app.py

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

# # Use a publicly available high-capacity model.
# # For instance, we use "google/pix2struct-docvqa-large". 
# # (If you need a different model or a private one, adjust accordingly and add authentication if necessary.)
# model_name = "google/pix2struct-docvqa-large"

# model = Pix2StructForConditionalGeneration.from_pretrained(model_name)
# processor = Pix2StructProcessor.from_pretrained(model_name)

# def solve_problem(image):
#     try:
#         # Ensure the image is in RGB.
#         image = image.convert("RGB")
        
#         # Preprocess image and text prompt.
#         inputs = processor(
#             images=[image],
#             text="Solve the following problem:",
#             return_tensors="pt",
#             max_patches=2048
#         )
        
#         # Generate prediction.
#         predictions = model.generate(
#             **inputs,
#             max_new_tokens=200,
#             early_stopping=True,
#             num_beams=4,
#             temperature=0.2
#         )
        
#         # Decode the prompt (input IDs) and the generated output.
#         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.
# iface = gr.Interface(
#     fn=solve_problem,
#     inputs=gr.Image(type="pil", label="Upload Your Problem Image", image_mode="RGB"),
#     outputs=gr.Textbox(label="Solution", show_copy_button=True),
#     title="Problem Solver with Pix2Struct",
#     description=(
#         "Upload an image (for example, a handwritten math or logic problem) "
#         "and get a solution generated by a high-capacity Pix2Struct model.\n\n"
#         "Note: For best results on domain-specific tasks, consider fine-tuning on your own dataset."
#     ),
#     examples=[
#         ["example_problem1.png"],
#         ["example_problem2.jpg"]
#     ],
#     theme="soft",
#     allow_flagging="never"
# )

# if __name__ == "__main__":
#     iface.launch()