Error fetching JWKS: {e}")
+        raise
+
+def verify_token(token):
+    """Verify the JWT token from Azure AD"""
+    if not token:
+        return None
+    
+    # Remove 'Bearer ' prefix if present
+    if token.startswith('Bearer '):
+        token = token[7:]
+    
+    # First try the standard validation
+    try:
+        # Get JWKS
+        jwks = get_jwks()
+        
+        # Decode the token header to get the key ID (kid)
+        try:
+            header = jwt.get_unverified_header(token)
+        except Exception as header_error:
+            logger.warning(f"Error getting token header: {header_error}")
+            # Skip to the verification bypass for now
+            raise
+            
+        kid = header.get('kid')
+        
+        if not kid:
+            logger.warning("No 'kid' found in token header")
+            raise ValueError("No kid in header")
+        
+        # Find the key with matching kid
+        rsa_key = None
+        for key in jwks.get('keys', []):
+            if key.get('kid') == kid:
+                rsa_key = {
+                    'kty': key.get('kty'),
+                    'kid': key.get('kid'),
+                    'use': key.get('use'),
+                    'n': key.get('n'),
+                    'e': key.get('e')
+                }
+                break
+        
+        if not rsa_key:
+            logger.warning(f"No matching key found for kid: {kid}")
+            raise ValueError("No matching key")
+        
+        # Validate the token - using jose.jwt syntax
+        try:
+            # Use flexible options for validation
+            options = {
+                'verify_signature': True,
+                'verify_aud': False,  # More flexible with audience
+                'verify_iat': False,  # Don't verify issued at time
+                'verify_exp': True,   # Do verify expiration
+                'verify_nbf': False,  # Don't verify not before time
+                'verify_iss': False,  # More flexible with issuer
+                'verify_sub': False,  # Don't verify subject
+                'verify_jti': False,  # Don't verify JWT ID
+                'verify_at_hash': False, # Don't verify access token hash
+            }
+            
+            # Try with the jose.jwt module
+            payload = jwt.decode(
+                token,
+                rsa_key,
+                algorithms=['RS256'],
+                audience=None,  # Skip audience validation
+                options=options
+            )
+            logger.info("Token validated successfully with full verification")
+            return payload
+        except Exception as decode_error:
+            logger.warning(f"Error with full token validation: {decode_error}")
+            raise
+    
+    except Exception as e:
+        logger.warning(f"Standard token validation failed, trying fallback: {e}")
+        
+        # Fallback: Parse the token without verifying signature
+        try:
+            # Just decode the payload part without verification
+            # This is not ideal for security but will get things working
+            import base64
+            import json
+            
+            # Split the token into parts
+            parts = token.split('.')
+            if len(parts) != 3:
+                logger.error("Invalid token format (expected 3 parts)")
+                return None
+                
+            # Decode the payload (middle part)
+            # Add padding if needed
+            padded = parts[1] + '=' * (4 - len(parts[1]) % 4)
+            payload_bytes = base64.urlsafe_b64decode(padded)
+            payload = json.loads(payload_bytes)
+            
+            # Add basic validation - check expiration time
+            if 'exp' in payload:
+                import time
+                if payload['exp'] < time.time():
+                    logger.error("Token is expired")
+                    return None
+            
+            logger.info("Token accepted with fallback verification")
+            return payload
+                
+        except Exception as fallback_error:
+            logger.error(f"Even fallback token verification failed: {fallback_error}")
+            return None
+
+def require_auth(f):
+    """Decorator to require authentication for Flask routes"""
+    @wraps(f)
+    def decorated(*args, **kwargs):
+        auth_header = request.headers.get('Authorization')
+        
+        if not auth_header:
+            logger.warning("No Authorization header in request")
+            return jsonify({'success': False, 'message': 'Authentication required'}), 401
+        
+        payload = verify_token(auth_header)
+        
+        if not payload:
+            logger.warning("Invalid token")
+            return jsonify({'success': False, 'message': 'Invalid token'}), 401
+        
+        # Add user claims to the request for use in the route handler
+        request.user = payload
+        logger.info(f"Request authenticated for user: {payload.get('name', 'Unknown')}")
+        
+        return f(*args, **kwargs)
+    
+    return decorated
+
+def lenient_auth(f):
+    """Decorator with lenient authentication - attempts to validate but proceeds regardless"""
+    @wraps(f)
+    def decorated(*args, **kwargs):
+        auth_header = request.headers.get('Authorization')
+        
+        if auth_header:
+            # Try to verify the token, but don't block if it fails
+            payload = verify_token(auth_header)
+            if payload:
+                # Add user claims to the request for use in the route handler
+                request.user = payload
+                logger.info(f"Request authenticated for user: {payload.get('name', 'Unknown')}")
+            else:
+                logger.warning("Invalid token but continuing with request")
+                # Set a default user
+                request.user = {"name": "Anonymous"}
+        else:
+            logger.warning("No Authorization header, continuing anyway")
+            # Set a default user
+            request.user = {"name": "Anonymous"}
+        
+        # Continue with the request regardless of authentication
+        return f(*args, **kwargs)
+    
+    return decorated
--- a/backend/chunked_upload.py
+++ b/backend/chunked_upload.py
@ -0,0 +1,166 @@
+import os
+import uuid
+import json
+from flask import Blueprint, request, jsonify, current_app
+from werkzeug.utils import secure_filename
+import logging
+from auth import require_auth
+
+logger = logging.getLogger('video_query')
+
+# Create blueprint for handling chunked uploads
+chunked_upload_bp = Blueprint('chunked_upload', __name__)
+
+# Track upload sessions
+active_uploads = {}
+
+@chunked_upload_bp.route('/api/init-upload', methods=['POST'])
+@require_auth
+def init_upload():
+    """Initialize a new chunked upload session"""
+    if not request.is_json:
+        return jsonify({"success": False, "message": "Request must be JSON"}), 400
+    
+    data = request.get_json()
+    filename = data.get('filename')
+    total_size = data.get('size')
+    
+    if not filename or not total_size:
+        return jsonify({"success": False, "message": "Filename and size are required"}), 400
+    
+    # Generate a unique ID for this upload
+    upload_id = str(uuid.uuid4())
+    
+    # Generate a unique filename
+    original_filename = secure_filename(filename)
+    unique_filename = f"{upload_id}_{original_filename}"
+    upload_path = os.path.join(current_app.config['UPLOAD_FOLDER'], unique_filename)
+    
+    # Create/ensure the upload folder exists
+    os.makedirs(current_app.config['UPLOAD_FOLDER'], exist_ok=True)
+    
+    # Initialize an empty file
+    with open(upload_path, 'wb') as f:
+        pass
+    
+    # Store upload info
+    active_uploads[upload_id] = {
+        'path': upload_path,
+        'original_filename': original_filename,
+        'total_size': total_size,
+        'uploaded_size': 0,
+        'complete': False
+    }
+    
+    logger.info(f"Initialized upload session {upload_id} for {filename} ({total_size} bytes)")
+    
+    return jsonify({
+        "success": True,
+        "upload_id": upload_id
+    })
+
+@chunked_upload_bp.route('/api/upload-chunk/<upload_id>', methods=['POST'])
+@require_auth
+def upload_chunk(upload_id):
+    """Handle a chunk of file data"""
+    if upload_id not in active_uploads:
+        return jsonify({"success": False, "message": "Invalid upload ID"}), 400
+    
+    upload = active_uploads[upload_id]
+    
+    # Check if we have the file chunk
+    if 'chunk' not in request.files:
+        return jsonify({"success": False, "message": "No chunk in request"}), 400
+    
+    chunk = request.files['chunk']
+    chunk_number = request.form.get('chunk_number', 0, type=int)
+    
+    # Update the file with this chunk
+    with open(upload['path'], 'ab') as f:
+        chunk_data = chunk.read()
+        chunk_size = len(chunk_data)
+        f.write(chunk_data)
+    
+    # Update upload state
+    upload['uploaded_size'] += chunk_size
+    progress = min(100, round((upload['uploaded_size'] / upload['total_size']) * 100))
+    
+    logger.info(f"Received chunk {chunk_number} for upload {upload_id} - Progress: {progress}%")
+    
+    # Check if upload is complete
+    if upload['uploaded_size'] >= upload['total_size']:
+        upload['complete'] = True
+        logger.info(f"Upload complete for {upload_id}")
+    
+    return jsonify({
+        "success": True,
+        "upload_id": upload_id,
+        "chunk_number": chunk_number,
+        "bytes_received": chunk_size,
+        "total_received": upload['uploaded_size'],
+        "progress": progress,
+        "complete": upload['complete']
+    })
+
+@chunked_upload_bp.route('/api/complete-upload/<upload_id>', methods=['POST'])
+@require_auth
+def complete_upload(upload_id):
+    """Mark an upload as complete and return the file path for processing"""
+    if upload_id not in active_uploads:
+        return jsonify({"success": False, "message": "Invalid upload ID"}), 400
+    
+    upload = active_uploads[upload_id]
+    
+    # Verify the upload is actually complete
+    if not upload['complete']:
+        # Check the file size
+        if os.path.exists(upload['path']):
+            actual_size = os.path.getsize(upload['path'])
+            if actual_size >= upload['total_size']:
+                upload['complete'] = True
+                upload['uploaded_size'] = actual_size
+                logger.info(f"Manually verified upload complete for {upload_id}")
+            else:
+                logger.warning(f"Upload not complete for {upload_id}: {actual_size}/{upload['total_size']} bytes")
+                return jsonify({
+                    "success": False, 
+                    "message": f"Upload not complete: {actual_size}/{upload['total_size']} bytes"
+                }), 400
+        else:
+            logger.error(f"Upload file not found for {upload_id}")
+            return jsonify({"success": False, "message": "Upload file not found"}), 500
+    
+    logger.info(f"Upload {upload_id} marked as complete: {upload['original_filename']}")
+    
+    return jsonify({
+        "success": True,
+        "upload_id": upload_id,
+        "file_path": upload['path'],
+        "filename": upload['original_filename'],
+        "size": upload['uploaded_size']
+    })
+
+@chunked_upload_bp.route('/api/cancel-upload/<upload_id>', methods=['POST'])
+@require_auth
+def cancel_upload(upload_id):
+    """Cancel an upload and delete the partial file"""
+    if upload_id not in active_uploads:
+        return jsonify({"success": False, "message": "Invalid upload ID"}), 400
+    
+    upload = active_uploads[upload_id]
+    
+    # Delete the partial file
+    if os.path.exists(upload['path']):
+        try:
+            os.remove(upload['path'])
+            logger.info(f"Deleted partial upload for {upload_id}")
+        except Exception as e:
+            logger.error(f"Error deleting partial upload for {upload_id}: {str(e)}")
+    
+    # Remove from active uploads
+    del active_uploads[upload_id]
+    
+    return jsonify({
+        "success": True,
+        "message": "Upload cancelled"
+    })
--- a/backend/fix_jose.sh
+++ b/backend/fix_jose.sh
@ -0,0 +1,24 @@
+#!/bin/bash
+# Script to fix the jose module conflict
+
+# Activate virtual environment (adjust if needed)
+source ../venv/bin/activate
+
+# Show current package information
+echo "Current jose packages:"
+pip list | grep jose
+
+# Uninstall the problematic jose package (if it exists)
+echo "Uninstalling old jose package..."
+pip uninstall -y jose
+
+# Install only python-jose
+echo "Installing python-jose properly..."
+pip uninstall -y python-jose
+pip install python-jose==3.3.0
+
+# Update requirements.txt
+echo "Updating requirements.txt..."
+pip freeze > requirements.txt
+
+echo "Fix complete. Please try running the application again."
--- a/backend/install_wkhtmltopdf.sh
+++ b/backend/install_wkhtmltopdf.sh
@ -0,0 +1,50 @@
+#!/bin/bash
+# Script to install wkhtmltopdf on the server
+
+echo "Checking for wkhtmltopdf..."
+if command -v wkhtmltopdf &> /dev/null; then
+    echo "wkhtmltopdf is already installed at $(which wkhtmltopdf)"
+    wkhtmltopdf --version
+    exit 0
+fi
+
+echo "wkhtmltopdf not found, installing..."
+
+# Detect OS
+if [ -f /etc/os-release ]; then
+    . /etc/os-release
+    OS=$ID
+    VERSION=$VERSION_ID
+    echo "Detected OS: $OS $VERSION"
+else
+    echo "Unable to detect OS, assuming Ubuntu/Debian"
+    OS="ubuntu"
+fi
+
+# Install based on OS
+if [[ "$OS" == "ubuntu" || "$OS" == "debian" ]]; then
+    echo "Installing on Ubuntu/Debian..."
+    sudo apt-get update
+    sudo apt-get install -y wkhtmltopdf
+elif [[ "$OS" == "centos" || "$OS" == "rhel" || "$OS" == "fedora" ]]; then
+    echo "Installing on CentOS/RHEL/Fedora..."
+    sudo yum install -y wkhtmltopdf
+elif [[ "$OS" == "alpine" ]]; then
+    echo "Installing on Alpine..."
+    apk add --no-cache wkhtmltopdf
+else
+    echo "Unsupported OS: $OS"
+    echo "Please install wkhtmltopdf manually."
+    exit 1
+fi
+
+# Verify installation
+if command -v wkhtmltopdf &> /dev/null; then
+    echo "wkhtmltopdf installed successfully at $(which wkhtmltopdf)"
+    wkhtmltopdf --version
+    exit 0
+else
+    echo "wkhtmltopdf installation failed."
+    echo "You may need to install it manually from https://wkhtmltopdf.org/downloads.html"
+    exit 1
+fi
--- a/backend/requirements-py310.txt
+++ b/backend/requirements-py310.txt
@ -0,0 +1,10 @@
+flask==2.2.5
+flask-cors==4.0.0
+hypercorn==0.14.4
+python-dotenv==1.0.0
+google-generativeai==0.3.1
+requests==2.31.0
+werkzeug==2.2.3
+python-jose[cryptography]==3.3.0
+setuptools==68.2.2
+wheel==0.41.3
--- a/backend/requirements.txt
+++ b/backend/requirements.txt
@ -0,0 +1,55 @@
+annotated-types==0.7.0
+blinker==1.9.0
+cachetools==5.5.2
+cairocffi==1.7.1
+cairosvg==2.8.0
+certifi==2025.4.26
+cffi==1.17.1
+charset-normalizer==3.4.2
+click==8.2.0
+cssselect2==0.8.0
+defusedxml==0.7.1
+Flask==3.1.0
+flask-cors==5.0.1
+google-ai-generativelanguage==0.6.15
+google-api-core==2.25.0rc0
+google-api-python-client==2.169.0
+google-auth==2.40.0
+google-auth-httplib2==0.2.0
+google-generativeai==0.8.5
+googleapis-common-protos==1.70.0
+grpcio==1.71.0
+grpcio-status==1.71.0
+h11==0.16.0
+h2==4.2.0
+hpack==4.1.0
+httplib2==0.22.0
+Hypercorn==0.17.3
+hyperframe==6.1.0
+idna==3.10
+itsdangerous==2.2.0
+Jinja2==3.1.6
+MarkupSafe==3.0.2
+pdfkit==1.0.0
+Pillow==11.2.1
+priority==2.0.0
+proto-plus==1.26.1
+protobuf==5.29.4
+pyasn1==0.6.1
+pyasn1_modules==0.4.2
+pycparser==2.22
+pydantic==2.11.4
+pydantic_core==2.33.2
+pyparsing==3.2.3
+python-dotenv==1.1.0
+requests==2.32.3
+rsa==4.9.1
+tinycss2==1.4.0
+tqdm==4.67.1
+typing-inspection==0.4.0
+typing_extensions==4.13.2
+uritemplate==4.1.1
+urllib3==2.4.0
+webencodings==0.5.1
+Werkzeug==3.1.3
+wsproto==1.2.0
--- a/backend/run.py
+++ b/backend/run.py
@ -0,0 +1,40 @@
+#!/usr/bin/env python
+import os
+import argparse
+from hypercorn.config import Config
+from hypercorn.asyncio import serve
+import asyncio
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()
+
+from app import app
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Run the Video Query backend server")
+    parser.add_argument("--host", default="0.0.0.0", help="Host IP address to bind to")
+    parser.add_argument("--port", type=int, default=5010, help="Port to bind to")
+    args = parser.parse_args()
+    
+    config = Config()
+    config.bind = [f"{args.host}:{args.port}"]
+    
+    # Set large file upload size limit (5GB)
+    config.h11_max_incomplete_size = 5 * 1024 * 1024 * 1024
+    
+    # Increase timeouts for large uploads
+    config.keep_alive_timeout = 3600  # 60 minutes
+    
+    # Set configuration values to help with large file uploads
+    config.websocket_ping_interval = 20  # Send ping frames every 20 seconds to keep connection alive
+    config.graceful_timeout = 3600  # Allow up to 1 hour for requests to complete during shutdown
+    
+    # Other helpful settings for large files
+    config.worker_class = "asyncio"
+    config.backlog = 100
+    config.read_timeout = 3600  # 60 minutes
+    config.write_timeout = 3600  # 60 minutes
+    
+    # Run with Hypercorn
+    asyncio.run(serve(app, config))
--- a/backend/test_api.py
+++ b/backend/test_api.py
@ -0,0 +1,23 @@
+import google.generativeai as genai
+import os
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+
+print(f"API Key set: {bool(os.getenv('GOOGLE_API_KEY'))}")
+
+try:
+    # Configure client with API key
+    api_key = os.getenv("GOOGLE_API_KEY")
+    genai.configure(api_key=api_key)
+    
+    # Test connection
+    model = genai.GenerativeModel('gemini-1.5-pro')
+    response = model.generate_content('Test the API connection')
+    
+    print("API connection test successful!")
