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

# # Load model and processor
# model_name = "google/pix2struct-docvqa-large"
# model = Pix2StructForConditionalGeneration.from_pretrained(model_name)
# processor = Pix2StructProcessor.from_pretrained(model_name)

# def process_image(image_path):
#     try:
#         # Load the image
#         image = Image.open(image_path).convert("RGB")

#         # Prepare the input
#         inputs = processor(images=image, text="What does this image say?", return_tensors="pt")

#         # Generate prediction
#         output = model.generate(**inputs)

#         # Decode the output
#         solution = processor.decode(output[0], skip_special_tokens=True)
#         return solution

#     except Exception as e:
#         return f"Error processing image: {str(e)}"

# def predict(image):
#     """Handles image input for Gradio."""
#     return process_image(image)

# # Gradio app
# iface = gr.Interface(
#     fn=predict,
#     inputs=gr.Image(type="filepath"),
#     outputs="text",
#     title="Image Text Solution"
# )

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