app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoProcessor
from PIL import Image
import torch

# Load model and processor
model = AutoModelForCausalLM.from_pretrained("mynkchaudhry/Florence-2-FT-DocVQA", trust_remote_code=True)
processor = AutoProcessor.from_pretrained("mynkchaudhry/Florence-2-FT-DocVQA")

def generate_response(image, question):
    try:
        if image.mode != "RGB":
            image = image.convert("RGB")

        inputs = processor(text=question, images=image, return_tensors="pt")

        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        model.to(device)
        inputs = {key: value.to(device) for key, value in inputs.items()}

        generated_ids = model.generate(
            input_ids=inputs["input_ids"],
            pixel_values=inputs["pixel_values"],
            max_length=1024,
            num_beams=3,
            early_stopping=True
        )

        response = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
        return response
    except Exception as e:
        return f"Error processing image: {e}"

# Example images for demonstration (update paths as needed)
examples = [
    ["demo.png", "what is the address in the page?"],
    ["demo2.jpg", "what is the date in the page?"],
    ["demo.png", "what is the name in the page?"]
]

# Gradio interface
iface = gr.Interface(
    fn=generate_response,
    inputs=[gr.Image(type="pil"), gr.Textbox(label="Question")],
    outputs=gr.Textbox(label="Response"),
    examples=examples,
    title="Image to Text Extractor",
    description="Upload an image and provide a question. This tool will extract the relevant information from the image based on your question."
)

# Launch the interface
iface.launch()