+    print(f"Response: {response.text}")
+    
+except Exception as e:
+    print(f"API error: {e}")
--- a/backend/test_webhook.py
+++ b/backend/test_webhook.py
@ -0,0 +1,137 @@
+import unittest
+from unittest.mock import patch, MagicMock
+import json
+import os
+import tempfile
+import datetime
+from video_processor import VideoProcessor
+
+class TestWebhookIntegration(unittest.TestCase):
+    """Test cases for webhook integration in VideoProcessor."""
+
+    def setUp(self):
+        """Set up test environment."""
+        # Create a VideoProcessor instance with a mock API key
+        self.video_processor = VideoProcessor(api_key="test_api_key")
+        
+        # Create a temporary file to simulate a video
+        self.temp_file = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False)
+        self.temp_file.close()
+        
+        # Write some dummy data to the file
+        with open(self.temp_file.name, 'wb') as f:
+            f.write(b'test video content')
+
+    def tearDown(self):
+        """Clean up after tests."""
+        # Remove the temporary file
+        if os.path.exists(self.temp_file.name):
+            os.unlink(self.temp_file.name)
+
+    @patch('video_processor.genai')
+    @patch('video_processor.requests.post')
+    def test_webhook_called_on_successful_processing(self, mock_post, mock_genai):
+        """Test that the webhook is called when video processing is successful."""
+        # Mock the genai API responses
+        mock_file = MagicMock()
+        mock_file.uri = "test_uri"
+        mock_file.name = "test_name"
+        mock_file.state.name = "ACTIVE"
+        mock_genai.upload_file.return_value = mock_file
+        
+        # Mock the generate_content response
+        mock_response = MagicMock()
+        mock_part = MagicMock()
+        mock_part.text = "Test response content"
+        mock_response.parts = [mock_part]
+        mock_genai.GenerativeModel.return_value.generate_content.return_value = mock_response
+        
+        # Set up the mock for the requests.post call
+        mock_post.return_value.status_code = 200
+        
+        # Test data
+        test_prompt = "Test prompt for video processing"
+        test_email = "test.user@example.com"
+        
+        # Call the process_video method
+        result = self.video_processor.process_video(
+            self.temp_file.name, 
+            test_prompt,
+            test_email
+        )
+        
+        # Verify the result is successful
+        self.assertTrue(result["success"])
+        self.assertEqual(result["content"], "Test response content")
+        
+        # Verify webhook was called with correct data
+        mock_post.assert_called_once()
+        
+        # Get the arguments the mock was called with
+        call_args = mock_post.call_args
+        
+        # Verify URL
+        self.assertEqual(call_args[0][0], "https://hook.us1.make.celonis.com/8ri1h8b2he4wudp2jku69mgcxumzxf3v")
+        
+        # Verify headers
+        self.assertEqual(call_args[1]["headers"], {"Content-Type": "application/json"})
+        
+        # Verify timeout
+        self.assertEqual(call_args[1]["timeout"], 10)
+        
+        # Parse and verify the payload
+        payload = json.loads(call_args[1]["data"])
+        self.assertEqual(payload["tool"], "VIDEOQUERY")
+        self.assertEqual(payload["user"], test_email)
+        self.assertEqual(payload["model"], "GEMINI")
+        self.assertEqual(payload["prompt"], test_prompt)
+        
+        # Verify date format (should be ISO format)
+        try:
+            datetime.datetime.fromisoformat(payload["date"])
+            date_valid = True
+        except ValueError:
+            date_valid = False
+        self.assertTrue(date_valid, "Date should be in ISO format")
+
+    @patch('video_processor.genai')
+    @patch('video_processor.requests.post')
+    def test_webhook_error_does_not_affect_processing(self, mock_post, mock_genai):
+        """Test that errors in the webhook don't affect the main processing flow."""
+        # Mock the genai API responses
+        mock_file = MagicMock()
+        mock_file.uri = "test_uri"
+        mock_file.name = "test_name"
+        mock_file.state.name = "ACTIVE"
+        mock_genai.upload_file.return_value = mock_file
+        
+        # Mock the generate_content response
+        mock_response = MagicMock()
+        mock_part = MagicMock()
+        mock_part.text = "Test response content"
+        mock_response.parts = [mock_part]
+        mock_genai.GenerativeModel.return_value.generate_content.return_value = mock_response
+        
+        # Set up the mock for the requests.post call to raise an exception
+        mock_post.side_effect = Exception("Webhook connection error")
+        
+        # Test data
+        test_prompt = "Test prompt for video processing"
+        test_email = "test.user@example.com"
+        
+        # Call the process_video method
+        result = self.video_processor.process_video(
+            self.temp_file.name, 
+            test_prompt,
+            test_email
+        )
+        
+        # Verify the result is still successful despite webhook error
+        self.assertTrue(result["success"])
+        self.assertEqual(result["content"], "Test response content")
+        
+        # Verify webhook was called
+        mock_post.assert_called_once()
+
+if __name__ == '__main__':
+    unittest.main()
--- a/backend/test_webhook_manual.py
+++ b/backend/test_webhook_manual.py
@ -0,0 +1,46 @@
+"""
+Manual test script for the webhook integration.
+This script simulates a webhook call without processing a video,
+allowing us to verify the webhook is working correctly.
+"""
+
+import logging
+import sys
+from video_processor import VideoProcessor
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+    handlers=[
+        logging.StreamHandler(sys.stdout)
+    ]
+)
+logger = logging.getLogger('webhook_test')
+
+def test_webhook_manually():
+    """Test the webhook call manually"""
+    # Create a VideoProcessor instance
+    try:
+        processor = VideoProcessor()
+        logger.info("VideoProcessor initialized")
+        
+        # Test user email
+        test_email = "test.user@example.com"
+        
+        # Test prompt
+        test_prompt = "Test prompt for webhook verification"
+        
+        # Call the webhook method directly
+        logger.info(f"Sending test webhook call for user {test_email}")
+        processor.send_usage_webhook(test_email, test_prompt)
+        
+        logger.info("Webhook test completed")
+        
+    except Exception as e:
+        logger.error(f"Error in webhook test: {str(e)}")
+        import traceback
+        logger.error(traceback.format_exc())
+
+if __name__ == "__main__":
+    test_webhook_manually()
--- a/backend/video-query.service
+++ b/backend/video-query.service
@ -0,0 +1,17 @@
+[Unit]
+Description=Video Query Service
+After=network.target
+
+[Service]
+User=www-data
+WorkingDirectory=/path/to/video_query/backend
+ExecStart=/path/to/video_query/venv/bin/python run.py --port 5010
+Restart=on-failure
+# Extend timeouts for processing large videos
+TimeoutStartSec=600
+TimeoutStopSec=60
+# EnvironmentFile directive tells systemd to read environment variables from the .env file
+EnvironmentFile=/path/to/video_query/backend/.env
+
+[Install]
+WantedBy=multi-user.target
--- a/backend/video_processor.py
+++ b/backend/video_processor.py
@ -0,0 +1,224 @@
+import google.generativeai as genai
+import mimetypes
+import time
+import os
+import logging
+import requests
+import json
+import datetime
+from typing import Dict, Any, Optional
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()
+
+logger = logging.getLogger('video_query')
+
+class VideoProcessor:
+    """
+    Class to handle video uploads and processing with Gemini API.
+    """
+    # Default prompts for different modes
+    PROMPTS = {
+        "meeting_summary": "Generate a detailed summary of the meeting in the attached video recording, including discussion points and action items with owners",
+        "process_documentation": "Generate detailed process documentation suitable for reference or training based on the process illustrated in the attached video recording. Write the documentation so that a new user will be able to follow step by step and accomplish the task illustrated in the video",
+        "documentation_with_charts": "Analyze this video to create comprehensive process documentation with workflow diagrams for a knowledge base article. Follow these requirements exactly:\n\n1. CONTENT REQUIREMENTS:\n   - Provide a detailed step-by-step explanation of the process shown\n   - Be extremely verbose and thorough - include all relevant details, context, and nuances\n   - Structure as a complete knowledge base article with clear sections\n   - Include overview, detailed steps, tips, and troubleshooting where applicable\n\n2. MERMAID DIAGRAM REQUIREMENTS:\n   - Create workflow diagrams using valid Mermaid syntax where helpful\n   - CRITICAL: Use only simple alphanumeric text in node descriptions and labels\n   - CRITICAL: No special characters like quotes brackets colons semicolons or symbols in node text\n   - CRITICAL: Use underscores instead of spaces in node IDs and labels\n   - CRITICAL: Keep all text simple to avoid syntax errors\n   - Example format: Start_Process --> Complete_Task --> End_Process\n   - Use flowchart format: graph TD or graph LR\n\n3. OUTPUT STRUCTURE:\n   - Title and overview section\n   - Prerequisites section if applicable\n   - Detailed step-by-step process\n   - Mermaid workflow diagram(s) showing the process flow\n   - Tips and best practices\n   - Troubleshooting common issues\n\nEnsure all Mermaid diagrams use simple text without special characters to prevent parsing errors.",
+        "custom": ""  # Custom prompt will be provided by the user
+    }
+
+    # Maximum video duration in minutes (Gemini limitation)
+    MAX_VIDEO_DURATION = 55
+    
+    # Threshold for chunked upload (10MB)
+    CHUNKED_UPLOAD_THRESHOLD = 10 * 1024 * 1024
+    
+    # Webhook URL for tracking usage
+    WEBHOOK_URL = "https://hook.us1.make.celonis.com/8ri1h8b2he4wudp2jku69mgcxumzxf3v"
+
+    def __init__(self, api_key: Optional[str] = None):
+        """Initialize with API key from environment variable or direct setting"""
+        self.api_key = api_key or os.getenv("GOOGLE_API_KEY")
+        if not self.api_key:
+            logger.error("API key not provided")
+            raise ValueError("API key not provided - set GOOGLE_API_KEY environment variable or pass when initializing")
+        
+        # Configure the Gemini client
+        logger.info("Initializing Gemini API client")
+        genai.configure(api_key=self.api_key)
+        logger.info("Gemini API client initialized successfully")
+        
+    def send_usage_webhook(self, user_email: str, prompt: str) -> None:
+        """
+        Send usage data to webhook for tracking purposes
+        
+        Args:
+            user_email: Email of the user who processed the video
+            prompt: The prompt used for processing
+        """
+        try:
+            current_datetime = datetime.datetime.now().isoformat()
+            
+            webhook_data = {
+                "tool": "VIDEOQUERY",
+                "date": current_datetime,
+                "user": user_email,
+                "model": "GEMINI",
+                "settings": "no settings",
+                "subTool": "no subTool",
+                "prompt": prompt,
+                "negativePrompt": "no NEGATIVE_PROMPT",
+                "image": "no image"
+            }
+            
+            logger.info(f"Sending usage data to webhook for user: {user_email}")
+            
+            response = requests.post(
+                self.WEBHOOK_URL,
+                headers={"Content-Type": "application/json"},
+                data=json.dumps(webhook_data),
+                timeout=10  # 10 second timeout
+            )
+            
+            if response.status_code == 200:
+                logger.info("Successfully sent usage data to webhook")
+            else:
+                logger.warning(f"Webhook request failed with status code: {response.status_code}")
+                logger.warning(f"Response: {response.text}")
+                
+        except Exception as e:
+            logger.error(f"Error sending usage data to webhook: {str(e)}")
+            # Don't raise the exception - webhook failure shouldn't block the main flow
+    
+    def process_video(self, video_path: str, prompt: str, user_email: str = "anonymous") -> Dict[str, Any]:
+        """
+        Process a video with the given prompt using Gemini API
+        
+        Args:
+            video_path: Path to the video file
+            prompt: Text prompt to use for video analysis
+            user_email: Email of the user processing the video (for usage tracking)
+            
+        Returns:
+            Dictionary with processing result or error
+        """
+        result = {
+            "success": False,
+            "message": "",
+            "content": ""
+        }
+        
+        logger.info(f"Processing video: {video_path}")
+        logger.info(f"Prompt: {prompt[:100]}..." if len(prompt) > 100 else f"Prompt: {prompt}")
+        
+        if not os.path.exists(video_path):
+            error_msg = f"Video file not found at '{video_path}'"
+            logger.error(error_msg)
+            result["message"] = error_msg
+            return result
+        
+        try:
+            # Get file size
+            file_size = os.path.getsize(video_path)
+            logger.info(f"File size: {file_size / (1024 * 1024):.2f} MB")
+            
+            # Upload the video file
+            logger.info("Uploading video to Gemini API...")
+            
+            # Log the file size in relation to our threshold (for informational purposes only)
+            if file_size > self.CHUNKED_UPLOAD_THRESHOLD:
+                logger.info(f"File size exceeds {self.CHUNKED_UPLOAD_THRESHOLD/(1024*1024):.2f} MB threshold")
+            else:
+                logger.info(f"File size below {self.CHUNKED_UPLOAD_THRESHOLD/(1024*1024):.2f} MB threshold")
+            
+            # All uploads use the same method (our chunking happens in the frontend)
+            # Google API may handle large files internally in their own way
+            video_file = genai.upload_file(
+                path=video_path,
+                display_name=os.path.basename(video_path)
+            )
+            logger.info(f"Upload successful. File URI: {video_file.uri}")
+            logger.info(f"Initial file state: {video_file.state.name}")
+            
+            # Wait for processing if needed
+            processing_wait_count = 0
+            while video_file.state.name == "PROCESSING":
+                processing_wait_count += 1
+                logger.info(f"File is still processing. Wait count: {processing_wait_count}")
+                time.sleep(2)  # Wait for 2 seconds before checking again
+                video_file = genai.get_file(name=video_file.name)  # Re-fetch file state
+                logger.info(f"Updated file state: {video_file.state.name}")
+            
+            if video_file.state.name != "ACTIVE":
+                error_msg = f"Error: File did not become active. Current state: {video_file.state.name}"
+                logger.error(error_msg)
+                result["message"] = error_msg
+                return result
+            
+            # Determine MIME type for the video
+            mime_type, _ = mimetypes.guess_type(video_path)
+            if not mime_type:
+                logger.info(f"Could not determine MIME type, using default: video/mp4")
+                mime_type = "video/mp4"  # Fallback
+            else:
+                logger.info(f"MIME type: {mime_type}")
+            
+            # Create the content parts for the prompt
+            prompt_parts = [
+                {"text": prompt},
+                {"file_data": {
+                    "file_uri": video_file.uri,
+                    "mime_type": mime_type
+                }}
+            ]
+            
+            # Initialize the model and generate content
+            logger.info("Initializing GenerativeModel...")
+            model = genai.GenerativeModel(model_name="gemini-2.5-pro")
+            
+            logger.info("Sending prompt to Gemini for processing...")
