Update app.py

app.py

@@ -1,31 +1,23 @@
+from flask import Flask, request, jsonify
 from transformers import AutoTokenizer, AutoModel
 
-def load_model_and_tokenizer():
-    try:
-        # Load the tokenizer with the specific revision
-        tokenizer = AutoTokenizer.from_pretrained(
-            'stepfun-ai/GOT-OCR2_0',
-            revision='cf6b7386bc89a54f09785612ba74cb12de6fa17c',  # Pin the specific revision
-            trust_remote_code=True
-        )
+app = Flask(__name__)
 
-        # Load the model with the specific revision
-        model = AutoModel.from_pretrained(
-            'stepfun-ai/GOT-OCR2_0',
-            revision='cf6b7386bc89a54f09785612ba74cb12de6fa17c',  # Pin the specific revision
-            trust_remote_code=True
-        )
+# Load model and tokenizer
+try:
+    tokenizer = AutoTokenizer.from_pretrained('stepfun-ai/GOT-OCR2_0', revision='cf6b7386bc89a54f09785612ba74cb12de6fa17c', trust_remote_code=True)
+    model = AutoModel.from_pretrained('stepfun-ai/GOT-OCR2_0', revision='cf6b7386bc89a54f09785612ba74cb12de6fa17c', trust_remote_code=True)
+except Exception as e:
+    print(f"Error loading model and tokenizer: {e}")
 
-        return model, tokenizer
+@app.route('/predict', methods=['POST'])
+def predict():
+    # Assuming you send image data in the request
+    data = request.json
+    # Add your model inference logic here
+    # e.g., model.forward(data)
 
-    except Exception as e:
-        print(f"An error occurred while loading the model and tokenizer: {e}")
-        return None, None
+    return jsonify({"message": "Prediction made successfully!"})
 
-# Example usage
 if __name__ == "__main__":
-    model, tokenizer = load_model_and_tokenizer()
-    if model and tokenizer:
-        print("Model and tokenizer loaded successfully!")
-    else:
-        print("Failed to load model and tokenizer.")
+    app.run(host='0.0.0.0', port=5000)  # Adjust port if necessary