app.py

from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor
import gradio as gr
from PIL import Image

# Load the pre-trained Pix2Struct model and processor.
model_name = "google/pix2struct-mathqa-base"
model = Pix2StructForConditionalGeneration.from_pretrained(model_name)
processor = Pix2StructProcessor.from_pretrained(model_name)

def solve_math_problem(image):
    try:
        # Ensure the image is in RGB format.
        image = image.convert("RGB")
        
        # Preprocess the image and text.
        # Note: We omit the header_text parameter because this is not a VQA task.
        inputs = processor(
            images=[image],                           # Provide a list of images.
            text="Solve the following math problem:", # Prompt text.
            return_tensors="pt",
            max_patches=2048                          # Increase the maximum patches for better math handling.
        )
        
        # Generate the solution with specified generation parameters.
        predictions = model.generate(
            **inputs,
            max_new_tokens=200,
            early_stopping=True,
            num_beams=4,
            temperature=0.2
        )
        
        # Decode the input text and the model prediction.
        # Here, we access "input_ids" via the dictionary key.
        problem_text = processor.decode(
            inputs["input_ids"][0],
            skip_special_tokens=True,
            clean_up_tokenization_spaces=True
        )
        solution = processor.decode(
            predictions[0],
            skip_special_tokens=True,
            clean_up_tokenization_spaces=True
        )
        
        return f"Problem: {problem_text}\nSolution: {solution}"
        
    except Exception as e:
        return f"Error processing image: {str(e)}"

# Set up the Gradio interface.
demo = gr.Interface(
    fn=solve_math_problem,
    inputs=gr.Image(
        type="pil", 
        label="Upload Handwritten Math Problem",
        image_mode="RGB",  # Force RGB conversion.
        source="upload"
    ),
    outputs=gr.Textbox(label="Solution", show_copy_button=True),
    title="Handwritten Math Problem Solver",
    description="Upload an image of a handwritten math problem (algebra, arithmetic, etc.) and get the solution",
    examples=[
        ["example_addition.png"],  # Ensure these example files exist in your working directory.
        ["example_algebra.jpg"]
    ],
    theme="soft",
    allow_flagging="never"
)

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