demo.py

#!/usr/bin/env python3
"""
Demo script for the Handwriting Assessment App
Creates actual images and processes them through the Gemini API to demonstrate real OCR functionality.
"""

import tempfile
import os
from app import HandwritingRecognizer, TextScorer
from config import GEMINI_API_KEY, GEMINI_MODEL
from sample_images import create_sample_answer_key_image, create_sample_student_response_image

def run_demo():
    """Run a demo that actually calls the Gemini API to process images."""
    print("🎯 Handwriting Assessment App - Real API Demo")
    print("=" * 60)
    
    # Check if API key is available
    if not GEMINI_API_KEY:
        print("❌ Error: No Gemini API key found!")
        print("   Please set GEMINI_API_KEY in your .env file")
        print("   Get your API key from: https://makersuite.google.com/app/apikey")
        return
    
    print(f"✅ API Key loaded: {GEMINI_API_KEY[:20]}...")
    print(f"🤖 Using model: {GEMINI_MODEL} (supports 768x768 image processing)")
    
    try:
        # Initialize the recognizer
        print("\n1. Initializing Gemini API...")
        recognizer = HandwritingRecognizer(GEMINI_API_KEY)
        print("   ✅ API initialized successfully")
        
        # Generate sample images
        print("\n2. Generating sample handwritten images...")
        answer_key_img = create_sample_answer_key_image()
        student_response_img = create_sample_student_response_image()
        print("   ✅ Sample images created")
        
        # Save images temporarily for display purposes
        with tempfile.NamedTemporaryFile(suffix='.png', delete=False) as tmp1:
            answer_key_img.save(tmp1.name)
            answer_key_path = tmp1.name
            
        with tempfile.NamedTemporaryFile(suffix='.png', delete=False) as tmp2:
            student_response_img.save(tmp2.name)
            student_response_path = tmp2.name
        
        print(f"   📁 Answer key image saved: {answer_key_path}")
        print(f"   📁 Student response image saved: {student_response_path}")
        
        # Process answer key through OCR
        print("\n3. Processing answer key through Gemini OCR...")
        answer_key_text = recognizer.extract_text_from_image(answer_key_img)
        print("   ✅ Answer key processed")
        print("   📄 Extracted text:")
        print("   " + "-" * 40)
        for line in answer_key_text.split('\n'):
            print(f"   {line}")
        
        # Process student response through OCR
        print("\n4. Processing student response through Gemini OCR...")
        student_response_text = recognizer.extract_text_from_image(student_response_img)
        print("   ✅ Student response processed")
        print("   📄 Extracted text:")
        print("   " + "-" * 40)
        for line in student_response_text.split('\n'):
            print(f"   {line}")
        
        # Calculate similarity score
        print("\n5. Calculating similarity score...")
        score, metrics = TextScorer.calculate_similarity(answer_key_text, student_response_text)
        interpretation, color = TextScorer.get_score_interpretation(score)
        
        print(f"\n📊 RESULTS:")
        print(f"   Overall Score: {score:.1%}")
        print(f"   Interpretation: {interpretation}")
        print(f"   Sequence Similarity: {metrics['sequence_similarity']:.1%}")
        print(f"   Word Similarity: {metrics['word_similarity']:.1%}")
        print(f"   Character Similarity: {metrics['char_similarity']:.1%}")
        
        print(f"\n✅ Real API demo completed successfully!")
        print("   🖼️  Sample images were processed through actual Gemini OCR")
        print(f"   🤖 Text extraction used {GEMINI_MODEL} with 768x768 image processing")
        print("   📊 Scoring was calculated from real OCR results")
        print("\n   To use the full Gradio interface, run: python app.py")
        
        # Clean up temporary files
        try:
            os.unlink(answer_key_path)
            os.unlink(student_response_path)
        except:
            pass
            
    except Exception as e:
        print(f"\n❌ Error during demo: {str(e)}")
        print("   This might be due to:")
        print("   - Invalid API key")
        print("   - Network connectivity issues")
        print("   - API quota exceeded")
        print("   - Model availability issues")

if __name__ == "__main__":
    run_demo()