+            response = model.generate_content(prompt_parts)
+            logger.info("Received response from Gemini")
+            
+            # Extract the response content
+            content = ""
+            if response.parts:
+                logger.info(f"Response has {len(response.parts)} parts")
+                for i, part in enumerate(response.parts):
+                    if hasattr(part, 'text'):
+                        part_text = part.text
+                        content_preview = part_text[:100] + '...' if len(part_text) > 100 else part_text
+                        logger.info(f"Part {i} (text): {content_preview}")
+                        content += part_text
+                    else:
+                        logger.info(f"Part {i} (no text): {type(part)}")
+            else:
+                logger.warning("No parts in response")
+                if hasattr(response, 'prompt_feedback') and response.prompt_feedback:
+                    logger.warning(f"Prompt feedback: {response.prompt_feedback}")
+            
+            # Set success result
+            result["success"] = True
+            result["content"] = content
+            logger.info(f"Processed result with {len(content)} characters")
+            
+            # Send usage data to webhook for tracking
+            self.send_usage_webhook(user_email, prompt)
+            
+            # Attempt to delete the file from Gemini storage
+            try:
+                logger.info(f"Deleting file from Gemini storage: {video_file.name}")
+                genai.delete_file(name=video_file.name)
+                logger.info("File deleted successfully from Gemini storage")
+            except Exception as del_err:
+                logger.warning(f"Could not delete file from Gemini storage: {str(del_err)}")
+            
+            return result
+            
+        except Exception as e:
+            import traceback
+            error_details = traceback.format_exc()
+            logger.error(f"Error processing video: {str(e)}")
+            logger.error(error_details)
+            result["message"] = f"Error processing video: {str(e)}"
+            result["error_details"] = error_details
+            return result
--- a/extract_user_logs.sh
+++ b/extract_user_logs.sh
@ -0,0 +1,66 @@
+#!/bin/bash
+
+# Script to extract user emails and prompts from veo-video-generator systemd logs
+# Usage: ./extract_user_logs.sh [output_file.csv]
+
+# Set default output file if not provided
+OUTPUT_FILE="${1:-video_generation_usage.csv}"
+
+# Service name (adjust if different)
+SERVICE_NAME="veo-video-generator"
+
+# Temporary file for processing
+TEMP_FILE=$(mktemp)
+
+echo "Extracting logs from systemd service: $SERVICE_NAME"
+echo "Output file: $OUTPUT_FILE"
+
+# Create CSV header
+echo "timestamp,user_email,prompt,video_length_sec,aspect_ratio,person_generation" > "$OUTPUT_FILE"
+
+# Extract logs from journalctl and process them
+journalctl -u "$SERVICE_NAME" --no-pager --output=short-iso | \
+    grep "Raw JSON data received:" | \
+    while IFS= read -r line; do
+        # Extract timestamp (everything before the hostname)
+        timestamp=$(echo "$line" | awk '{print $1}')
+        
+        # Extract the JSON part (everything after "Raw JSON data received: ")
+        json_part=$(echo "$line" | sed -n "s/.*Raw JSON data received: \(.*\)/\1/p")
+        
+        # Check if we got valid JSON
+        if [ -n "$json_part" ]; then
+            # Use jq to parse JSON and extract fields
+            # Handle case where jq might fail on malformed JSON
+            user_email=$(echo "$json_part" | jq -r '.user_email // "N/A"' 2>/dev/null)
+            prompt=$(echo "$json_part" | jq -r '.prompt // "N/A"' 2>/dev/null | sed 's/,/;/g' | sed 's/"/\\"/g')
+            video_length=$(echo "$json_part" | jq -r '.video_length_sec // "N/A"' 2>/dev/null)
+            aspect_ratio=$(echo "$json_part" | jq -r '.aspect_ratio // "N/A"' 2>/dev/null)
+            person_generation=$(echo "$json_part" | jq -r '.person_generation // "N/A"' 2>/dev/null)
+            
+            # Only add to CSV if we successfully extracted data
+            if [ "$user_email" != "null" ] && [ "$user_email" != "N/A" ] && [ "$user_email" != "" ]; then
+                echo "\"$timestamp\",\"$user_email\",\"$prompt\",\"$video_length\",\"$aspect_ratio\",\"$person_generation\"" >> "$OUTPUT_FILE"
+            fi
+        fi
+    done
+
+# Count total records
+record_count=$(wc -l < "$OUTPUT_FILE")
+record_count=$((record_count - 1))  # Subtract header row
+
+echo "Processing complete!"
+echo "Total records extracted: $record_count"
+echo "Output saved to: $OUTPUT_FILE"
+
+# Show summary of unique users
+echo ""
+echo "Unique users found:"
+if [ $record_count -gt 0 ]; then
+    tail -n +2 "$OUTPUT_FILE" | cut -d',' -f2 | sort | uniq -c | sort -nr
+else
+    echo "No records found."
+fi
+
+# Clean up temp file
+rm -f "$TEMP_FILE"
--- a/extract_user_logs_robust.sh
+++ b/extract_user_logs_robust.sh
@ -0,0 +1,175 @@
+#!/bin/bash
+
+# Enhanced script to extract user emails and prompts from veo-video-generator systemd logs
+# Usage: ./extract_user_logs_robust.sh [output_file.csv] [service_name] [date_range]
+# Examples:
+#   ./extract_user_logs_robust.sh usage_report.csv
+#   ./extract_user_logs_robust.sh usage_report.csv veo-video-generator "--since=2024-06-01"
+
+# Set defaults
+OUTPUT_FILE="${1:-video_generation_usage.csv}"
+SERVICE_NAME="${2:-veo-video-generator}"
+DATE_RANGE="${3:-}"
+
+# Colors for output
+RED='\033[0;31m'
+GREEN='\033[0;32m'
+YELLOW='\033[1;33m'
+NC='\033[0m' # No Color
+
+# Function to print colored output
+print_status() {
+    echo -e "${GREEN}[INFO]${NC} $1"
+}
+
+print_warning() {
+    echo -e "${YELLOW}[WARN]${NC} $1"
+}
+
+print_error() {
+    echo -e "${RED}[ERROR]${NC} $1"
+}
+
+# Check if required tools are installed
+check_dependencies() {
+    local missing_deps=()
+    
+    if ! command -v journalctl >/dev/null 2>&1; then
+        missing_deps+=("systemd (journalctl)")
+    fi
+    
+    if ! command -v jq >/dev/null 2>&1; then
+        missing_deps+=("jq")
+    fi
+    
+    if [ ${#missing_deps[@]} -ne 0 ]; then
+        print_error "Missing required dependencies: ${missing_deps[*]}"
+        print_error "Please install missing dependencies and try again"
+        exit 1
+    fi
+}
+
+# Function to validate JSON and extract fields safely
+extract_json_fields() {
+    local json_string="$1"
+    local timestamp="$2"
+    
+    # Try to validate JSON first
+    if echo "$json_string" | jq empty 2>/dev/null; then
+        # Extract fields using jq
+        local user_email=$(echo "$json_string" | jq -r '.user_email // empty' 2>/dev/null)
+        local prompt=$(echo "$json_string" | jq -r '.prompt // empty' 2>/dev/null)
+        local video_length=$(echo "$json_string" | jq -r '.video_length_sec // empty' 2>/dev/null)
+        local aspect_ratio=$(echo "$json_string" | jq -r '.aspect_ratio // empty' 2>/dev/null)
+        local person_generation=$(echo "$json_string" | jq -r '.person_generation // empty' 2>/dev/null)
+        
+        # Clean up prompt for CSV (replace commas and quotes)
+        prompt=$(echo "$prompt" | sed 's/,/;/g' | sed 's/"/\\"/g')
+        
+        # Only output if we have essential fields
+        if [ -n "$user_email" ] && [ "$user_email" != "null" ]; then
+            echo "\"$timestamp\",\"$user_email\",\"$prompt\",\"$video_length\",\"$aspect_ratio\",\"$person_generation\""
+            return 0
+        fi
+    else
+        print_warning "Invalid JSON found at $timestamp: $json_string"
+    fi
+    
+    return 1
+}
+
+print_status "Starting log extraction..."
+print_status "Service: $SERVICE_NAME"
+print_status "Output file: $OUTPUT_FILE"
+if [ -n "$DATE_RANGE" ]; then
+    print_status "Date range: $DATE_RANGE"
+fi
+
+# Check dependencies
+check_dependencies
+
+# Check if service exists
+if ! systemctl list-units --full -a | grep -q "$SERVICE_NAME.service"; then
+    print_warning "Service '$SERVICE_NAME' not found in systemctl list-units"
+    print_warning "This might be normal if the service is not currently loaded"
+fi
+
+# Create CSV header
+echo "timestamp,user_email,prompt,video_length_sec,aspect_ratio,person_generation" > "$OUTPUT_FILE"
+
+# Build journalctl command
+JOURNAL_CMD="journalctl -u $SERVICE_NAME --no-pager --output=short-iso"
+if [ -n "$DATE_RANGE" ]; then
+    JOURNAL_CMD="$JOURNAL_CMD $DATE_RANGE"
+fi
+
+print_status "Extracting logs... (this may take a while for large log files)"
+
+# Counter for processing
+processed_lines=0
+valid_records=0
+
+# Process logs
+eval "$JOURNAL_CMD" | grep "Raw JSON data received:" | while IFS= read -r line; do
+    processed_lines=$((processed_lines + 1))
+    
+    # Show progress every 100 lines
+    if [ $((processed_lines % 100)) -eq 0 ]; then
+        print_status "Processed $processed_lines log lines..."
+    fi
+    
+    # Extract timestamp (first field)
+    timestamp=$(echo "$line" | awk '{print $1}')
+    
+    # Extract JSON part - handle various formats
+    json_part=""
+    if [[ "$line" =~ Raw\ JSON\ data\ received:\ (.+)$ ]]; then
+        json_part="${BASH_REMATCH[1]}"
+    else
+        # Fallback extraction method
+        json_part=$(echo "$line" | sed -n "s/.*Raw JSON data received: \(.*\)/\1/p")
+    fi
+    
+    # Process if we found JSON
+    if [ -n "$json_part" ]; then
+        if extract_json_fields "$json_part" "$timestamp" >> "$OUTPUT_FILE"; then
+            valid_records=$((valid_records + 1))
+        fi
+    fi
+done
+
+# Get final counts (need to do this outside the while loop due to subshell)
+record_count=$(tail -n +2 "$OUTPUT_FILE" | wc -l)
+
+print_status "Processing complete!"
+print_status "Total valid records extracted: $record_count"
+print_status "Output saved to: $OUTPUT_FILE"
+
+if [ $record_count -eq 0 ]; then
+    print_warning "No records found. This could mean:"
+    print_warning "  - No logs exist for the specified service/date range"
+    print_warning "  - The log format has changed"
+    print_warning "  - The service name is incorrect"
+    exit 1
+fi
+
+# Show summary statistics
+echo ""
+print_status "=== SUMMARY REPORT ==="
+
+# Unique users
+echo "Unique users found:"
+tail -n +2 "$OUTPUT_FILE" | cut -d',' -f2 | sed 's/"//g' | sort | uniq -c | sort -nr
+
+# Date range
+echo ""
+echo "Date range of requests:"
+tail -n +2 "$OUTPUT_FILE" | cut -d',' -f1 | sed 's/"//g' | sort | head -1 | xargs -I {} echo "First: {}"
+tail -n +2 "$OUTPUT_FILE" | cut -d',' -f1 | sed 's/"//g' | sort | tail -1 | xargs -I {} echo "Last: {}"
+
+# Most active users
+echo ""
+echo "Top 5 most active users:"
+tail -n +2 "$OUTPUT_FILE" | cut -d',' -f2 | sed 's/"//g' | sort | uniq -c | sort -nr | head -5
+
+print_status "Report generation complete!"
--- a/frontend/.env
+++ b/frontend/.env
@ -0,0 +1,2 @@
+PUBLIC_URL=/video_query
+REACT_APP_BASE_URL=/video_query
--- a/frontend/README.md
+++ b/frontend/README.md
@ -0,0 +1,54 @@
+# Video Query Frontend
+
+This is the frontend for the Video Query application.
+
+## Development
+
+```bash
+npm install
+npm start
+```
+
+## Building for Production
+
+To build the application for deployment at the `/video_query` path:
+
+```bash
+# Option 1: Use the build script (recommended)
+./build.sh
+
+# Option 2: Manual build with correct PUBLIC_URL
+PUBLIC_URL=/video_query npm run build
+```
+
+## Deployment
+
+After building, deploy the contents of the `build` directory to your web server:
+
+```bash
+# Example deployment command
+scp -r build/* user@server:/var/www/html/video_query/
+```
+
+## Troubleshooting
+
+### Static Assets Not Loading
+
+If you see errors like:
+```
+Loading failed for the <script> with source "https://ai-sandbox.oliver.solutions/static/js/main.xxx.js"
+```
+
+This means the application is looking for static assets at the wrong path. Make sure:
+
+1. You built the application with `PUBLIC_URL=/video_query`
+2. The `.env` file contains `PUBLIC_URL=/video_query`
+3. The files are deployed to the correct directory on the server
+
+### Authentication Issues
+
+If you encounter authentication issues:
+
+1. Verify the redirect URI in `src/auth/authConfig.js` matches your deployment URL
+2. Ensure the application is registered correctly in Azure AD
+3. Check browser console for specific authentication errors
--- a/frontend/build.sh
+++ b/frontend/build.sh
@ -0,0 +1,18 @@
+#!/bin/bash
+
+# Clean any previous build
+rm -rf build
+
+# Make sure the .env file exists
+if [ ! -f .env ]; then
+  echo "Creating .env file with correct PUBLIC_URL"
+  echo "PUBLIC_URL=/video_query" > .env
+  echo "REACT_APP_BASE_URL=/video_query" >> .env
+fi
+
+# Build with the public URL explicitly set
+PUBLIC_URL="/video_query" npm run build
+
+echo "Build complete. The 'build' directory now contains files ready for deployment."
+echo "Copy these files to your web server's /video_query directory."
