Delete app.py

app.py

@@ -1,34 +0,0 @@
-from transformers import AutoModel, AutoTokenizer
-from flask import Flask, request, jsonify
-import tensorflow as tf
-
-app = Flask(__name__)
-
-# Load Hugging Face model and tokenizer
-tokenizer = AutoTokenizer.from_pretrained("Erfan11/Neuracraft", use_auth_token="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")
-hf_model = AutoModel.from_pretrained("Erfan11/Neuracraft", use_auth_token="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")
-
-# Load TensorFlow model
-tf_model = tf.keras.models.load_model('path_to_your_tf_model.h5')
-
-@app.route('/predict', methods=['POST'])
-def predict():
-    data = request.get_json()
-    
-    # Tokenize the input using Hugging Face's tokenizer
-    inputs = tokenizer(data["text"], return_tensors="pt")
-    
-    # Make prediction with Hugging Face model
-    hf_outputs = hf_model(**inputs)
-    
-    # Optionally: You can also add TensorFlow model predictions here, depending on what it’s used for.
-    # Assuming the TensorFlow model is used for something else like feature extraction
-    tf_outputs = tf_model.predict([data["text"]])  # Modify based on your input processing
-
-    return jsonify({
-        "hf_outputs": hf_outputs[0].tolist(),  # Convert Hugging Face output to JSON serializable format
-        "tf_outputs": tf_outputs.tolist()  # Convert TensorFlow output to JSON serializable format
-    })
-
-if __name__ == '__main__':
-    app.run(debug=True)