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backend/IT18256888/app.py

+from flask import Flask, request, jsonify
+import joblib
+from keras.layers import Dense,Dropout
+from keras.models import Sequential
+import keras.backend as K
+
+scaler_x = joblib.load('scaler_x.pkl')
+scaler_y = joblib.load('scaler_y.pkl')
+
+app=Flask(__name__)
+
+def load_dyslexia_model():
+
+    K.clear_session()
+    model=Sequential() #model is an empty NN
+    #1st hidden layer (dense type-fully connected)
+    model.add(Dense(64,input_dim=7,activation='relu'))
+    model.add(Dropout(0.5))
+    #2nd Hidden layer
+    model.add(Dense(128,activation='relu',kernel_initializer='normal'))
+    model.add(Dropout(0.5))
+    model.add(Dense(64,activation='relu',kernel_initializer='normal'))
+    model.add(Dropout(0.5))
+    model.add(Dense(7,activation='relu',kernel_initializer='normal'))
+    #final layer
+    model.add(Dense(1,input_dim=7,activation='linear'))
+
+    model.compile(loss='mse',optimizer='adam')
+    model.load_weights('model-569.model')
+    
+    return model
+
+@app.route('/get_level', methods=['POST','GET'])
+
+def get_level():
+
+    # User_json=request.json
+    Q1 =int(request.args.get('q1'))
+    Q2 =int(request.args.get('q2'))
+    Q3 =int(request.args.get('q3'))
+    Q4 =int(request.args.get('q4'))
+    Q5 =int(request.args.get('q5'))
+    Q6 =int(request.args.get('q6'))
+    TD =float(request.args.get('time'))
+
+    test_data=[Q1,Q2,Q3,Q4,Q5,Q6,TD]
+    print(test_data)
+    
+    return "terra"
+
+app.run(host='0.0.0.0',port=5000)