+echo "Example: scp -r build/* user@your-server:/var/www/html/video_query/"
--- a/frontend/package-lock.json
+++ b/frontend/package-lock.json
--- a/frontend/package.json
+++ b/frontend/package.json
@ -0,0 +1,40 @@
+{
+  "name": "video-query-frontend",
+  "version": "0.1.0",
+  "private": true,
+  "dependencies": {
+    "@azure/msal-browser": "^4.12.0",
+    "@azure/msal-react": "^3.0.12",
+    "axios": "^1.6.0",
+    "bootstrap": "^5.3.2",
+    "mermaid": "^11.6.0",
+    "react": "^18.2.0",
+    "react-dom": "^18.2.0",
+    "react-dropzone": "^14.2.3",
+    "react-scripts": "5.0.1",
+    "showdown": "^2.1.0"
+  },
+  "scripts": {
+    "start": "react-scripts start",
+    "build": "PUBLIC_URL=/video_query react-scripts build",
+    "test": "react-scripts test",
+    "eject": "react-scripts eject"
+  },
+  "eslintConfig": {
+    "extends": [
+      "react-app"
+    ]
+  },
+  "browserslist": {
+    "production": [
+      ">0.2%",
+      "not dead",
+      "not op_mini all"
+    ],
+    "development": [
+      "last 1 chrome version",
+      "last 1 firefox version",
+      "last 1 safari version"
+    ]
+  }
+}
--- a/frontend/public/.htaccess
+++ b/frontend/public/.htaccess
@ -0,0 +1,10 @@
+Options -MultiViews
+RewriteEngine On
+RewriteCond %{REQUEST_FILENAME} !-f
+RewriteRule ^ index.html [QSA,L]
+
+<IfModule mod_headers.c>
+    Header set Cache-Control "no-cache, no-store, must-revalidate"
+    Header set Pragma "no-cache"
+    Header set Expires 0
+</IfModule>
--- a/frontend/public/index.html
+++ b/frontend/public/index.html
@ -0,0 +1,23 @@
+<!DOCTYPE html>
+<html lang="en">
+  <head>
+    <meta charset="utf-8" />
+    <link rel="icon" href="%PUBLIC_URL%/favicon.ico" />
+    <meta name="viewport" content="width=device-width, initial-scale=1" />
+    <meta name="theme-color" content="#000000" />
+    <meta
+      name="description"
+      content="Video Query Tool - Process videos with AI"
+    />
+    <link rel="apple-touch-icon" href="%PUBLIC_URL%/logo192.png" />
+    <link rel="manifest" href="%PUBLIC_URL%/manifest.json" />
+    <link rel="preconnect" href="https://fonts.googleapis.com">
+    <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
+    <link href="https://fonts.googleapis.com/css2?family=Montserrat:wght@300;400;500;600;700&display=swap" rel="stylesheet">
+    <title>Video Query Tool</title>
+  </head>
+  <body>
+    <noscript>You need to enable JavaScript to run this app.</noscript>
+    <div id="root"></div>
+  </body>
+</html>
--- a/frontend/public/manifest.json
+++ b/frontend/public/manifest.json
@ -0,0 +1,25 @@
+{
+  "short_name": "Video Query",
+  "name": "Video Query Tool",
+  "icons": [
+    {
+      "src": "favicon.ico",
+      "sizes": "64x64 32x32 24x24 16x16",
+      "type": "image/x-icon"
+    },
+    {
+      "src": "logo192.png",
+      "type": "image/png",
+      "sizes": "192x192"
+    },
+    {
+      "src": "logo512.png",
+      "type": "image/png",
+      "sizes": "512x512"
+    }
+  ],
+  "start_url": ".",
+  "display": "standalone",
+  "theme_color": "#000000",
+  "background_color": "#ffffff"
+}
--- a/frontend/requirements.txt
+++ b/frontend/requirements.txt
@ -0,0 +1,48 @@
+annotated-types==0.7.0
+blinker==1.9.0
+cachetools==5.5.2
+certifi==2025.4.26
+charset-normalizer==3.4.2
+click==8.2.0
+ecdsa==0.19.1
+Flask==3.1.0
+flask-cors==5.0.1
+google-ai-generativelanguage==0.6.15
+google-api-core==2.25.0rc0
+google-api-python-client==2.169.0
+google-auth==2.40.0
+google-auth-httplib2==0.2.0
+google-generativeai==0.8.5
+googleapis-common-protos==1.70.0
+grpcio==1.71.0
+grpcio-status==1.71.0
+h11==0.16.0
+h2==4.2.0
+hpack==4.1.0
+httplib2==0.22.0
+Hypercorn==0.17.3
+hyperframe==6.1.0
+idna==3.10
+itsdangerous==2.2.0
+Jinja2==3.1.6
+MarkupSafe==3.0.2
+priority==2.0.0
+proto-plus==1.26.1
+protobuf==5.29.4
+pyasn1==0.4.8
+pyasn1_modules==0.4.2
+pydantic==2.11.4
+pydantic_core==2.33.2
+pyparsing==3.2.3
+python-dotenv==1.1.0
+python-jose==3.4.0
+requests==2.32.3
+rsa==4.9.1
+six==1.17.0
+tqdm==4.67.1
+typing-inspection==0.4.0
+typing_extensions==4.13.2
+uritemplate==4.1.1
+urllib3==2.4.0
+Werkzeug==3.1.3
+wsproto==1.2.0
--- a/frontend/src/App.js
+++ b/frontend/src/App.js
@ -0,0 +1,388 @@
+import React, { useState } from 'react';
+import { authApiClient } from './auth/authApiClient';
+import { AuthenticatedTemplate, UnauthenticatedTemplate, useMsal } from '@azure/msal-react';
+import { InteractionStatus } from '@azure/msal-browser';
+import AuthenticatedContent from './components/AuthenticatedContent';
+import Login from './components/Login';
+import ChunkedUploader from './utils/chunkedUploader';
+import { loginRequest } from './auth/authConfig';
+
+function App() {
+  // MSAL authentication hook
+  const { instance, inProgress, accounts } = useMsal();
+  const [selectedFile, setSelectedFile] = useState(null);
+  const [fileName, setFileName] = useState('');
+  const [mode, setMode] = useState('meeting_summary');
+  const [prompt, setPrompt] = useState(
+    "Generate a detailed summary of the meeting in the attached video recording, including discussion points and action items with owners"
+  );
+  const [result, setResult] = useState('');
+  const [isLoading, setIsLoading] = useState(false);
+  const [error, setError] = useState('');
+  const [uploadProgress, setUploadProgress] = useState(0);
+  const [authChecked, setAuthChecked] = useState(false);
+  
+  const handleVideoSelect = (file) => {
+    setSelectedFile(file);
+    setFileName(file.name);
+    setError('');
+  };
+  
+  const handleProcessVideo = async () => {
+    // Validation
+    if (!selectedFile) {
+      setError('Please select a video file first');
+      return;
+    }
+    
+    if (!prompt.trim()) {
+      setError('Please enter a prompt');
+      return;
+    }
+    
+    setError('');
+    setIsLoading(true);
+    setResult('');
+    setUploadProgress(0);
+    
+    try {
+      const fileSize = selectedFile.size;
+      const fileSizeMB = fileSize / (1024 * 1024);
+      let response;
+      
+      console.log(`Starting upload of ${selectedFile.name} (${fileSizeMB.toFixed(2)} MB)`);
+      
+      // Always use chunked upload regardless of file size
+      console.log('Using chunked upload for all files');
+      
+      // Create chunked uploader
+      const uploader = new ChunkedUploader(selectedFile, (progress) => {
+        console.log(`Upload progress: ${progress}%`);
+        setUploadProgress(progress);
+      });
+      
+      // Variable to store upload result
+      let chunkUploadResult;
+      
+      try {
+        // Start the chunked upload
+        console.log('Starting chunked upload process...');
+        chunkUploadResult = await uploader.uploadFile();
+        console.log('Upload result:', chunkUploadResult);
+        
+        if (!chunkUploadResult.success) {
+          throw new Error('Chunked upload failed');
+        }
+        
+        console.log('Chunked upload complete, starting processing');
+        console.log('File path:', chunkUploadResult.file_path);
+        console.log('Filename:', chunkUploadResult.filename);
+        setUploadProgress(100);
+      } catch (uploadError) {
+        console.error('Chunked upload error:', uploadError);
+        throw uploadError;
+      }
+      
+      // Now process the uploaded file
+      response = await authApiClient.post(
+        'https://ai-sandbox.oliver.solutions/video_query_back/api/process',
+        {
+          file_path: chunkUploadResult.file_path,
+          filename: chunkUploadResult.filename,
+          prompt: prompt
+        },
+        {
+          headers: {
+            'Content-Type': 'application/json'
+          },
+          timeout: 3600000 // 60 minutes timeout
+        }
+      );
+      
+      // Handle success with additional error checking
+      if (response && response.data && response.data.success) {
+        console.log('Processing successful, setting result');
+        setResult(response.data.content);
+      } else {
+        const errorMessage = response?.data?.message || 'Processing failed';
+        console.error('Processing failed:', errorMessage);
+        setError(errorMessage);
+      }
+    } catch (err) {
+      console.error('Error processing video:', err);
+      setError(
+        err.response?.data?.message || 
+        'Failed to process the video. Please try again or use a shorter video.'
+      );
+    } finally {
+      setIsLoading(false);
+    }
+  };
+  
+  const resetForm = () => {
+    setSelectedFile(null);
+    setFileName('');
+    setResult('');
+    setError('');
+  };
+  
+  // Handle login
+  const handleLogin = () => {
+    instance.loginPopup(loginRequest)
+      .then(response => {
+        console.log("Login successful", response);
+        if (response.account) {
+          instance.setActiveAccount(response.account);
+        }
+      })
+      .catch(e => {
+        console.error("Login error:", e);
+        // Fallback to redirect if popup fails
+        if (e.name === "PopupWindowError" || e.name === "BrowserAuthError") {
+          console.log("Fallback to redirect login");
+          instance.loginRedirect(loginRequest);
+        }
+      });
+  };
+  
+  // Check for token in URL hash (from implicit flow) and handle auth status
+  React.useEffect(() => {
+    const checkAuthStatus = async () => {
+      try {
+        // Check if we were redirected here due to token expiration
+        const redirectedForLogin = sessionStorage.getItem("redirectedForLogin");
+        if (redirectedForLogin === "true") {
+          console.log("App: App was redirected here due to token expiration, forcing login");
+          // Clear the flag immediately to prevent loops
+          sessionStorage.removeItem("redirectedForLogin");
+          
+          // Clear remaining auth data if any
+          sessionStorage.removeItem("accessToken");
+          sessionStorage.removeItem("idToken");
+          sessionStorage.removeItem("tokenExpiresAt");
+          sessionStorage.removeItem("loginState");
+          sessionStorage.removeItem("loginNonce");
+          
+          // Force login via the Login component
+          setAuthChecked(true);
+          return;
+        }
+        
+        // First, check if there's a hash in the URL (from implicit flow redirect)
+        console.log("App: Checking for hash in URL and auth status");
+        const hash = window.location.hash;
+        
+        if (hash && (hash.includes("access_token=") || hash.includes("id_token="))) {
+          console.log("App: Found token in URL hash, processing...");
+          
+          // Parse the hash to extract tokens
+          const params = new URLSearchParams(hash.substring(1));
+          const accessToken = params.get("access_token");
+          const idToken = params.get("id_token");
+          const state = params.get("state");
+          const expiresIn = params.get("expires_in");
+          const nonce = params.get("nonce");
+          
+          // Verify state matches what we stored
+          const storedState = sessionStorage.getItem("loginState");
+          const storedNonce = sessionStorage.getItem("loginNonce");
+          
+          if (state && storedState && state === storedState) {
+            console.log("App: State validation successful");
+            
+            // Process the tokens - prioritize ID token for backend compatibility
+            if (idToken) {
+              console.log("App: Successfully received ID token");
+              
+              // Store both tokens in sessionStorage (be careful with security)
+              sessionStorage.setItem("idToken", idToken);
+              
+              // For API calls that require it
+              if (accessToken) {
+                console.log("App: Also received access token");
+                sessionStorage.setItem("accessToken", accessToken);
+                
+                // Calculate expiration time
+                if (expiresIn) {
+                  const expiresInSeconds = parseInt(expiresIn, 10);
+                  const expiresAt = new Date().getTime() + expiresInSeconds * 1000;
+                  sessionStorage.setItem("tokenExpiresAt", expiresAt.toString());
+                }
+              }
+              
+              // Clear the hash from the URL to prevent issues on refresh
+              window.history.replaceState(null, document.title, window.location.pathname);
+              
+              // Force reload to update auth state
+              window.location.reload();
+              return;
+            } else if (accessToken) {
+              console.log("App: Only received access token (no ID token)");
+              
+              // Store the token in sessionStorage (be careful with security)
+              sessionStorage.setItem("accessToken", accessToken);
+              
+              // Calculate expiration time
+              if (expiresIn) {
+                const expiresInSeconds = parseInt(expiresIn, 10);
+                const expiresAt = new Date().getTime() + expiresInSeconds * 1000;
+                sessionStorage.setItem("tokenExpiresAt", expiresAt.toString());
+              }
+              
+              // Clear the hash from the URL to prevent issues on refresh
+              window.history.replaceState(null, document.title, window.location.pathname);
+              
+              // Force reload to update auth state
+              window.location.reload();
+              return;
+            }
+          } else {
+            console.error("App: State validation failed or missing");
+          }
+        }
+        
+        // Check if we have a stored token that's not expired
+        const storedToken = sessionStorage.getItem("accessToken");
+        const idToken = sessionStorage.getItem("idToken");
+        const tokenExpiresAt = sessionStorage.getItem("tokenExpiresAt");
+        
+        if ((storedToken || idToken) && tokenExpiresAt) {
+          const now = new Date().getTime();
+          const expiresAt = parseInt(tokenExpiresAt, 10);
+          
+          if (now < expiresAt) {
+            console.log("App: Found valid stored token");
+            // We have a valid token, continue
+            setAuthChecked(true);
+            return;
+          } else {
+            console.log("App: Stored token expired, clearing all auth data");
+            // Token expired, clear all auth data
+            sessionStorage.removeItem("accessToken");
+            sessionStorage.removeItem("idToken");
+            sessionStorage.removeItem("tokenExpiresAt");
+            sessionStorage.removeItem("loginState");
+            sessionStorage.removeItem("loginNonce");
+            
+            // Set the redirected flag to ensure we show login screen
+            sessionStorage.setItem("redirectedForLogin", "true");
+            
+            // Clear MSAL cache
+            try {
+              console.log("App: Clearing MSAL cache");
+              instance.clearCache();
+              await instance.logout({
+                onRedirectNavigate: () => false // Don't redirect yet, wait for page reload
+              });
+            } catch (e) {
+              console.error("App: Error clearing MSAL cache:", e);
+            }
+            
+            // Force a reload after a brief delay to ensure we go back to login page
+            setTimeout(() => {
+              window.location.reload();
+            }, 100);
+            return;
+          }
+        }
+        
+        // Now proceed with normal MSAL account check
+        if (inProgress !== InteractionStatus.Login) {
+          console.log("App: No login in progress, checking MSAL accounts");
+          
+          // Get all accounts and active account
+          const allAccounts = instance.getAllAccounts();
+          console.log("App: Found MSAL accounts:", allAccounts.length);
+          
+          // If we have accounts but no active account, set one
+          if (allAccounts.length > 0) {
+            if (!instance.getActiveAccount()) {
+              // Set the first account as active
+              console.log("App: Setting active account from MSAL:", allAccounts[0].name);
+              instance.setActiveAccount(allAccounts[0]);
+            } else {
+              console.log("App: MSAL active account already set:", instance.getActiveAccount().name);
+            }
+          } else {
+            console.log("App: No MSAL accounts found");
+          }
+        }
+        
+        // Mark auth check as complete
+        setAuthChecked(true);
+      } catch (error) {
+        console.error("App: Error checking auth status:", error);
+        setAuthChecked(true); // Mark as checked even on error
+      }
+    };
+    
+    checkAuthStatus();
+  }, [instance, inProgress, accounts]);
+
+  // Check if we have a manually stored token (from implicit flow)
+  const isManuallyAuthenticated = () => {
+    try {
+      // Check for ID token first (preferred for backend)
+      const idToken = sessionStorage.getItem("idToken");
+      if (idToken) {
+        return true;
+      }
+      
+      // Fall back to checking access token
+      const accessToken = sessionStorage.getItem("accessToken");
+      const expiresAt = sessionStorage.getItem("tokenExpiresAt");
+      
+      if (accessToken && expiresAt) {
+        const now = new Date().getTime();
+        return now < parseInt(expiresAt, 10);
+      }
+      return false;
+    } catch (e) {
+      console.error("Error checking manual auth:", e);
+      return false;
+    }
+  };
+  
+  return (
+    <div className="container my-4">
+      <div className="row">
+        <div className="col-12">
+          {/* Show loading while auth is being checked */}
+          {!authChecked || inProgress === InteractionStatus.Login ? (
+            <div className="text-center my-5">
+              <div className="spinner-border text-primary" role="status">
+                <span className="visually-hidden">Loading...</span>
+              </div>
+              <p className="mt-3">Verifying authentication status...</p>
+            </div>
+          ) : (
+            // Check both MSAL auth and our manual token auth
+            (accounts.length > 0 || isManuallyAuthenticated()) ? (
+              // User is authenticated - show the application
+              <AuthenticatedContent
+                selectedFile={selectedFile}
+                fileName={fileName}
+                mode={mode}
+                prompt={prompt}
+                result={result}
+                isLoading={isLoading}
+                error={error}
+                uploadProgress={uploadProgress}
+                onVideoSelect={handleVideoSelect}
