app.py

# app.py

import gradio as gr
from transformers import TrOCRProcessor, VisionEncoderDecoderModel
from PIL import Image
import sympy

# Load the pre-trained model and processor outside the function for efficiency
processor = TrOCRProcessor.from_pretrained('microsoft/trocr-base-handwritten')
model = VisionEncoderDecoderModel.from_pretrained('microsoft/trocr-base-handwritten')

def solve_math_problem(image):
    try:
        # Ensure the image is in RGB format
        image = image.convert("RGB")
        
        # Resize and normalize the image as expected by the model
        pixel_values = processor(images=image, return_tensors="pt").pixel_values
        
        # Generate the text (this extracts the handwritten equation)
        generated_ids = model.generate(pixel_values)
        generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
        
        # Clean and prepare the extracted text
        problem_text = generated_text.strip().replace(' ', '')
        
        # Use sympy to parse and solve the equation
        # Handle simple arithmetic and algebraic equations
        expr = sympy.sympify(problem_text)
        solution = sympy.solve(expr)
        
        # Format the solution for display
        if isinstance(solution, list):
            solution = ', '.join([str(s) for s in solution])
        else:
            solution = str(solution)
        
        return f"**Problem:** {problem_text}\n\n**Solution:** {solution}"
        
    except Exception as e:
        return f"**Error processing image:** {str(e)}"

# Create the Gradio interface
demo = gr.Interface(
    fn=solve_math_problem,
    inputs=gr.Image(
        type="pil", 
        label="Upload Handwritten Math Problem",
        image_mode="RGB"
    ),
    outputs=gr.Markdown(),
    title="Handwritten Math Problem Solver",
    description="Upload an image of a handwritten math problem, and the app will attempt to solve it.",
    examples=[
        ["example_addition.png"],
        ["example_algebra.jpg"]
    ],
    allow_flagging="never"
)

if __name__ == "__main__":
    demo.launch()