Create app.py

app.py

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