app.py

import torch
from PIL import Image
from transformers import TrOCRProcessor, VisionEncoderDecoderModel
import gradio as gr

# Set up the device (GPU or CPU)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# Load the fine-tuned model and processor from the Hugging Face repository
model = VisionEncoderDecoderModel.from_pretrained("Heramb26/TC-OCR-Custom").to(device)
processor = TrOCRProcessor.from_pretrained("microsoft/trocr-large-handwritten")

def ocr_image(image):
    """
    Perform OCR on an image using the loaded model.
    :param image: Input PIL image.
    :return: Extracted text.
    """
    # Preprocess image and generate OCR text
    pixel_values = processor(image, return_tensors="pt").pixel_values.to(device)
    generated_ids = model.generate(pixel_values)
    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
    return generated_text

# Create a Gradio interface
interface = gr.Interface(fn=ocr_image,  # Function to be called when an image is uploaded
                         inputs=gr.Image(type="pil"),  # Input is an image file (Gradio v3+ API)
                         outputs="text",  # Output is extracted text
                         title="OCR Inference",  # Title of the app
                         description="Upload an image with handwritten text to extract the text.")  # Description

# Launch the Gradio app
interface.launch()