app.py

import gradio as gr
import numpy as np
from PIL import Image
from transformers import pipeline


ocr_pipe = pipeline("image-to-text", model="facebook/nougat-base")  # Best for academic handwriting
# Alternative OCR models: "mfrashad/arabic-handwriting-ocr", "TesseractOCR"

similarity_pipe = pipeline("feature-extraction", model="sentence-transformers/paraphrase-albert-small-v2")  # Lightweight

def validate_answer(image, user_text, correct_answer):
    outputs = ["", "", ""]  # Initialize outputs
    
    try:
        # OCR Processing
        if image:
            img = Image.fromarray(image.astype('uint8'))
            ocr_result = ocr_pipe(img)
            user_text = ocr_result[0]['generated_text']
            outputs[1] = f"📝 Extracted: {user_text}"
        
        # Clarity Check (simple rule-based)
        clarity = sum(c.isalnum() for c in user_text) / max(1, len(user_text))
        outputs[0] = f"✅ Clarity: {clarity:.0%}" if clarity > 0.5 else "⚠️ Unclear handwriting"
        
        # Only compare if text is clear
        if clarity > 0.5:
            embeds = np.array(similarity_pipe([correct_answer, user_text]))
            similarity = np.dot(embeds[0].mean(axis=0), embeds[1].mean(axis=0))
            outputs[2] = f"🔍 Similarity: {similarity:.1%}"
            
    except Exception as e:
        outputs = [f"❌ Error: {str(e)}"] * 3
        
    return outputs

# Simple Interface
with gr.Blocks() as demo:
    gr.Markdown("## Free Handwriting Validator")
    with gr.Row():
        gr.Image(label="Upload Answer", sources=["upload"], type="numpy")
        gr.Textbox(label="Or Type Answer")
    gr.Textbox(label="Correct Answer", value="Photosynthesis occurs in chloroplasts.")
    gr.Button("Validate").click(
        validate_answer,
        inputs=[gr.Image(), gr.Textbox(), gr.Textbox()],
        outputs=[gr.Textbox(label="Status"), 
                gr.Textbox(label="OCR Result"), 
                gr.Textbox(label="Comparison")]
    )

demo.launch(debug=True)