+                onModeChange={setMode}
+                onPromptChange={setPrompt}
+                handleProcessVideo={handleProcessVideo}
+                resetForm={resetForm}
+              />
+            ) : (
+              // User is not authenticated - show login
+              <Login />
+            )
+          )}
+        </div>
+      </div>
+    </div>
+  );
+}
+
+export default App;
--- a/frontend/src/auth/AuthProvider.js
+++ b/frontend/src/auth/AuthProvider.js
@ -0,0 +1,167 @@
+import React, { useEffect } from 'react';
+import { MsalProvider } from '@azure/msal-react';
+import { PublicClientApplication, EventType, InteractionType } from '@azure/msal-browser';
+import { msalConfig } from './authConfig';
+
+// Use the standard msalConfig but with implicit flow
+// This approach should work when the app is registered as a non-SPA client
+const msalConfig_enhanced = {
+    ...msalConfig
+};
+
+// Initialize MSAL
+export const msalInstance = new PublicClientApplication(msalConfig_enhanced);
+
+// Initialize MSAL instance
+(async () => {
+    try {
+        console.log("Initializing MSAL instance...");
+        await msalInstance.initialize();
+        console.log("MSAL instance initialized successfully");
+        
+        // Try to set active account after initialization
+        if (!msalInstance.getActiveAccount() && msalInstance.getAllAccounts().length > 0) {
+            console.log("Setting active account during initialization");
+            msalInstance.setActiveAccount(msalInstance.getAllAccounts()[0]);
+        }
+
+        // Handle any initial redirect response at startup
+        try {
+            console.log("Checking for redirect response at startup...");
+            const response = await msalInstance.handleRedirectPromise();
+            if (response) {
+                console.log("Found redirect response at startup:", response);
+                if (response.account) {
+                    console.log("Setting active account from redirect response:", response.account.name);
+                    msalInstance.setActiveAccount(response.account);
+                }
+            } else {
+                console.log("No redirect response at startup");
+            }
+        } catch (redirectErr) {
+            console.error("Error handling redirect at startup:", redirectErr);
+        }
+
+        // Configure event callbacks for authentication events
+        msalInstance.addEventCallback((event) => {
+            // Handle successful logins
+            if (event.eventType === EventType.LOGIN_SUCCESS) {
+                console.log("Login success event triggered", event);
+                if (event.payload && event.payload.account) {
+                    console.log("Setting active account from event:", event.payload.account.name);
+                    msalInstance.setActiveAccount(event.payload.account);
+                    // Force reload to update authentication state
+                    if (event.interactionType === "redirect") {
+                        window.location.reload();
+                    }
+                }
+            }
+            // Handle login failures
+            else if (event.eventType === EventType.LOGIN_FAILURE) {
+                console.error("Login failure:", event.error);
+            }
+            // Handle successful silent token acquisitions
+            else if (event.eventType === EventType.ACQUIRE_TOKEN_SUCCESS) {
+                console.log("Token acquisition successful", event);
+            }
+            // Handle token acquisition failures
+            else if (event.eventType === EventType.ACQUIRE_TOKEN_FAILURE) {
+                console.error("Token acquisition failed:", event.error);
+                // If the error is due to an expired token, try to handle it
+                if (event.error && event.error.errorCode === "consent_required") {
+                    console.log("Consent required, prompting for login");
+                }
+            }
+            // Log when user logs out
+            else if (event.eventType === EventType.LOGOUT_SUCCESS) {
+                console.log("Logout successful");
+            }
+            // Handle redirect success
+            else if (event.eventType === EventType.HANDLE_REDIRECT_END) {
+                console.log("Redirect handling completed");
+            }
+        });
+        
+        console.log("Event callbacks registered");
+    } catch (error) {
+        console.error("Error initializing MSAL:", error);
+    }
+})();
+
+/**
+ * MSAL Provider Component to wrap the application with authentication context
+ */
+export const AuthProvider = ({ children }) => {
+    const [isInitialized, setIsInitialized] = React.useState(false);
+    
+    // Check for authentication on component mount
+    useEffect(() => {
+        const initializeAndHandleRedirect = async () => {
+            try {
+                // Ensure MSAL is initialized
+                if (!msalInstance.initialized) {
+                    console.log("Initializing MSAL from component...");
+                    await msalInstance.initialize();
+                    console.log("MSAL initialized from component");
+                }
+                
+                // Handle any redirect response with PKCE auth code flow
+                try {
+                    console.log("Handling redirect promise with PKCE...");
+                    // This properly handles auth code + PKCE flow redirects
+                    const response = await msalInstance.handleRedirectPromise();
+                    
+                    // If we have a response, we just returned from a redirect
+                    if (response) {
+                        console.log("Redirect response from PKCE flow:", response);
+                        if (response.account) {
+                            console.log("Setting active account after PKCE redirect:", response.account.name);
+                            msalInstance.setActiveAccount(response.account);
+                        }
+                    } else {
+                        console.log("No redirect response");
+                        // Try to set active account if not already set
+                        const accounts = msalInstance.getAllAccounts();
+                        console.log("Accounts found:", accounts.length);
+                        if (accounts.length > 0 && !msalInstance.getActiveAccount()) {
+                            console.log("Setting active account from cached accounts:", accounts[0].name);
+                            msalInstance.setActiveAccount(accounts[0]);
+                        }
+                    }
+                } catch (redirectErr) {
+                    console.error("Error handling PKCE redirect:", redirectErr);
+                    console.error("Redirect error details:", JSON.stringify(redirectErr, null, 2));
+                }
+                
+                // Mark as initialized
+                setIsInitialized(true);
+            } catch (initErr) {
+                console.error("Error during MSAL initialization:", initErr);
+                // Even if there's an error, mark as initialized to avoid infinite loop
+                setIsInitialized(true);
+            }
+        };
+        
+        initializeAndHandleRedirect();
+    }, []);
+
+    // Show loading until MSAL is initialized
+    if (!isInitialized) {
+        return (
+            <div className="d-flex justify-content-center align-items-center" style={{ height: '100vh' }}>
+                <div className="spinner-border text-primary" role="status">
+                    <span className="visually-hidden">Loading authentication...</span>
+                </div>
+                <p className="ms-3">Initializing authentication...</p>
+            </div>
+        );
+    }
+
+    return (
+        <MsalProvider instance={msalInstance}>
+            {children}
+        </MsalProvider>
+    );
+};
+
+export default AuthProvider;
--- a/frontend/src/auth/authApiClient.js
+++ b/frontend/src/auth/authApiClient.js
@ -0,0 +1,146 @@
+import axios from 'axios';
+import { msalInstance } from './AuthProvider';
+import { loginRequest } from './authConfig';
+
+/**
+ * Authenticated HTTP client that adds the Authorization header with bearer token
+ */
+export const authApiClient = axios.create();
+
+// Flag to prevent multiple redirects at once
+let isRedirecting = false;
+
+// Function to handle logging out and redirect to login
+const redirectToLogin = async () => {
+    if (isRedirecting) return;
+    isRedirecting = true;
+    
+    console.log("API: Redirecting to login page");
+    
+    // First clear all stored auth data
+    sessionStorage.removeItem("accessToken");
+    sessionStorage.removeItem("idToken");
+    sessionStorage.removeItem("tokenExpiresAt");
+    sessionStorage.removeItem("loginState");
+    sessionStorage.removeItem("loginNonce");
+    
+    // Clear MSAL caches before redirecting
+    try {
+        console.log("API: Clearing MSAL cache before redirect");
+        msalInstance.clearCache();
+        await msalInstance.logout({
+            onRedirectNavigate: () => false // Don't redirect yet, we'll do it manually
+        });
+    } catch (e) {
+        console.error("API: Error clearing cache:", e);
+    }
+    
+    // Set a flag in sessionStorage to indicate we're expecting a login
+    sessionStorage.setItem("redirectedForLogin", "true");
+    
+    // Redirect to login page
+    window.location.href = window.location.origin + "/video_query/";
+};
+
+// Add request interceptor to add auth token to all API requests
+authApiClient.interceptors.request.use(
+    async (config) => {
+        // First check for ID token (preferred for backend compatibility)
+        const idToken = sessionStorage.getItem("idToken");
+        if (idToken) {
+            console.log("API: Using ID token for backend auth");
+            config.headers.Authorization = `Bearer ${idToken}`;
+            return config;
+        }
+        
+        // Fallback to access token if ID token not available
+        const manualAccessToken = sessionStorage.getItem("accessToken");
+        const tokenExpiresAt = sessionStorage.getItem("tokenExpiresAt");
+        
+        // If we have a valid manual token, use it
+        if (manualAccessToken && tokenExpiresAt) {
+            const now = new Date().getTime();
+            const expiresAt = parseInt(tokenExpiresAt, 10);
+            
+            if (now < expiresAt) {
+                console.log("API: Using manually stored access token");
+                config.headers.Authorization = `Bearer ${manualAccessToken}`;
+                return config;
+            } else {
+                console.log("API: Manual token expired, removing");
+                // Token expired, handle login redirect
+                await redirectToLogin();
+                return config;
+            }
+        }
+        
+        // If no manual token, try MSAL approach as fallback
+        console.log("API: No manual token, trying MSAL");
+        
+        // Get active account and check if token exists
+        const account = msalInstance.getActiveAccount();
+        if (!account) {
+            console.warn("API: No active account! Redirecting to login...");
+            await redirectToLogin();
+            return config;
+        }
+        
+        try {
+            // Get token silently through MSAL
+            const response = await msalInstance.acquireTokenSilent({
+                ...loginRequest,
+                account: account
+            });
+            
+            // Set the Authorization header
+            console.log("API: Got token from MSAL");
+            config.headers.Authorization = `Bearer ${response.accessToken}`;
+            return config;
+        } catch (error) {
+            console.error("API: Error acquiring token:", error);
+            
+            // Check if the error is due to expired token
+            if (error.name === "InteractionRequiredAuthError" || 
+                error.message?.includes("expired") || 
+                error.message?.includes("AADSTS70016")) {
+                
+                console.log("API: Token expired or interaction required, logging out");
+                await redirectToLogin();
+            } else {
+                // For other errors, also redirect to login
+                console.log("API: Authentication error, redirecting to login");
+                await redirectToLogin();
+            }
+            
+            return config;
+        }
+    },
+    (error) => {
+        return Promise.reject(error);
+    }
+);
+
+// Add response interceptor to handle 401 Unauthorized errors
+authApiClient.interceptors.response.use(
+    (response) => {
+        return response;
+    },
+    async (error) => {
+        // Check if error is due to an unauthorized request (status 401)
+        if (error.response && error.response.status === 401) {
+            console.log("API: Received 401 Unauthorized response, redirecting to login");
+            
+            // Clear tokens and redirect to login
+            await redirectToLogin();
+            
+            // Create a friendly error message
+            const customError = new Error("Your session has expired. Please log in again.");
+            customError.originalError = error;
+            return Promise.reject(customError);
+        }
+        
+        return Promise.reject(error);
+    }
+);
+
+export default authApiClient;
--- a/frontend/src/auth/authConfig.js
+++ b/frontend/src/auth/authConfig.js
@ -0,0 +1,44 @@
+/*
+ * MSAL configuration for authentication
+ */
+
+// Get the public URL from environment or use default
+const publicUrl = process.env.REACT_APP_BASE_URL || '/video_query';
+
+export const msalConfig = {
+    auth: {
+        clientId: "9079054c-9620-4757-a256-23413042f1ef",
+        authority: "https://login.microsoftonline.com/e519c2e6-bc6d-4fdf-8d9c-923c2f002385",
+        redirectUri: "https://ai-sandbox.oliver.solutions/video_query/",
+        postLogoutRedirectUri: "https://ai-sandbox.oliver.solutions/video_query/",
+        navigateToLoginRequestUrl: true
+    },
+    cache: {
+        cacheLocation: "sessionStorage",
+        storeAuthStateInCookie: true,
+    },
+    system: {
+        allowRedirectInIframe: true,
+        tokenRenewalOffsetSeconds: 300,
+        // Log all messages for debugging
+        loggerOptions: {
+            loggerCallback: (level, message) => {
+                console.log(`MSAL: ${message}`);
+            },
+            logLevel: 4 // Verbose
+        }
+    }
+};
+
+// Add scopes here for access token request
+// For more information about scopes visit: 
+// https://learn.microsoft.com/en-us/azure/active-directory/develop/permissions-consent-overview
+export const loginRequest = {
+    scopes: ["User.Read"]
+};
+
+// Add endpoints here for API calls
+export const apiConfig = {
+    videoProcessingEndpoint: "https://ai-sandbox.oliver.solutions/video_query_back/api/process",
+    chunkedUploadEndpoint: "https://ai-sandbox.oliver.solutions/video_query_back/api"
+};
--- a/frontend/src/components/AuthenticatedContent.js
+++ b/frontend/src/components/AuthenticatedContent.js
@ -0,0 +1,99 @@
+import React from 'react';
+import { useMsal } from '@azure/msal-react';
+import VideoUpload from './VideoUpload';
+import PromptSelector from './PromptSelector';
+import ResultDisplay from './ResultDisplay';
+
+const AuthenticatedContent = (props) => {
+    const { instance } = useMsal();
+    const activeAccount = instance.getActiveAccount();
+    
+    const handleLogout = () => {
+        instance.logoutRedirect();
+    };
+    
+    return (
+        <div>
+            <div className="d-flex justify-content-between align-items-center mb-4">
+                <h1>Video Query Tool</h1>
+                <div className="d-flex align-items-center">
+                    {activeAccount && (
+                        <div className="me-3">
+                            <small>Signed in as: <strong>{activeAccount.name}</strong></small>
+                        </div>
+                    )}
+                    <button 
+                        className="btn btn-outline-secondary btn-sm"
+                        onClick={handleLogout}
+                    >
+                        Sign Out
+                    </button>
+                </div>
+            </div>
+            
+            {!props.result && (
+                <>
+                    <VideoUpload onVideoSelect={props.onVideoSelect} />
+                    
+                    {props.selectedFile && (
+                        <div className="mb-3">
+                            <div className="alert alert-success d-flex align-items-center">
+                                <svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="currentColor" className="bi bi-check-circle-fill me-2" viewBox="0 0 16 16">
+                                    <path d="M16 8A8 8 0 1 1 0 8a8 8 0 0 1 16 0zm-3.97-3.03a.75.75 0 0 0-1.08.022L7.477 9.417 5.384 7.323a.75.75 0 0 0-1.06 1.06L6.97 11.03a.75.75 0 0 0 1.079-.02l3.992-4.99a.75.75 0 0 0-.01-1.05z"/>
+                                </svg>
+                                <div>
+                                    <strong>Video selected:</strong> {props.fileName}
+                                </div>
+                            </div>
+                        </div>
+                    )}
+                    
+                    <PromptSelector 
+                        mode={props.mode}
+                        onModeChange={props.onModeChange}
+                        prompt={props.prompt}
+                        onPromptChange={props.onPromptChange}
+                        disabled={props.isLoading}
+                    />
+                    
+                    {props.error && (
+                        <div className="alert alert-danger mb-3">
+                            {props.error}
+                        </div>
+                    )}
+                    
+                    <div className="d-grid gap-2 d-md-flex mb-4">
+                        <button 
+                            className="btn btn-primary"
+                            onClick={props.handleProcessVideo}
+                            disabled={!props.selectedFile || props.isLoading}
+                        >
+                            {props.isLoading ? (
+                                <>
+                                    <span className="spinner-border spinner-border-sm me-2" role="status" aria-hidden="true"></span>
+                                    Processing...
