app.py

import os
from flask import Flask, request, jsonify
from PIL import Image
import torch
from torchvision.transforms import functional as F
from transformers import TrOCRProcessor, VisionEncoderDecoderModel
from transformers import AutoModel

from transformers import AutoModel

model_name = "trocrnew.pth"
access_token = os.environ.get("HF_TOKEN")
model = AutoModel.from_pretrained(model_name, token=access_token)

# access_token = os.environ.get("HF_TOKEN")

# model = AutoModel.from_pretrained("trocrnew.pth", token=access_token)

app = Flask(__name__)

# Load the trained model and processor
processor = TrOCRProcessor.from_pretrained("microsoft/trocr-small-printed")
model = VisionEncoderDecoderModel.from_pretrained("trocrnew.pth")

# Set the model in evaluation mode
model.eval()

def ocr(image):
    # Preprocess the image
    image = F.to_tensor(image).unsqueeze(0)
    
    # Perform OCR
    with torch.no_grad():
        generated_ids = model.generate(image)
    
    # Decode the generated text
    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
    
    return generated_text

@app.route('/ocr', methods=['POST'])
def ocr_endpoint():
    # Check if the POST request contains a file
    if 'file' not in request.files:
        return jsonify({'error': 'No file provided'}), 400

    file = request.files['file']

    # Check if the file has an allowed extension
    allowed_extensions = {'png', 'jpg', 'jpeg', 'gif'}
    if '.' not in file.filename or file.filename.split('.')[-1].lower() not in allowed_extensions:
        return jsonify({'error': 'Invalid file type'}), 400

    # Read the image and perform OCR
    try:
        image = Image.open(file).convert('RGB')
        text = ocr(image)
        return jsonify({'text': text}), 200
    except Exception as e:
        return jsonify({'error': str(e)}), 500

if __name__ == '__main__':
    app.run(debug=True)