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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()
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