Update app.py

app.py

@@ -74,16 +74,38 @@ def get_response_with_fallback(prompt):
   # If none succeed, return an error message
   return "Failed to generate diagnosis with any model."
 
+
+def preprocess_data(new_data_path):
+    # Load new data
+    new_data = pd.DataFrame(new_data_path)
+    
+    # Scale the data using the same scaler used during training
+    scaler = MinMaxScaler(feature_range=(0, 1))
+    scaled_data = scaler.fit_transform(new_data)
+    
+    # Reshape the data to fit the model input
+    reshaped_data = scaled_data.reshape((scaled_data.shape[1], scaled_data.shape[0], 1))
+    
+    return reshaped_data
+
+def predict_clusters(model, preprocessed_data):
+    # Predict the cluster for each instance
+    predictions = model.predict(preprocessed_data)
+    
+    # Get the cluster with the highest probability
+    predicted_clusters = np.argmax(predictions, axis=1)
+    
+    return predicted_clusters
+
 @app.post("/predict", response_model=PersonalityResponse)
 def predict_personality(request: PersonalityRequest):
-    # input_datas = list(map(five2one, request.responses))
+    preprocessed_data = preprocess_data(request.responses)
 
-    input_data = np.array(request.responses).reshape(1, -1, 1) 
-    
-    prediction = model.predict(input_data)
+    predicted_clusters = predict_clusters(model, preprocessed_data)
+    personality_type = predicted_clusters[0]
     
-    personality_type = int(np.argmax(prediction, axis=1)[0])
-    personality_name = personality_mapping[personality_type]
+    personality_name = personality_mapping[predicted_clusters[0]]
+    print(personality_name)
     
     return PersonalityResponse(personality_type=personality_type, personality_name=personality_name)