Update app.py

app.py

@@ -19,18 +19,23 @@ X = pd.DataFrame([symptoms_dict], columns=column_list)
 st.write('Generated DataFrame:')
 st.write(X)
 
-# Unpickle classifier
-clf = joblib.load("self_diagnose_model.pkl")
-
-# Store inputs into dataframe
-X = pd.DataFrame([[
-    age, sex, high_chol, chol_check, bmi, smoker, hda, pa, fruit, veggies, hac, gh, mh, ph, dw, stroke, high_bp
-]], columns=preds)
-
-# Get prediction
-prediction = clf.predict(X)[0]
-
-st.text(prediction)
+if st.button("Submit"):
+    # Unpickle classifier
+    clf = joblib.load("self_diagnose_model.pkl")
+    
+    # Get prediction
+    prediction = clf.predict(X)[0]
+    
+    st.text("Predicted Disease:", prediction)
+    st.text("Confidence Scores (Probabilities for Each Class):")
+    st.write(confidence_scores)
+
+    # You can also display the top N predicted classes with their probabilities
+    top_n = 5  # Change this value to display more or fewer top classes
+    top_n_classes = np.argsort(-confidence_scores)[:top_n]
+    st.text("Top {} Predicted Diseases:".format(top_n))
+    for i, class_idx in enumerate(top_n_classes):
+        st.text(f"{clf.classes_[class_idx]}: {confidence_scores[class_idx]:.2f}")