app.py

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()