iniit

main.py

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+# from flask import Flask, request, jsonify
+# import tensorflow as tf
+# import numpy as np
+
+# app = Flask(__name__)
+
+# # Load the model
+# model = tf.keras.models.load_model('walking_classifier_tf.h5')
+
+# @app.route('/predict', methods=['POST'])
+# def predict():
+#     data = request.get_json(force=True)
+#     predictions = model.predict(np.array(data['features']))
+#     return jsonify({'predictions': predictions.tolist()})
+
+# if __name__ == '__main__':
+#     app.run(host='0.0.0.0', port=5000)
+
+
+import os
+from flask import Flask, request, jsonify
+import numpy as np
+import tensorflow as tf
+from sklearn.preprocessing import StandardScaler
+import joblib
+
+app = Flask(__name__)
+
+# Load the saved scaler and model
+scaler = StandardScaler()
+scaler.mean_ = np.loadtxt('scaler_mean.csv', delimiter=',')
+scaler.scale_ = np.loadtxt('scaler_std.csv', delimiter=',')
+
+# Load the TFLite model and allocate tensors
+interpreter = tf.lite.Interpreter(model_path="walking_classifier.tflite")
+interpreter.allocate_tensors()
+
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    try:
+        # Get the data from the request
+        input_data = request.json['data']
+
+        # Convert to numpy array and reshape
+        input_data = np.array(input_data, dtype=np.float32)
+
+        # Normalize the data
+        input_data = scaler.transform(input_data)
+
+        # Check the input shape
+        if input_data.shape[1] != input_details[0]['shape'][1]:
+            return jsonify({"error": "Input shape does not match model expected shape."})
+
+        # Prepare the prediction list
+        predictions = []
+
+        # Run the model for each input data
+        for i in range(input_data.shape[0]):
+            single_input_data = input_data[i].reshape(1, -1)
+            interpreter.set_tensor(input_details[0]['index'], single_input_data)
+            interpreter.invoke()
+            output_data = interpreter.get_tensor(output_details[0]['index'])[0]
+            predictions.append(float(output_data))
+
+        # Convert to binary labels
+        threshold = 0.5
+        predicted_labels = (np.array(predictions) > threshold).astype(int).tolist()
+
+        # Return the predictions as JSON
+        return jsonify({"predictions": predicted_labels})
+    except Exception as e:
+        return jsonify({"error": str(e)})
+
+if __name__ == '__main__':
+    port = int(os.environ.get('PORT', 8080))
+    app.run(host='0.0.0.0', port=port)

requirements.txt

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+Flask
+tensorflow
+scikit-learn
+numpy