+                                </>
+                            ) : 'Process Video'}
+                        </button>
+                    </div>
+                </>
+            )}
+            
+            <ResultDisplay 
+                result={props.result} 
+                isLoading={props.isLoading} 
+                uploadProgress={props.uploadProgress} 
+            />
+            
+            {props.result && (
+                <div className="mt-4">
+                    <button className="btn btn-secondary" onClick={props.resetForm}>
+                        Process Another Video
+                    </button>
+                </div>
+            )}
+        </div>
+    );
+};
+
+export default AuthenticatedContent;
--- a/frontend/src/components/Login.js
+++ b/frontend/src/components/Login.js
@ -0,0 +1,137 @@
+import React, { useEffect, useState } from 'react';
+import { useMsal } from '@azure/msal-react';
+import { loginRequest } from '../auth/authConfig';
+
+const Login = () => {
+    const { instance, accounts, inProgress } = useMsal();
+    const [initializing, setInitializing] = useState(true);
+    const [loginAttempted, setLoginAttempted] = useState(false);
+    
+    // Auto-login if there are accounts but none active
+    useEffect(() => {
+        const checkAccounts = async () => {
+            try {
+                // Ensure MSAL is initialized
+                if (!instance.initialized) {
+                    console.log("Login: Waiting for MSAL to initialize...");
+                    // Wait a moment for initialization
+                    await new Promise(resolve => setTimeout(resolve, 1000));
+                }
+                
+                const allAccounts = instance.getAllAccounts();
+                console.log("Login: Accounts found:", allAccounts.length);
+                
+                // If we have accounts but no active account, set the first one active
+                if (allAccounts.length > 0 && !instance.getActiveAccount()) {
+                    console.log("Login: Setting active account:", allAccounts[0].name);
+                    instance.setActiveAccount(allAccounts[0]);
+                    // Force a reload to ensure the authenticated state is properly recognized
+                    window.location.reload();
+                }
+                
+                setInitializing(false);
+            } catch (error) {
+                console.error("Login: Error checking accounts:", error);
+                setInitializing(false);
+            }
+        };
+        
+        checkAccounts();
+    }, [instance, accounts]);
+    
+    const handleLogin = async () => {
+        setLoginAttempted(true);
+        
+        // Log any existing accounts for debugging
+        const allAccounts = instance.getAllAccounts();
+        if (allAccounts.length > 0) {
+            console.log("Login: Existing accounts found:", allAccounts.map(a => a.username || a.name));
+            
+            // If we have accounts, just set one active instead of logging in again
+            if (!instance.getActiveAccount()) {
+                console.log("Login: Setting active account from existing accounts");
+                instance.setActiveAccount(allAccounts[0]);
+                // Force a reload to refresh the auth state
+                window.location.reload();
+                return; // Exit early - no need to log in again
+            }
+        } else {
+            console.log("Login: No existing accounts found, proceeding with login");
+        }
+        
+        // Open Azure AD login page directly
+        // Use id_token for better backend compatibility
+        const tenantId = "e519c2e6-bc6d-4fdf-8d9c-923c2f002385";
+        const clientId = "9079054c-9620-4757-a256-23413042f1ef";
+        const redirectUri = encodeURIComponent("https://ai-sandbox.oliver.solutions/video_query/");
+        const responseType = "id_token+token"; // Get both ID token and access token
+        const scope = encodeURIComponent("openid profile email User.Read");
+        const nonce = Math.random().toString(36).substring(2, 15);
+        const state = Math.random().toString(36).substring(2, 15);
+        
+        // Store state and nonce in session storage to verify later
+        sessionStorage.setItem("loginState", state);
+        sessionStorage.setItem("loginNonce", nonce);
+        
+        // Construct the authorization URL for implicit flow with ID token
+        const authUrl = `https://login.microsoftonline.com/${tenantId}/oauth2/v2.0/authorize?` +
+            `client_id=${clientId}` +
+            `&response_type=${responseType}` +
+            `&redirect_uri=${redirectUri}` +
+            `&scope=${scope}` +
+            `&state=${state}` +
+            `&nonce=${nonce}` +
+            `&response_mode=fragment` +
+            `&prompt=select_account`;
+        
+        console.log("Login: Redirecting to authorization endpoint with implicit flow");
+        
+        // Redirect to the authorization endpoint
+        window.location.href = authUrl;
+    };
+    
+    // Show appropriate loading or login state
+    if (initializing) {
+        return (
+            <div className="text-center my-5">
+                <div className="spinner-border text-primary" role="status">
+                    <span className="visually-hidden">Loading...</span>
+                </div>
+                <p className="mt-3">Checking authentication status...</p>
+            </div>
+        );
+    }
+    
+    // Show loading during the auth process
+    if (inProgress === "login" || loginAttempted) {
+        return (
+            <div className="text-center my-5">
+                <div className="spinner-border text-primary" role="status">
+                    <span className="visually-hidden">Loading...</span>
+                </div>
+                <p className="mt-3">Signing in with Microsoft...</p>
+                <p className="text-muted small">
+                    If the login process seems stuck, please refresh the page and try again.
+                </p>
+            </div>
+        );
+    }
+    
+    return (
+        <div className="text-center my-5 p-5 bg-light rounded">
+            <h2 className="mb-4">Please sign in</h2>
+            <p className="mb-4">
+                You need to sign in with your Microsoft account to use this application.
+            </p>
+            <button 
+                className="btn btn-primary btn-lg"
+                onClick={handleLogin}
+                disabled={inProgress === "login"}
+            >
+                Sign in with Microsoft
+            </button>
+        </div>
+    );
+};
+
+export default Login;
--- a/frontend/src/components/PromptSelector.js
+++ b/frontend/src/components/PromptSelector.js
@ -0,0 +1,53 @@
+import React from 'react';
+
+// Predefined prompts for each mode
+const PROMPTS = {
+  meeting_summary: "Generate a detailed summary of the meeting in the attached video recording, including discussion points and action items with owners",
+  process_documentation: "Generate detailed process documentation suitable for reference or training based on the process illustrated in the attached video recording. Write the documentation so that a new user will be able to follow step by step and accomplish the task illustrated in the video",
+  documentation_with_charts: "Analyze this video to create comprehensive process documentation with workflow diagrams for a knowledge base article. Follow these requirements exactly:\n\n1. CONTENT REQUIREMENTS:\n   - Provide a detailed step-by-step explanation of the process shown\n   - Be extremely verbose and thorough - include all relevant details, context, and nuances\n   - Structure as a complete knowledge base article with clear sections\n   - Include overview, detailed steps, tips, and troubleshooting where applicable\n\n2. MERMAID DIAGRAM REQUIREMENTS:\n   - Create workflow diagrams using valid Mermaid syntax where helpful\n   - CRITICAL: Use only simple alphanumeric text in node descriptions and labels\n   - CRITICAL: No special characters like quotes brackets colons semicolons or symbols in node text\n   - CRITICAL: Use underscores instead of spaces in node IDs and labels\n   - CRITICAL: Keep all text simple to avoid syntax errors\n   - Example format: Start_Process --> Complete_Task --> End_Process\n   - Use flowchart format: graph TD or graph LR\n\n3. OUTPUT STRUCTURE:\n   - Title and overview section\n   - Prerequisites section if applicable\n   - Detailed step-by-step process\n   - Mermaid workflow diagram(s) showing the process flow\n   - Tips and best practices\n   - Troubleshooting common issues\n\nEnsure all Mermaid diagrams use simple text without special characters to prevent parsing errors.",
+  custom: "" // Empty for custom mode
+};
+
+const PromptSelector = ({ mode, onModeChange, prompt, onPromptChange, disabled }) => {
+  // Handle mode change and update prompt
+  const handleModeChange = (e) => {
+    const newMode = e.target.value;
+    onModeChange(newMode);
+    onPromptChange(PROMPTS[newMode]);
+  };
+
+  return (
+    <div className="mb-4">
+      <div className="mb-3">
+        <label htmlFor="mode-select" className="form-label">Select Mode:</label>
+        <select 
+          id="mode-select" 
+          className="form-select" 
+          value={mode} 
+          onChange={handleModeChange}
+          disabled={disabled}
+        >
+          <option value="meeting_summary">Meeting Summary</option>
+          <option value="process_documentation">Process/Tool Documentation</option>
+          <option value="documentation_with_charts">Process Tool Documentation with Charts</option>
+          <option value="custom">Custom Prompt</option>
+        </select>
+      </div>
+      
+      <div className="mb-3">
+        <label htmlFor="prompt-text" className="form-label">Prompt:</label>
+        <textarea 
+          id="prompt-text" 
+          className="form-control" 
+          rows="4" 
+          value={prompt} 
+          onChange={(e) => onPromptChange(e.target.value)}
+          placeholder="Enter or edit the prompt..."
+          disabled={disabled}
+        />
+      </div>
+    </div>
+  );
+};
+
+export default PromptSelector;
--- a/frontend/src/components/ResultDisplay.js
+++ b/frontend/src/components/ResultDisplay.js
@ -0,0 +1,401 @@
+import React, { useRef, useEffect, useState } from 'react';
+import showdown from 'showdown';
+import mermaid from 'mermaid';
+
+const ResultDisplay = ({ result, isLoading, uploadProgress = 0 }) => {
+  const resultRef = useRef(null);
+  const [htmlContent, setHtmlContent] = useState('');
+  
+  // Initialize mermaid
+  useEffect(() => {
+    mermaid.initialize({
+      startOnLoad: true,
+      theme: 'default',
+      securityLevel: 'loose'
+    });
+  }, []);
+  
+  // Convert markdown to HTML using showdown
+  useEffect(() => {
+    if (result) {
+      const converter = new showdown.Converter({
+        tables: true,
+        tasklists: true,
+        strikethrough: true,
+        ghCodeBlocks: true
+      });
+      const html = converter.makeHtml(result);
+      setHtmlContent(html);
+    }
+  }, [result]);
+  
+  // Render mermaid diagrams after HTML content is set
+  useEffect(() => {
+    if (htmlContent && resultRef.current) {
+      setTimeout(() => {
+        try {
+          // Find text containing "graph" or "sequenceDiagram" or "flowchart" outside of code blocks
+          const textNodes = Array.from(resultRef.current.childNodes)
+            .filter(node => node.nodeType === Node.TEXT_NODE || 
+                           (node.nodeType === Node.ELEMENT_NODE && 
+                            node.tagName !== 'PRE' && 
+                            node.tagName !== 'CODE'));
+          
+          // Standard code blocks with mermaid
+          const mermaidCodeBlocks = resultRef.current.querySelectorAll('pre code.language-mermaid');
+          
+          // Also try to find any pre/code with mermaid content when class wasn't set correctly
+          const potentialMermaidBlocks = Array.from(resultRef.current.querySelectorAll('pre'))
+            .filter(pre => {
+              const codeEl = pre.querySelector('code');
+              if (!codeEl) return false;
+              const content = codeEl.textContent.trim();
+              return content.startsWith('graph ') || 
+                     content.startsWith('sequenceDiagram') || 
+                     content.startsWith('flowchart ') || 
+                     content.includes('mermaid');
+            });
+          
+          // Process all known mermaid code blocks
+          const processBlock = (element, index) => {
+            const isPreElement = element.tagName === 'PRE';
+            const codeEl = isPreElement ? element.querySelector('code') : null;
+            const mermaidCode = codeEl ? codeEl.textContent : element.textContent;
+            
+            if (!mermaidCode.trim()) return;
+            
+            // Create a div to hold the rendered diagram
+            const diagramDiv = document.createElement('div');
+            diagramDiv.className = 'mermaid';
+            diagramDiv.id = `mermaid-diagram-${index}`;
+            diagramDiv.textContent = mermaidCode;
+            
+            // Store the original code as an attribute so we can access it later
+            diagramDiv.setAttribute('data-original-code', mermaidCode);
+            
+            // Replace the original element with the diagram div
+            if (isPreElement && element.parentElement) {
+              element.parentElement.replaceChild(diagramDiv, element);
+            } else if (element.parentElement) {
+              element.parentElement.replaceChild(diagramDiv, element);
+            }
+          };
+          
+          // Process standard mermaid blocks
+          mermaidCodeBlocks.forEach(processBlock);
+          
+          // Process potential mermaid blocks not correctly marked
+          potentialMermaidBlocks.forEach((block, index) => {
+            // Only process if it wasn't already processed as a standard mermaid block
+            if (!block.querySelector('code.language-mermaid')) {
+              processBlock(block, mermaidCodeBlocks.length + index);
+            }
+          });
+          
+          // Now run mermaid rendering
+          mermaid.run();
+        } catch (error) {
+          console.error('Error rendering mermaid diagrams:', error);
+        }
+      }, 100); // Small delay to ensure DOM is fully updated
+    }
+  }, [htmlContent]);
+  
+  // This function is no longer needed with the new approach
+
+  const copyToClipboard = () => {
+    if (!htmlContent) return;
+    
+    // Clone the current content with rendered diagrams
+    const contentToExport = resultRef.current.cloneNode(true);
+    document.body.appendChild(contentToExport);
+    
+    // Select the content
+    const range = document.createRange();
+    range.selectNode(contentToExport);
+    window.getSelection().removeAllRanges();
+    window.getSelection().addRange(range);
+    
+    // Copy the selected content as HTML
+    document.execCommand('copy');
+    
+    // Clean up
+    window.getSelection().removeAllRanges();
+    document.body.removeChild(contentToExport);
+    
+    alert('Formatted content copied to clipboard! You can now paste it into Word or other applications.');
+  };
+  
+  const [isPdfLoading, setIsPdfLoading] = useState(false);
+  
+  const downloadPdf = async () => {
+    if (!htmlContent) return;
+    
+    setIsPdfLoading(true);
+    
+    try {
+      // Wait for any final rendering
+      await new Promise(resolve => setTimeout(resolve, 1000));
+      
+      // Force re-render if necessary and wait
+      if (resultRef.current && resultRef.current.querySelectorAll('.mermaid:not(:empty) svg').length !== resultRef.current.querySelectorAll('.mermaid').length) {
+        // console.log("Forcing mermaid.run() for PDF export.");
+        try {
+          mermaid.run({ nodes: resultRef.current.querySelectorAll('.mermaid') });
+          await new Promise(resolve => setTimeout(resolve, 1500)); // Longer wait after explicit run
+        } catch(e) {
+          console.error("Error during mermaid.run() for PDF:", e);
+        }
+      }
+
+      const diagramPngs = {}; // Store base64 PNGs keyed by diagram ID
+      const textDiagrams = {}; // Still useful for alt text or if PNG fails
+      
+      // It's crucial to work on the live DOM state that mermaid.js has modified.
+      // Clone the resultRef.current to avoid altering the displayed content if we assign IDs.
+      const contentToExport = resultRef.current.cloneNode(true);
+
+      const mermaidDivs = contentToExport.querySelectorAll('.mermaid');
+      // console.log(`Found ${mermaidDivs.length} .mermaid elements for PDF export.`);
+
+      // Create an array of promises for all conversions
+      const conversionPromises = Array.from(mermaidDivs).map(async (div, index) => {
+        let diagramId = div.id;
+        if (!diagramId) {
+          // If a div.mermaid doesn't have an ID, assign one.
+          diagramId = `mermaid-export-${index}`;
+          div.id = diagramId; // Assign ID to the div in our cloned content
+          // console.log(`Assigned dynamic ID ${diagramId} to a .mermaid div for export.`);
+        }
+
+        const originalCode = div.getAttribute('data-original-code') || 
+                           (div.firstChild && div.firstChild.nodeType === Node.TEXT_NODE ? div.firstChild.textContent.trim() : div.textContent.trim());
+        
+        if (originalCode) {
+          textDiagrams[diagramId] = originalCode;
+        } else {
+          console.warn(`No original code found for .mermaid div with ID: ${diagramId}`);
+        }
+
+        const svgElement = div.querySelector('svg');
+        if (svgElement) {
+          try {
+            const svgString = new XMLSerializer().serializeToString(svgElement);
+            const svgDataUrl = `data:image/svg+xml;charset=utf-8,${encodeURIComponent(svgString)}`;
+
+            const image = new Image();
+            
+            // Create a promise for each image load and canvas conversion
+            await new Promise((resolve, reject) => {
+              image.onload = () => {
+                const canvas = document.createElement('canvas');
+                // --- Determine canvas size ---
+                // Option 1: Use SVG's explicit width/height if they are pixel values
+                let svgWidth = parseFloat(svgElement.getAttribute('width'));
+                let svgHeight = parseFloat(svgElement.getAttribute('height'));
+
+                // Option 2: If no explicit width/height, use viewBox (more robust)
+                if (isNaN(svgWidth) || isNaN(svgHeight) || svgWidth <= 0 || svgHeight <= 0) {
+                  const viewBox = svgElement.getAttribute('viewBox');
+                  if (viewBox) {
+                    const parts = viewBox.split(' ');
+                    svgWidth = parseFloat(parts[2]);
+                    svgHeight = parseFloat(parts[3]);
+                  }
+                }
+                
+                // Fallback if dimensions still not found
+                if (isNaN(svgWidth) || isNaN(svgHeight) || svgWidth <= 0 || svgHeight <= 0) {
+                  console.warn(`Could not determine dimensions for SVG ${diagramId}, using fallback.`);
+                  svgWidth = 600; // Default/fallback width
+                  svgHeight = 400; // Default/fallback height
+                }
+
+                // Apply a scale factor for better resolution
+                const scaleFactor = 2; // 2x resolution
+                canvas.width = svgWidth * scaleFactor;
+                canvas.height = svgHeight * scaleFactor;
+
+                const ctx = canvas.getContext('2d');
+                if (!ctx) {
+                  console.error('Could not get 2D context from canvas');
+                  reject(new Error('Canvas 2D context unavailable'));
+                  return;
+                }
+                
+                // Fill background with white to ensure opaque PNG
+                ctx.fillStyle = 'white';
+                ctx.fillRect(0, 0, canvas.width, canvas.height);
+
+                ctx.drawImage(image, 0, 0, canvas.width, canvas.height);
+                
+                diagramPngs[diagramId] = canvas.toDataURL('image/png');
+                // console.log(`Converted SVG ${diagramId} to PNG (length: ${diagramPngs[diagramId].length})`);
+                resolve();
+              };
+              image.onerror = (err) => {
+                console.error(`Error loading SVG into Image object for ${diagramId}:`, err);
+                reject(new Error(`Image loading error for SVG ${diagramId}`));
+              };
+              image.src = svgDataUrl;
+            });
+          } catch (e) {
+            console.error(`Error converting SVG ${diagramId} to PNG:`, e);
+            // No PNG for this diagram if conversion fails
+          }
+        } else {
+          console.warn(`No SVG element found in .mermaid div ID: ${diagramId}. Original code snippet: ${(originalCode || '').substring(0,30)}`);
+        }
+      });
+
+      // Wait for all SVG to PNG conversions to complete
+      await Promise.all(conversionPromises);
+
+      // The HTML to send is the innerHTML of our (potentially ID-modified) cloned container.
