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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)
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