app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForImageClassification
from PIL import Image
import torch

# Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained('ucaslcl/GOT-OCR2_0')
model = AutoModelForImageClassification.from_pretrained('ucaslcl/GOT-OCR2_0')

def perform_ocr(image):
    # Ensure the image is in the right format
    if isinstance(image, Image.Image):
        image = image.convert("RGB")
    else:
        raise ValueError("Input must be a PIL Image")

    # Use the model to perform OCR
    inputs = tokenizer(image, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs)
    
    # Get the predictions
    predictions = outputs.logits.argmax(dim=1).item()
    return predictions

# Create the Gradio interface
iface = gr.Interface(
    fn=perform_ocr,
    inputs=gr.inputs.Image(type="pil"),
    outputs="text",
    title="OCR with GOT-OCR2.0",
    description="Upload an image for Optical Character Recognition."
)

# Launch the interface
iface.launch()