+      const htmlToSend = contentToExport.innerHTML;
+
+      // Debug logging commented out
+      // console.log("Sending to backend for PDF generation:", {
+      //   htmlLength: htmlToSend.length,
+      //   numTextDiagrams: Object.keys(textDiagrams).length,
+      //   numDiagramPngs: Object.keys(diagramPngs).length,
+      // });
+      // if (Object.keys(diagramPngs).length > 0) {
+      //   console.log("Diagram PNG IDs:", Object.keys(diagramPngs));
+      // }
+      
+      // Debug logging commented out
+      // console.log("HTML CONTENT START -------------------");
+      // console.log(htmlToSend);
+      // console.log("HTML CONTENT END ---------------------");
+
+      // Make API request to generate PDF
+      const authApiClient = require('../auth/authApiClient').authApiClient;
+      const response = await authApiClient.post(
+        'https://ai-sandbox.oliver.solutions/video_query_back/api/generate-pdf',
+        {
+          html: htmlToSend,
+          textDiagrams: textDiagrams,
+          diagramPngs: diagramPngs   // Send the base64 PNGs instead of SVGs
+        },
+        {
+          headers: {
+            'Content-Type': 'application/json'
+          }
+        }
+      );
+      
+      if (response.data.success) {
+        // Convert base64 PDF to blob
+        const pdfData = atob(response.data.pdf);
+        const pdfBytes = new Uint8Array(pdfData.length);
+        for (let i = 0; i < pdfData.length; i++) {
+          pdfBytes[i] = pdfData.charCodeAt(i);
+        }
+        const pdfBlob = new Blob([pdfBytes], { type: 'application/pdf' });
+        const pdfUrl = URL.createObjectURL(pdfBlob);
+        
+        // Create download link and trigger download
+        const downloadLink = document.createElement('a');
+        downloadLink.href = pdfUrl;
+        downloadLink.download = response.data.filename || 'video_query_result.pdf';
+        document.body.appendChild(downloadLink);
+        downloadLink.click();
+        
+        // Clean up
+        document.body.removeChild(downloadLink);
+        setTimeout(() => {
+          URL.revokeObjectURL(pdfUrl);
+        }, 100);
+      } else {
+        throw new Error(response.data.message || 'PDF generation failed');
+      }
+    } catch (error) {
+      console.error('Error downloading PDF:', error);
+      alert('Failed to generate PDF. Please try again later.');
+    } finally {
+      setIsPdfLoading(false);
+    }
+  };
+  
+  if (isLoading) {
+    const isUploading = uploadProgress < 100;
+    return (
+      <div className="processing-spinner">
+        <div className="spinner-border text-primary mb-3" role="status">
+          <span className="visually-hidden">Loading...</span>
+        </div>
+        <p>
+          {isUploading 
+            ? `Uploading video: ${uploadProgress}% complete...` 
+            : 'Processing video... This may take several minutes depending on the video length.'}
+        </p>
+        <div className="progress w-75 mb-3">
+          <div 
+            className="progress-bar progress-bar-striped progress-bar-animated" 
+            role="progressbar" 
+            style={{ width: isUploading ? `${uploadProgress}%` : '100%' }}
+            aria-valuenow={isUploading ? uploadProgress : 100} 
+            aria-valuemin="0" 
+            aria-valuemax="100"
+          ></div>
+        </div>
+        {!isUploading && (
+          <div className="alert alert-info">
+            <small>
+              <strong>Note:</strong> Your video has been uploaded successfully and is being processed by Gemini AI. 
+              This may take several minutes for longer videos (up to 55 minutes supported).
+            </small>
+          </div>
+        )}
+      </div>
+    );
+  }
+  
+  if (!result) {
+    return null;
+  }
+  
+  return (
+    <div>
+      <div className="d-flex justify-content-between align-items-center mb-3">
+        <h3>Result</h3>
+        <div className="d-flex gap-2">
+          <button 
+            className="btn btn-primary btn-sm" 
+            onClick={copyToClipboard}
+          >
+            <svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="currentColor" className="bi bi-clipboard me-1" viewBox="0 0 16 16">
+              <path d="M4 1.5H3a2 2 0 0 0-2 2V14a2 2 0 0 0 2 2h10a2 2 0 0 0 2-2V3.5a2 2 0 0 0-2-2h-1v1h1a1 1 0 0 1 1 1V14a1 1 0 0 1-1 1H3a1 1 0 0 1-1-1V3.5a1 1 0 0 1 1-1h1v-1z"/>
+              <path d="M9.5 1a.5.5 0 0 1 .5.5v1a.5.5 0 0 1-.5.5h-3a.5.5 0 0 1-.5-.5v-1a.5.5 0 0 1 .5-.5h3zm-3-1A1.5 1.5 0 0 0 5 1.5v1A1.5 1.5 0 0 0 6.5 4h3A1.5 1.5 0 0 0 11 2.5v-1A1.5 1.5 0 0 0 9.5 0h-3z"/>
+            </svg>
+            Copy Formatted
+          </button>
+          <button 
+            className="btn btn-danger btn-sm" 
+            onClick={downloadPdf}
+            disabled={isPdfLoading}
+          >
+            <svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="currentColor" className="bi bi-file-pdf me-1" viewBox="0 0 16 16">
+              <path d="M14 14V4.5L9.5 0H4a2 2 0 0 0-2 2v12a2 2 0 0 0 2 2h8a2 2 0 0 0 2-2zM9.5 3A1.5 1.5 0 0 0 11 4.5h2V14a1 1 0 0 1-1 1H4a1 1 0 0 1-1-1V2a1 1 0 0 1 1-1h5.5v2z"/>
+              <path d="M4.603 14.087a.81.81 0 0 1-.438-.42c-.195-.388-.13-.776.08-1.102.198-.307.526-.568.897-.787a7.68 7.68 0 0 1 1.482-.645 19.697 19.697 0 0 0 1.062-2.227 7.269 7.269 0 0 1-.43-1.295c-.086-.4-.119-.796-.046-1.136.075-.354.274-.672.65-.823.192-.077.4-.12.602-.077a.7.7 0 0 1 .477.365c.088.164.12.356.127.538.007.188-.012.396-.047.614-.084.51-.27 1.134-.52 1.794a10.954 10.954 0 0 0 .98 1.686 5.753 5.753 0 0 1 1.334.05c.364.066.734.195.96.465.12.144.193.32.2.518.007.192-.047.382-.138.563a1.04 1.04 0 0 1-.354.416.856.856 0 0 1-.51.138c-.331-.014-.654-.196-.933-.417a5.712 5.712 0 0 1-.911-.95 11.651 11.651 0 0 0-1.997.406 11.307 11.307 0 0 1-1.02 1.51c-.292.35-.609.656-.927.787a.793.793 0 0 1-.58.029zm1.379-1.901c-.166.076-.32.156-.459.238-.328.194-.541.383-.647.547-.094.145-.096.25-.04.361.01.022.02.036.026.044a.266.266 0 0 0 .035-.012c.137-.056.355-.235.635-.572a8.18 8.18 0 0 0 .45-.606zm1.64-1.33a12.71 12.71 0 0 1 1.01-.193 11.744 11.744 0 0 1-.51-.858 20.801 20.801 0 0 1-.5 1.05zm2.446.45c.15.163.296.3.435.41.24.19.407.253.498.256a.107.107 0 0 0 .07-.015.307.307 0 0 0 .094-.125.436.436 0 0 0 .059-.2.095.095 0 0 0-.026-.063c-.052-.062-.2-.152-.518-.209a3.876 3.876 0 0 0-.612-.053zM8.078 7.8a6.7 6.7 0 0 0 .2-.828c.031-.188.043-.343.038-.465a.613.613 0 0 0-.032-.198.517.517 0 0 0-.145.04c-.087.035-.158.106-.196.283-.04.192-.03.469.046.822.024.111.054.227.09.346z"/>
+            </svg>
+            {isPdfLoading ? 'Generating...' : 'Download PDF'}
+          </button>
+        </div>
+      </div>
+      
+      <div 
+        className="result-container" 
+        ref={resultRef}
+        dangerouslySetInnerHTML={{ __html: htmlContent }}
+      />
+      
+      <div className="copy-instruction mt-3">
+        <p className="mb-0"><strong>Tip:</strong> Click "Copy Formatted" to copy the content in a format suitable for pasting into Word or other document editors.</p>
+      </div>
+    </div>
+  );
+};
+
+export default ResultDisplay;
--- a/frontend/src/components/VideoUpload.js
+++ b/frontend/src/components/VideoUpload.js
@ -0,0 +1,75 @@
+import React, { useCallback, useState } from 'react';
+import { useDropzone } from 'react-dropzone';
+
+const MAX_FILE_SIZE = 5 * 1024 * 1024 * 1024; // 5GB
+
+const VideoUpload = ({ onVideoSelect }) => {
+  const [error, setError] = useState('');
+  
+  const onDrop = useCallback((acceptedFiles) => {
+    setError('');
+    
+    // Handle the uploaded files
+    if (acceptedFiles && acceptedFiles.length > 0) {
+      const file = acceptedFiles[0];
+      
+      // Check file size
+      if (file.size > MAX_FILE_SIZE) {
+        setError(`File is too large. Maximum size is 5GB.`);
+        return;
+      }
+      
+      // Check file type
+      const validTypes = ['video/mp4', 'video/avi', 'video/quicktime', 'video/x-ms-wmv', 'video/x-matroska', 'video/webm'];
+      if (!validTypes.includes(file.type)) {
+        setError('Please upload a valid video file (MP4, AVI, MOV, WMV, MKV, WEBM)');
+        return;
+      }
+      
+      // Pass the file to parent component
+      onVideoSelect(file);
+    }
+  }, [onVideoSelect]);
+  
+  const { getRootProps, getInputProps, isDragActive } = useDropzone({
+    onDrop,
+    accept: {
+      'video/*': ['.mp4', '.avi', '.mov', '.wmv', '.mkv', '.webm']
+    },
+    maxFiles: 1
+  });
+  
+  return (
+    <div className="mb-4">
+      <div 
+        {...getRootProps()} 
+        className={`dropzone ${isDragActive ? 'active' : ''}`}
+        style={{
+          borderColor: error ? '#dc3545' : isDragActive ? '#0d6efd' : '#ced4da'
+        }}
+      >
+        <input {...getInputProps()} />
+        <div className="mb-3">
+          <svg xmlns="http://www.w3.org/2000/svg" width="40" height="40" fill="currentColor" className="bi bi-camera-video" viewBox="0 0 16 16">
+            <path fillRule="evenodd" d="M0 5a2 2 0 0 1 2-2h7.5a2 2 0 0 1 1.983 1.738l3.11-1.382A1 1 0 0 1 16 4.269v7.462a1 1 0 0 1-1.406.913l-3.111-1.382A2 2 0 0 1 9.5 13H2a2 2 0 0 1-2-2V5zm11.5 5.175 3.5 1.556V4.269l-3.5 1.556v4.35zM2 4a1 1 0 0 0-1 1v6a1 1 0 0 0 1 1h7.5a1 1 0 0 0 1-1V5a1 1 0 0 0-1-1H2z"/>
+          </svg>
+        </div>
+        <p>
+          {isDragActive ? 
+          'Drop the video here...' : 
+          'Drag and drop a video file here, or click to select a file'}
+        </p>
+        <p className="text-muted small">Supported formats: MP4, AVI, MOV, WMV, MKV, WEBM (max 5GB)</p>
+      </div>
+      
+      {error && <div className="alert alert-danger">{error}</div>}
+      
+      <div className="notice">
+        <strong>Important:</strong> The Gemini AI model can only process videos up to 55 minutes in length.
+        Longer videos will fail to process.
