Update NdR_disease.py

NdR_disease.py

@@ -196,6 +196,10 @@ def run_disease_train():
     return linear_model, neural_model, scaler, conditions, num_classes
 
 # Function to get user input and make predictions
+import streamlit as st
+import numpy as np
+import torch
+
 def get_user_input_and_predict(linear_model, neural_model, scaler, conditions, num_classes):
     st.write("\nAdjust the sliders for the following symptoms on a scale from 0 (none) to 10 (severe):")
     
@@ -214,29 +218,28 @@ def get_user_input_and_predict(linear_model, neural_model, scaler, conditions, n
     interaction_term2 = user_features[0] * user_features[4]  # Fever * Nausea
     user_features.extend([interaction_term, interaction_term2])
     
-    # Normalize features
-    user_features = scaler.transform([user_features])
-    user_tensor = torch.from_numpy(user_features).float()
-    
-    # Random prediction
-    random_pred = np.random.randint(num_classes)
-    st.write(f"\nRandom Prediction: {conditions[random_pred]}")
-    
-    # Linear Model Prediction
-    linear_model.eval()
-    with torch.no_grad():
-        outputs = linear_model(user_tensor)
-        _, predicted = torch.max(outputs.data, 1)
-        linear_pred = predicted.item()
-    st.write(f"Linear Model Prediction: {conditions[linear_pred]}")
-    
-    # Neural Network Prediction
-    neural_model.eval()
-    with torch.no_grad():
-        outputs = neural_model(user_tensor)
-        _, predicted = torch.max(outputs.data, 1)
-        neural_pred = predicted.item()
-    st.write(f"Neural Network Prediction: {conditions[neural_pred]}")
-
-linear_model, neural_model, scaler, conditions, num_classes = run_disease_train()
-get_user_input_and_predict(linear_model, neural_model, scaler, conditions, num_classes)
+    # Add a button to trigger the predictions
+    if st.button('Calculate Predictions'):
+        # Normalize features
+        user_features = scaler.transform([user_features])
+        user_tensor = torch.from_numpy(user_features).float()
+        
+        # Random prediction
+        random_pred = np.random.randint(num_classes)
+        st.write(f"\nRandom Prediction: {conditions[random_pred]}")
+        
+        # Linear Model Prediction
+        linear_model.eval()
+        with torch.no_grad():
+            outputs = linear_model(user_tensor)
+            _, predicted = torch.max(outputs.data, 1)
+            linear_pred = predicted.item()
+        st.write(f"Linear Model Prediction: {conditions[linear_pred]}")
+        
+        # Neural Network Prediction
+        neural_model.eval()
+        with torch.no_grad():
+            outputs = neural_model(user_tensor)
+            _, predicted = torch.max(outputs.data, 1)
+            neural_pred = predicted.item()
+        st.write(f"Neural Network Prediction: {conditions[neural_pred]}")