Update app.py

app.py

@@ -7,40 +7,42 @@ import numpy as np
 import pickle
 import streamlit as st
 
+# Dictionary to hold different model names and their corresponding file paths
+models = {
+    "Logistic Regression": "LogisticRegression_model.pkl",
+    "Decision Tree Classifier": "DecisionTreeClassifier_model.pkl",
+    "Random Forest Classifier": "RandomForestClassifier_model.pkl",
+    "SVC": "SVC_model.pkl"
+}
 
-# loading the saved model
-loaded_model = pickle.load(open('LogisticRegression_model.pkl', 'rb'))
+# Load the default model (Logistic Regression in this case)
+selected_model = "Logistic Regression"
+loaded_model = pickle.load(open(models[selected_model], 'rb'))
 
-
-# creating a function for Prediction
-
-def diabetes_prediction(input_data):
+# Function for making predictions
+def diabetes_prediction(input_data, model):
+    # Load the selected model
+    loaded_model = pickle.load(open(models[model], 'rb'))
     
-
-    # changing the input_data to numpy array
+    # Convert input_data to numpy array
     input_data_as_numpy_array = np.asarray(input_data)
-
-    # reshape the array as we are predicting for one instance
-    input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)
-
+    input_data_reshaped = input_data_as_numpy_array.reshape(1, -1)
+    
     prediction = loaded_model.predict(input_data_reshaped)
-    print(prediction)
 
-    if (prediction[0] == 0):
-      return 'The person is not diabetic'
+    if prediction[0] == 0:
+        return 'The person is not diabetic'
     else:
-      return 'The person is diabetic'
-  
-    
-  
+        return 'The person is diabetic'
+
+# Main function for the Streamlit app
 def main():
-    
-    # giving a title
     st.title('Diabetes Prediction Web App')
     
+    # Dropdown for model selection
+    selected_model = st.selectbox("Select Model", list(models.keys()))
     
-    # getting the input data from the user
-    
+    # Input fields for user data
     Pregnancies = st.text_input('Number of Pregnancies')
     Glucose = st.text_input('Glucose Level')
     BloodPressure = st.text_input('Blood Pressure Value')
@@ -50,15 +52,11 @@ def main():
     DiabetesPedigreeFunction = st.text_input('Diabetes Pedigree Function Value')
     Age = st.text_input('Age of the Person')
     
-    
-        # code for Prediction
     diagnosis = ''
 
-    # creating a button for Prediction
-
+    # Prediction button
     if st.button('Diabetes Test Result'):
         try:
-            # Convert input data to floating-point numbers with error handling
             input_data = [
                 float(Pregnancies),
                 float(Glucose),
@@ -69,11 +67,11 @@ def main():
                 float(DiabetesPedigreeFunction),
                 float(Age)
             ]
-            diagnosis = diabetes_prediction(input_data)
+            diagnosis = diabetes_prediction(input_data, selected_model)
         except ValueError as e:
             diagnosis = "Invalid input. Please enter numeric values for all fields."
 
     st.success(diagnosis)
 
 if __name__ == '__main__':
-    main()
+    main()