+      </div>
+    </div>
+  );
+};
+
+export default VideoUpload;
--- a/frontend/src/index.css
+++ b/frontend/src/index.css
@ -0,0 +1,213 @@
+body {
+  margin: 0;
+  font-family: 'Montserrat', -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Roboto', 'Oxygen',
+    'Ubuntu', 'Cantarell', 'Fira Sans', 'Droid Sans', 'Helvetica Neue',
+    sans-serif;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+  padding: 20px;
+}
+
+code {
+  /* Preserve monospace font for code blocks */
+  font-family: source-code-pro, Menlo, Monaco, Consolas, 'Courier New',
+    monospace;
+  background-color: #f5f5f5;
+  padding: 2px 5px;
+  border-radius: 3px;
+  font-size: 0.9em;
+  letter-spacing: 0; /* Reset letter spacing for code */
+}
+
+pre {
+  background-color: #f5f5f5;
+  padding: 15px;
+  border-radius: 5px;
+  overflow-x: auto;
+}
+
+pre code {
+  background-color: transparent;
+  padding: 0;
+}
+
+.dropzone {
+  border: 2px dashed #ccc;
+  border-radius: 5px;
+  padding: 30px;
+  text-align: center;
+  cursor: pointer;
+  margin-bottom: 20px;
+  transition: all 0.3s ease;
+}
+
+.dropzone:hover, .dropzone.active {
+  border-color: #0d6efd;
+  background-color: rgba(13, 110, 253, 0.1);
+}
+
+.result-container {
+  padding: 20px;
+  border: 1px solid #dee2e6;
+  border-radius: 0.25rem;
+  margin-top: 20px;
+  max-height: 600px;
+  overflow-y: auto;
+  line-height: 1.7;
+  font-family: 'Montserrat', -apple-system, BlinkMacSystemFont, sans-serif;
+  letter-spacing: 0.1px;
+}
+
+/* Mermaid diagram styling */
+.mermaid {
+  text-align: center;
+  margin: 20px 0;
+  overflow-x: auto;
+}
+
+.mermaid svg {
+  max-width: 100%;
+  height: auto;
+  margin: 0 auto;
+  display: block;
+}
+
+/* Universal Font Styling */
+h1, h2, h3, h4, h5, h6, p, div, span, button, input, textarea, select, label, a {
+  font-family: 'Montserrat', -apple-system, BlinkMacSystemFont, sans-serif;
+}
+
+/* Adjust font weights for better readability with Montserrat */
+h1, h2, h3 {
+  font-weight: 600;
+  letter-spacing: -0.5px;
+}
+
+h4, h5, h6 {
+  font-weight: 500;
+  letter-spacing: -0.25px;
+}
+
+p, div, span, label {
+  font-weight: 400;
+  letter-spacing: 0.1px;
+}
+
+button, input[type="button"], input[type="submit"] {
+  font-weight: 500;
+  letter-spacing: 0.3px;
+}
+
+/* Bootstrap overrides for Montserrat */
+.btn {
+  font-family: 'Montserrat', -apple-system, BlinkMacSystemFont, sans-serif !important;
+  font-weight: 500 !important;
+  letter-spacing: 0.3px;
+}
+
+.form-control, .form-select {
+  font-family: 'Montserrat', -apple-system, BlinkMacSystemFont, sans-serif !important;
+  font-weight: 400;
+}
+
+.alert, .card, .modal-content {
+  font-family: 'Montserrat', -apple-system, BlinkMacSystemFont, sans-serif !important;
+}
+
+/* Styling for markdown rendered content */
+.result-container h1, 
+.result-container h2, 
+.result-container h3, 
+.result-container h4, 
+.result-container h5, 
+.result-container h6 {
+  margin-top: 1.5rem;
+  margin-bottom: 1rem;
+  font-weight: 600;
+  line-height: 1.25;
+  font-family: 'Montserrat', -apple-system, BlinkMacSystemFont, sans-serif;
+}
+
+.result-container h1 {
+  font-size: 2em;
+  border-bottom: 1px solid #eaecef;
+  padding-bottom: 0.3em;
+}
+
+.result-container h2 {
+  font-size: 1.5em;
+  border-bottom: 1px solid #eaecef;
+  padding-bottom: 0.3em;
+}
+
+.result-container p {
+  margin-top: 0;
+  margin-bottom: 16px;
+}
+
+.result-container ul, 
+.result-container ol {
+  padding-left: 2em;
+  margin-bottom: 16px;
+}
+
+.result-container blockquote {
+  padding: 0 1em;
+  color: #6a737d;
+  border-left: 0.25em solid #dfe2e5;
+  margin: 0 0 16px 0;
+}
+
+.result-container table {
+  display: block;
+  width: 100%;
+  overflow: auto;
+  margin-bottom: 16px;
+  border-spacing: 0;
+  border-collapse: collapse;
+}
+
+.result-container table th,
+.result-container table td {
+  padding: 6px 13px;
+  border: 1px solid #dfe2e5;
+}
+
+.result-container table tr {
+  background-color: #fff;
+  border-top: 1px solid #c6cbd1;
+}
+
+.result-container table tr:nth-child(2n) {
+  background-color: #f6f8fa;
+}
+
+.notice {
+  background-color: #fff3cd;
+  color: #856404;
+  padding: 15px;
+  border-radius: 5px;
+  margin-bottom: 20px;
+  border-left: 5px solid #ffeeba;
+}
+
+.processing-spinner {
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  justify-content: center;
+  margin: 40px 0;
+}
+
+/* Progress Bar */
+.progress {
+  height: 10px;
+}
+
+.copy-instruction {
+  background-color: #f8f9fa;
+  padding: 15px;
+  border-radius: 5px;
+  margin-top: 15px;
+  font-size: 0.9rem;
+}
--- a/frontend/src/index.js
+++ b/frontend/src/index.js
@ -0,0 +1,15 @@
+import React from 'react';
+import { createRoot } from 'react-dom/client';
+import 'bootstrap/dist/css/bootstrap.min.css';
+import './index.css';
+import App from './App';
+import { AuthProvider } from './auth/AuthProvider';
+
+const root = createRoot(document.getElementById('root'));
+root.render(
+  <React.StrictMode>
+    <AuthProvider>
+      <App />
+    </AuthProvider>
+  </React.StrictMode>
+);
--- a/frontend/src/utils/chunkedUploader.js
+++ b/frontend/src/utils/chunkedUploader.js
@ -0,0 +1,210 @@
+/**
+ * Handles chunked file uploads for large files
+ */
+
+import { authApiClient } from '../auth/authApiClient';
+
+const CHUNK_SIZE = 2 * 1024 * 1024; // 2MB chunks
+const BACKEND_URL = 'https://ai-sandbox.oliver.solutions/video_query_back';
+
+class ChunkedUploader {
+  constructor(file, onProgress) {
+    this.file = file;
+    this.onProgress = onProgress || (() => {});
+    this.uploadId = null;
+    this.aborted = false;
+  }
+
+  /**
+   * Initialize upload session
+   */
+  async initUpload() {
+    try {
+      const response = await authApiClient.post(
+        `${BACKEND_URL}/api/init-upload`, 
+        {
+          filename: this.file.name,
+          size: this.file.size,
+        },
+        {
+          headers: {
+            'Content-Type': 'application/json'
+          }
+        }
+      );
+      
+      if (!response.data.success) {
+        throw new Error(response.data.message || 'Failed to initialize upload');
+      }
+      
+      this.uploadId = response.data.upload_id;
+      console.log(`Upload initialized with ID: ${this.uploadId}`);
+      return this.uploadId;
+    } catch (err) {
+      console.error('Error initializing upload:', err);
+      
+      // Check for auth error (The auth client should handle the redirect)
+      if (err.response && err.response.status === 401) {
+        this.aborted = true;
+        throw new Error('Authentication error: Your session expired. Please log in again.');
+      }
+      
+      throw err;
+    }
+  }
+
+  /**
+   * Upload a chunk of the file
+   */
+  async uploadChunk(chunk, chunkNumber) {
+    if (this.aborted) {
+      throw new Error('Upload aborted');
+    }
+    
+    const formData = new FormData();
+    formData.append('chunk', chunk);
+    formData.append('chunk_number', chunkNumber);
+    
+    try {
+      const response = await authApiClient.post(
+        `${BACKEND_URL}/api/upload-chunk/${this.uploadId}`,
+        formData,
+        {
+          headers: {
+            'Content-Type': 'multipart/form-data'
+          }
+        }
+      );
+      
+      if (!response.data.success) {
+        throw new Error(response.data.message || 'Failed to upload chunk');
+      }
+      
+      return response.data;
+    } catch (err) {
+      console.error(`Error uploading chunk ${chunkNumber}:`, err);
+      
+      // Check for auth error (The auth client should handle the redirect)
+      if (err.response && err.response.status === 401) {
+        this.aborted = true; // Mark upload as aborted so other chunks don't continue
+        throw new Error('Authentication error: Your session expired during the upload. Please log in again.');
+      }
+      
+      throw err;
+    }
+  }
+
+  /**
+   * Complete the upload process
+   */
+  async completeUpload() {
+    try {
+      const response = await authApiClient.post(
+        `${BACKEND_URL}/api/complete-upload/${this.uploadId}`,
+        {},
+        {
+          headers: {
+            'Content-Type': 'application/json'
+          }
+        }
+      );
+      
+      if (!response.data.success) {
+        throw new Error(response.data.message || 'Failed to complete upload');
+      }
+      
+      return response.data;
+    } catch (err) {
+      console.error('Error completing upload:', err);
+      
+      // Check for auth error (The auth client should handle the redirect)
+      if (err.response && err.response.status === 401) {
+        this.aborted = true;
+        throw new Error('Authentication error: Your session expired. Please log in again.');
+      }
+      
+      throw err;
+    }
+  }
+
+  /**
+   * Cancel the upload
+   */
+  async cancelUpload() {
+    this.aborted = true;
+    
+    if (!this.uploadId) {
+      return { success: true, message: 'No active upload to cancel' };
+    }
+    
+    try {
+      const response = await authApiClient.post(
+        `${BACKEND_URL}/api/cancel-upload/${this.uploadId}`,
+        {},
+        {
+          headers: {
+            'Content-Type': 'application/json'
+          }
+        }
+      );
+      
+      return response.data;
+    } catch (err) {
+      console.error('Error canceling upload:', err);
+      throw err;
+    }
+  }
+
+  /**
+   * Upload the entire file in chunks
+   */
+  async uploadFile() {
+    try {
+      // Initialize the upload
+      await this.initUpload();
+      
+      const totalChunks = Math.ceil(this.file.size / CHUNK_SIZE);
+      console.log(`Uploading file in ${totalChunks} chunks...`);
+      
+      // Upload each chunk
+      for (let i = 0; i < totalChunks; i++) {
+        if (this.aborted) {
+          throw new Error('Upload aborted');
+        }
+        
+        const start = i * CHUNK_SIZE;
+        const end = Math.min(this.file.size, start + CHUNK_SIZE);
+        const chunk = this.file.slice(start, end);
+        
+        const result = await this.uploadChunk(chunk, i);
+        
+        // Report progress
+        this.onProgress(result.progress);
+        
+        console.log(`Chunk ${i+1}/${totalChunks} uploaded (${result.progress}%)`);
+      }
+      
+      // Complete the upload
+      const result = await this.completeUpload();
+      console.log('Upload completed successfully:', result);
+      
+      // Final progress update
+      this.onProgress(100);
+      
+      return result;
+    } catch (err) {
+      console.error('Upload failed:', err);
+      // Try to cancel/cleanup on failure
+      try {
+        if (this.uploadId) {
+          await this.cancelUpload();
+        }
+      } catch (cancelErr) {
+        console.error('Error during upload cleanup:', cancelErr);
+      }
+      throw err;
+    }
+  }
+}
+
+export default ChunkedUploader;
--- a/quick_extract.sh
+++ b/quick_extract.sh
@ -0,0 +1,17 @@
+#!/bin/bash
+
+# Quick one-liner to extract user emails from logs
+# Usage: ./quick_extract.sh [service_name]
+
+SERVICE_NAME="${1:-veo-video-generator}"
+
+echo "Extracting user emails from $SERVICE_NAME logs..."
+echo "user_email,prompt" > quick_users.csv
+
+journalctl -u "$SERVICE_NAME" --no-pager | \
+    grep "Raw JSON data received:" | \
+    sed -n "s/.*Raw JSON data received: \(.*\)/\1/p" | \
+    jq -r 'select(.user_email != null) | [.user_email, .prompt] | @csv' 2>/dev/null >> quick_users.csv
+
+echo "Results saved to quick_users.csv"
+echo "Total records: $(($(wc -l < quick_users.csv) - 1))"
--- a/requirements.txt
+++ b/requirements.txt
@ -0,0 +1,43 @@
+annotated-types==0.7.0
+blinker==1.9.0
+cachetools==5.5.2
+certifi==2025.4.26
+charset-normalizer==3.4.2
+click==8.2.0
+Flask==3.1.0
+google-ai-generativelanguage==0.6.15
+google-api-core==2.25.0rc0
+google-api-python-client==2.169.0
+google-auth==2.40.0
+google-auth-httplib2==0.2.0
+google-generativeai==0.8.5
+googleapis-common-protos==1.70.0
+grpcio==1.71.0
+grpcio-status==1.71.0
+h11==0.16.0
+h2==4.2.0
+hpack==4.1.0
+httplib2==0.22.0
+Hypercorn==0.17.3
+hyperframe==6.1.0
+idna==3.10
+itsdangerous==2.2.0
+Jinja2==3.1.6
+MarkupSafe==3.0.2
+priority==2.0.0
+proto-plus==1.26.1
+protobuf==5.29.4
+pyasn1==0.6.1
+pyasn1_modules==0.4.2
+pydantic==2.11.4
+pydantic_core==2.33.2
+pyparsing==3.2.3
+requests==2.32.3
+rsa==4.9.1
+tqdm==4.67.1
+typing-inspection==0.4.0
+typing_extensions==4.13.2
+uritemplate==4.1.1
+urllib3==2.4.0
+Werkzeug==3.1.3
+wsproto==1.2.0
--- a/restart.sh
+++ b/restart.sh
@ -0,0 +1,26 @@
+#!/bin/bash
+
+echo "Stopping any existing servers..."
+pkill -f "python.*run.py" || true
+pkill -f "test_endpoint.py" || true
+
+# Make the script executable
+chmod +x ./backend/run.py
+chmod +x ./backend/test_endpoint.py
+
+# Start the test endpoint in a separate terminal window
+echo "Starting test endpoint server..."
+cd backend
+python test_endpoint.py &
+cd ..
+
+echo "Waiting for test server to start..."
+sleep 2
+
+echo "You can now restart the React development server with:"
+echo "cd frontend && npm start"
+
+echo "To test the full application with large files, run:"
+echo "cd backend && python run.py"
+
+echo "Done!"
--- a/video_query.py
+++ b/video_query.py
@ -0,0 +1,141 @@
+import google.generativeai as genai
+import mimetypes
+import time
+import os
+import argparse
+
+# --- CONFIGURATION ---
+# !!! REPLACE WITH YOUR ACTUAL API KEY !!!
+# It's best practice to set this as an environment variable
+# e.g., export GOOGLE_API_KEY="YOUR_API_KEY"
+# If you do that, the next line can be removed.
+API_KEY = "AIzaSyBF3Ia1nVS4PLuLpWt-85ct_heJ7FrlvkQ" # <--- REPLACE THIS!
+
+# Default prompt if none is provided
+DEFAULT_PROMPT = "What is happening in this video? Describe the main objects and actions."
+
+# File size in bytes for chunked upload (10MB)
+CHUNKED_UPLOAD_CUTOFF = 10 * 1024 * 1024
+
+# --- END CONFIGURATION ---
+
+def upload_video_and_query(api_key, video_path, prompt):
+    """
+    Uploads a video to Gemini, queries it with a prompt, and prints the response.
+    """
+    if api_key == "YOUR_GEMINI_API_KEY":
+        print("ERROR: Please replace 'YOUR_GEMINI_API_KEY' with your actual API key.")
+        return
+    if not os.path.exists(video_path):
+        print(f"ERROR: Video file not found at '{video_path}'")
+        return
+
+    try:
+        # Configure the Gemini client
+        genai.configure(api_key=api_key)
+
+        print(f"Attempting to upload '{video_path}'...")
+
+        # 1. Upload the video file
+        file_size = os.path.getsize(video_path)
+        
+        # Log file size relative to threshold (for informational purposes only)
+        if file_size > CHUNKED_UPLOAD_CUTOFF:
+            print(f"File size ({file_size} bytes) exceeds {CHUNKED_UPLOAD_CUTOFF} bytes threshold...")
+        else:
+            print(f"File size ({file_size} bytes) is below threshold...")
+        
+        # Upload the file - all uploads use same method, but we always log the threshold
+        video_file = genai.upload_file(
+            path=video_path,
+            display_name=os.path.basename(video_path)
+        )
+            
+        print(f"Successfully uploaded file: {video_file.display_name} as {video_file.uri}")
+        print(f"File state: {video_file.state.name}") # Should ideally be ACTIVE
+
+        # Ensure the file is ready for use (though upload_file usually handles this)
+        # This is a safety check loop.
+        while video_file.state.name == "PROCESSING":
+            print("File is still processing. Waiting...")
+            time.sleep(5) # Wait for 5 seconds before checking again
+            video_file = genai.get_file(name=video_file.name) # Re-fetch file state
+            print(f"File state: {video_file.state.name}")
+
+        if video_file.state.name != "ACTIVE":
+            print(f"Error: File '{video_file.name}' did not become active. Current state: {video_file.state.name}")
+            print("It might still be processing, or an error occurred during upload/processing.")
+            print("Please check the file status in Google AI Studio or try again later.")
+            return
+
+        # 2. Prepare the prompt for the vision model
+        # We need the MIME type for the video file.
+        mime_type, _ = mimetypes.guess_type(video_path)
+        if not mime_type:
+            print(f"Could not determine MIME type for {video_path}. Assuming 'video/mp4'.")
+            mime_type = "video/mp4" # Fallback, adjust if needed
+
+        # Create the content parts: the text prompt and the video file reference
+        # In newer versions of the API, we use a dictionary format for content
+        prompt_parts = [
+            {"text": prompt},  # Your text prompt
+            {"file_data": {
+                "file_uri": video_file.uri,
+                "mime_type": mime_type
+            }}
+        ]
+
+        # 3. Initialize the Generative Model (gemini-2.5-pro for video)
+        model = genai.GenerativeModel(model_name="gemini-2.5-pro")
+
+        print("\nSending prompt to Gemini 2.5 Pro model...")
+        # 4. Generate content
+        response = model.generate_content(prompt_parts)
+
+        # 5. Print the response
+        print("\n--- Gemini Response ---")
+        if response.parts:
+            for part in response.parts:
+                if hasattr(part, 'text'):
+                    print(part.text)
+        else:
+            print("No content parts in the response. Full response:")
+            print(response)
+            if hasattr(response, 'prompt_feedback') and response.prompt_feedback:
+                print(f"\nPrompt Feedback: {response.prompt_feedback}")
+
+
+    except Exception as e:
+        print(f"\nAn error occurred: {e}")
+        import traceback
+        traceback.print_exc()
+
+    finally:
+        # Optional: Delete the file from Gemini storage if you no longer need it.
+        # Be careful with this in a real application.
+        # if 'video_file' in locals() and video_file:
+        #     try:
+        #         print(f"\nAttempting to delete file: {video_file.name}")
+        #         genai.delete_file(name=video_file.name)
+        #         print("File deleted successfully.")
+        #     except Exception as e_del:
+        #         print(f"Error deleting file: {e_del}")
+        pass
+
+
+if __name__ == "__main__":
+    # Setup argument parser
+    parser = argparse.ArgumentParser(description="Upload a video to Gemini and query it")
+    parser.add_argument("video_path", help="Path to the video file")
+    parser.add_argument("--prompt", "-p", help="Query prompt for the video", default=DEFAULT_PROMPT)
+    args = parser.parse_args()
+    
+    # Try to get API key from environment variable first
+    env_api_key = os.getenv("GOOGLE_API_KEY")
+    if env_api_key:
+        print("Using API key from GOOGLE_API_KEY environment variable.")
+        current_api_key = env_api_key
+    else:
+        current_api_key = API_KEY # Use the one defined in the script
+
+    upload_video_and_query(current_api_key, args.video_path, args.prompt)
				`@ -0,0 +1 @@`
				`GOOGLE_API_KEY=AIzaSyBF3Ia1nVS4PLuLpWt-85ct_heJ7FrlvkQ`