Update app.py

app.py

@@ -7,14 +7,14 @@ import pandas as pd
 import matplotlib.pyplot as plt
 
 
-# Load the LightGBM model and other necessary objects
+
 with open('lgb1_model.pkl', 'rb') as f:
     lgb1 = pickle.load(f)
 
 categorical_features = joblib.load("categorical_features.joblib")
 encoder = joblib.load("encoder.joblib")
 
-# Sidebar option to select the dashboard
+
 option = st.sidebar.selectbox("Which dashboard?", ("Model information", "Stroke prediction"))
 st.title(option)
 
@@ -24,7 +24,7 @@ def get_pred():
     """
     st.header("Stroke probability calculator ")
 
-    # User input for prediction
+    
     gender = st.selectbox("Select gender: ", ["Male", "Female", 'Other'])
     work_type = st.selectbox("Work type: ", ["Private", "Self_employed", 'children', 'Govt_job', 'Never_worked'])
     residence_status = st.selectbox("Residence status: ", ["Urban", "Rural"])
@@ -36,7 +36,7 @@ def get_pred():
     avg_glucosis_lvl = st.slider("Average glucosis level: ", 50, 280)
     bmi = st.slider("Input Bmi: ", 10, 100)
 
-    # User input data
+    
     data = {
         "gender": gender,
         "work_type": work_type,
@@ -52,34 +52,28 @@ def get_pred():
 
 
     if st.button("Predict"):
-        # Convert input data to a DataFrame
         X = pd.DataFrame([data])
-
-
+        
         encoded_features = encoder.transform(X[categorical_features])
 
-
         feature_names = encoder.get_feature_names_out(input_features=categorical_features)
 
-
         encoded_df = pd.DataFrame(encoded_features, columns=feature_names)
+        
         X_encoded = pd.concat([X.drop(columns=categorical_features), encoded_df], axis=1)
 
-
         prediction_proba = lgb1.predict_proba(X_encoded)
 
-
         explainer = shap.TreeExplainer(lgb1)
+        
         shap_values = explainer.shap_values(X_encoded)
 
-
         probability = prediction_proba[0, 1]  # Assuming binary classification
         st.subheader(f"The predicted probability of stroke is {probability}.")
         st.subheader("IF you see result , higher than 0.3, we advice you to see a doctor")
         st.header("Shap forceplot")
         st.subheader("Features values impact on model made prediction")
 
-
         shap.force_plot(explainer.expected_value[1], shap_values[1], features=X_encoded.iloc[0, :], matplotlib=True)