Update app.py

app.py

@@ -71,7 +71,7 @@ with open("ohe_drain.pkl", "rb") as f:
     ohe_drain = pickle.load(f)
 
 with open("ohe_stage.pkl", "rb") as f:
-    ohe_drain = pickle.load(f)
+    ohe_stage = pickle.load(f)
 
 with open("le.pkl", "rb") as f:
     le = pickle.load(f)
@@ -79,15 +79,23 @@ with open("le.pkl", "rb") as f:
 with open("knn.pkl", "rb") as f:
     knn = pickle.load(f)
 
-stage_encoded = ohe.transform([[stage_fear]])[0][0]  # gender encoded using one hot encoding
-drain_encoded = ohe.transform([[drained]])[0][0]
+stage_encoded = ohe_stage.transform([[stage_fear]])[0]  # gender encoded using one hot encoding
+drain_encoded = ohe_drain.transform([[drained]])[0]
 
 
 numeric_features = np.array([[time_spent, social_event, going_outside, friends, post_frequency]])
-scaled_features = rs.transform(numeric_features)
+scaled_features = rs.transform(numeric_features)[0]
+
+st.write("Scaled Features:", scaled_features)
 
 
-final_input = np.concatenate(([gender_encoded], scaled_features[0])).reshape(1, -1)
+final_input = np.concatenate((
+    scaled_features[:1],        
+    stage_encoded,              
+    scaled_features[1:3],       
+    drain_encoded,               
+    scaled_features[3:]          
+)).reshape(1, -1)
 
 
 #input_data = np.array([[gender_encoded, age, urea, cr, HbA1c, chol, tg, hdl, ldl, vldl, bmi]])
@@ -96,19 +104,18 @@ final_input = np.concatenate(([gender_encoded], scaled_features[0])).reshape(1,
 
 
 prediction_labels = {
-    0: "Non-Diabetic",
-    1: "Diabetic",
-    2: "Pre-Diabetic"
+    0: "Extrovert",
+    1: "Introvert"
 }
 
-# if st.button("Predict"):
-#     prediction = knn.predict(final_input)[0]
-#     result_label = prediction_labels.get(prediction, "Unknown")
-#     st.success(f"Predicted Result: {result_label}")
-#     if result_label == "Pre-Diabetic":
-#         st.warning("You are in the pre-diabetic range. It's advisable to consult a healthcare professional for further evaluation.")
-#     elif result_label == "Diabetic":
-#         st.error("You are classified as diabetic. Please seek medical advice for appropriate management.")
+if st.button("Predict"):
+    prediction = knn.predict(final_input)[0]
+    result_label = prediction_labels.get(prediction, "Unknown")
+    # st.success(f"Predicted Personality: {result_label}")
+    # if result_label == "Pre-Diabetic":
+    #     st.warning("You are in the pre-diabetic range. It's advisable to consult a healthcare professional for further evaluation.")
+    # elif result_label == "Diabetic":
+    #     st.error("You are classified as diabetic. Please seek medical advice for appropriate management.")
 
 
 
@@ -126,11 +133,11 @@ if st.button("Predict"):
         unsafe_allow_html=True
     )
 
-    # Message based on result
-    if result_label == "Pre-Diabetic":
-        st.warning("You are in the pre-diabetic range. It's advisable to consult a healthcare professional for further evaluation.")
-    elif result_label == "Diabetic":
-        st.error("You are classified as diabetic. Please seek medical advice for appropriate management.")
+    # # Message based on result
+    # if result_label == "Pre-Diabetic":
+    #     st.warning("You are in the pre-diabetic range. It's advisable to consult a healthcare professional for further evaluation.")
+    # elif result_label == "Diabetic":
+    #     st.error("You are classified as diabetic. Please seek medical advice for appropriate management.")