Update app.py

app.py

@@ -1,31 +1,36 @@
-import tensorflow as tf
-from transformers import TFBertForSequenceClassification
-from flask import Flask, request, jsonify
+import os
+from transformers import TFBertForSequenceClassification, BertTokenizerFast
 
-app = Flask(__name__)
+def load_model(model_name):
+    try:
+        # Load TensorFlow model from Hugging Face
+        model = TFBertForSequenceClassification.from_pretrained(model_name, use_auth_token=os.getenv('API_KEY'))
+    except OSError:
+        # Fallback to PyTorch model if TensorFlow fails
+        model = TFBertForSequenceClassification.from_pretrained(model_name, use_auth_token=os.getenv('API_KEY'), from_pt=True)
+    return model
 
-# Load the model
-model_name = "Erfan11/Neuracraft"
-model = TFBertForSequenceClassification.from_pretrained(model_name, use_auth_token="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")
+def load_tokenizer(model_name):
+    tokenizer = BertTokenizerFast.from_pretrained(model_name, use_auth_token=os.getenv('API_KEY'))
+    return tokenizer
 
-@app.route('/predict', methods=['POST'])
-def predict():
-    data = request.get_json()
-    # Preprocess input data
-    inputs = preprocess_data(data)
-    # Make prediction
-    predictions = model.predict(inputs)
-    # Postprocess and return results
-    results = postprocess_predictions(predictions)
-    return jsonify(results)
+def predict(text, model, tokenizer):
+    inputs = tokenizer(text, return_tensors="tf")
+    outputs = model(**inputs)
+    return outputs
 
-def preprocess_data(data):
-    # Implement your data preprocessing here
-    pass
+def main():
+    model_name = os.getenv('MODEL_PATH')
+    if model_name is None:
+        raise ValueError("MODEL_PATH environment variable not set or is None")
 
-def postprocess_predictions(predictions):
-    # Implement your result postprocessing here
-    pass
+    model = load_model(model_name)
+    tokenizer = load_tokenizer(model_name)
 
-if __name__ == '__main__':
-    app.run(debug=True)
+    # Example prediction
+    text = "Sample input text"
+    result = predict(text, model, tokenizer)
+    print(result)
+
+if __name__ == "__main__":
+    main()