This is the backend API for the SkoolMe course generation system. It provides file upload, analysis, and content processing capabilities.

• File Upload: Support for documents (.txt, .pdf, .docx, .png, .jpg, .jpeg, .bmp) and audio files (.mp3, .wav, .m4a)
• Document Processing: OCR and text extraction from various document formats
• Audio Processing: Speech-to-text transcription for audio files
• Progress Tracking: Real-time progress updates for file analysis
• File Size Validation: 100MB limit for documents, 50MB for audio files
• Google Cloud Integration: Uses Google Cloud Speech-to-Text and Vision APIs

cd backend
pip install -r requirements.txt

Windows:

• Install Tesseract OCR
• Add Tesseract to your system PATH
• Install Poppler for PDF processing

macOS:

brew install tesseract poppler

Linux (Ubuntu/Debian):

sudo apt-get install tesseract-ocr poppler-utils

1. Create a Google Cloud project
2. Enable the following APIs:
   • Speech-to-Text API
   • Cloud Vision API
   • Cloud Storage API
3. Create a service account and download the JSON key file
4. Place the key file as skoolme-ocr-b933da63cd81.json in the backend directory

gsutil mb gs://skoolme-audio-transcripts

For Development:

python run_server.py

For Production:

gunicorn --bind 0.0.0.0:5000 wsgi:app

GET /api/health

Returns server status and active sessions count.

POST /api/upload

Upload files for analysis. Returns a session ID.

Request: Multipart form data with files field Response:

{
    "session_id": "uuid",
    "files": [
        {
            "filename": "document.pdf",
            "original_name": "document.pdf",
            "file_type": "document",
            "size": 1024000
        }
    ],
    "message": "Successfully uploaded 1 files"
}

POST /api/analyze

Start analysis of uploaded files.

Request:

{
    "session_id": "uuid"
}

Response:

{
    "session_id": "uuid",
    "message": "Analysis started",
    "status": "processing"
}

GET /api/progress/{session_id}

Get real-time analysis progress.

Response:

{
  "status": "processing|completed|error",
  "progress": 85,
  "message": "Processing file 3 of 5...",
  "results": [...],
  "overall_score": 82.5,
  "generated_title": "Introduction to Physics",
  "error": null
}

DELETE /api/cleanup/{session_id}

Clean up session files and data.

• PDF: Text extraction with OCR fallback
• DOCX: Native text extraction
• TXT: Direct text reading with encoding detection
• Images: OCR using Google Vision API or Tesseract

• Format Support: MP3, WAV, M4A
• Conversion: Auto-converts to 16kHz mono WAV
• Transcription: Google Cloud Speech-to-Text with timestamps
• Speaker Diarization: Identifies multiple speakers

The system calculates extraction scores based on:

• Content Length (50%): Amount of text extracted
• Word Diversity (30%): Unique words vs total words
• Structure (20%): Presence of sentences and paragraphs

• 80-100%: 🟢 Green - Excellent extraction
• 30-79%: 🟡 Yellow - Good extraction with some issues
• 0-29%: 🔴 Red - Poor extraction, unusable content

The API provides comprehensive error handling:

• File size validation
• File type validation
• Google Cloud API errors
• Processing timeouts
• Storage errors

GOOGLE_APPLICATION_CREDENTIALS=path/to/credentials.json
FLASK_ENV=production

FROM python:3.9-slim

WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt

# Install system dependencies
RUN apt-get update && apt-get install -y \
    tesseract-ocr \
    poppler-utils \
    && rm -rf /var/lib/apt/lists/*

COPY . .
EXPOSE 5000

CMD ["gunicorn", "--bind", "0.0.0.0:5000", "wsgi:app"]

• Use Redis for session storage in production
• Implement file cleanup jobs
• Set up monitoring and logging
• Use cloud storage for file uploads
• Implement rate limiting

# Test file upload
curl -X POST -F "[email protected]" http://localhost:5000/api/upload

# Test health check
curl http://localhost:5000/api/health

Set FLASK_ENV=development for detailed error messages and auto-reload.

• Documents: Usually < 30 seconds
• Audio files: 1-2 minutes per minute of audio
• Large files: May take longer, progress is tracked

• File validation prevents malicious uploads
• Temporary files are cleaned up automatically
• Google Cloud credentials should be kept secure
• Implement authentication for production use

Import Errors:

• Ensure all dependencies are installed: pip install -r requirements.txt
• Check system dependencies (Tesseract, Poppler)

Google Cloud Errors:

• Verify credentials file exists and is valid
• Check API permissions and billing
• Ensure storage bucket exists

File Processing Errors:

• Check file format compatibility
• Verify file size limits
• Ensure sufficient disk space

Check the console output for detailed error messages and processing status.