app.py

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

# Set device
device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32

# Load model and processor
model = AutoModelForCausalLM.from_pretrained("microsoft/Florence-2-large", torch_dtype=torch_dtype, trust_remote_code=True).to(device)
processor = AutoProcessor.from_pretrained("microsoft/Florence-2-large", trust_remote_code=True)

# Prediction function
def predict_from_url(url):
    prompt = "<OCR>"
    if not url:
        return "Error: Please input a URL", None

    try:
        # Open the image and convert to RGB format to handle grayscale or other formats
        image = Image.open(BytesIO(requests.get(url).content)).convert("RGB")
    except Exception as e:
        return f"Error: Failed to load or process the image: {str(e)}", None
    
    # Preprocess and perform inference
    try:
        inputs = processor(text=prompt, images=image, return_tensors="pt").to(device, torch_dtype)
        generated_ids = model.generate(
            input_ids=inputs["input_ids"],
            pixel_values=inputs["pixel_values"],
            max_new_tokens=4096,
            num_beams=3,
            do_sample=False
        )
        generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
        parsed_answer = processor.post_process_generation(generated_text, task=prompt, image_size=(image.width, image.height))
        
        # Extract OCR text
        ocr_text = parsed_answer.get("<OCR>", "")
        output_text = " ".join(ocr_text.replace("\n", " ").strip().split())
    except Exception as e:
        return f"Error: Failed to process the image for OCR: {str(e)}", image

    return output_text, image

# Gradio Interface
demo = gr.Interface(
    fn=predict_from_url,
    inputs=gr.Textbox(label="Enter Image URL"),
    outputs=[
        gr.Textbox(label="Extracted Text"),
        gr.Image(label="Uploaded Image")
    ],
    title="OCR Text Extractor",
    description="Provide an image URL, and this tool will extract text using OCR.",
    allow_flagging="never"
)

# Launch the app
demo.launch()