Update app.py

app.py

@@ -1,8 +1,3 @@
-# -*- coding: utf-8 -*-
-"""
-Diabetes Prediction Web App
-"""
-
 import numpy as np
 import pickle
 import streamlit as st
@@ -15,34 +10,27 @@ models = {
     "SVC": "SVC_model.pkl"
 }
 
-# Load the default model (Logistic Regression in this case)
-selected_model = "Logistic Regression"
-loaded_model = pickle.load(open(models[selected_model], 'rb'))
+def load_model(model_name):
+    model_path = models.get(model_name)
+    if model_path:
+        return pickle.load(open(model_path, 'rb'))
+    return None
 
-# Function for making predictions
 def diabetes_prediction(input_data, model):
-    # Load the selected model
-    loaded_model = pickle.load(open(models[model], 'rb'))
-    
-    # Convert input_data to numpy array
-    input_data_as_numpy_array = np.asarray(input_data)
-    input_data_reshaped = input_data_as_numpy_array.reshape(1, -1)
-    
-    prediction = loaded_model.predict(input_data_reshaped)
+    prediction = model.predict([input_data])
+    return 'The person is diabetic' if prediction[0] else 'The person is not diabetic'
 
-    if prediction[0] == 0:
-        return 'The person is not diabetic'
-    else:
-        return 'The person is diabetic'
-
-# Main function for the Streamlit app
 def main():
     st.title('Diabetes Prediction Web App')
+
+    selected_model_name = st.selectbox("Choose Prediction Method (Default: Logistic Regression)", list(models.keys()))
+
+    loaded_model = load_model(selected_model_name)
     
-    # Dropdown for model selection
-    selected_model = st.selectbox("Choose Prediction Method (Default: Logistic Regression)", list(models.keys()))
+    if loaded_model is None:
+        st.error("Model not found!")
+        return
     
-    # 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')
@@ -51,32 +39,23 @@ def main():
     BMI = st.text_input('BMI Value')
     DiabetesPedigreeFunction = st.text_input('Diabetes Pedigree Function Value')
     Age = st.text_input('Age of the Person')
-    
-    diagnosis = ''
 
-    # Prediction button
+    diagnosis = ''
     if st.button('Diabetes Test Result'):
         try:
-            input_data = [
-                float(Pregnancies),
-                float(Glucose),
-                float(BloodPressure),
-                float(SkinThickness),
-                float(Insulin),
-                float(BMI),
-                float(DiabetesPedigreeFunction),
-                float(Age)
-            ]
-            diagnosis = diabetes_prediction(input_data, selected_model)
-        except ValueError as e:
+            input_data = np.array([
+                float(Pregnancies), float(Glucose), float(BloodPressure),
+                float(SkinThickness), float(Insulin), float(BMI),
+                float(DiabetesPedigreeFunction), float(Age)
+            ])
+            diagnosis = diabetes_prediction(input_data, loaded_model)
+        except ValueError:
             diagnosis = "Invalid input. Please enter numeric values for all fields."
 
     st.success(diagnosis)
 
-    # Dummy Entries or Demo for showcasing
     st.markdown('## Demo - Test with Dummy Entries')
     
-    # You can set default values for the inputs to showcase the prediction functionality
     default_values = {
         "Number of Pregnancies": 5,
         "Glucose Level": 130,
@@ -88,20 +67,14 @@ def main():
         "Age of the Person": 40
     }
     
-    demo_button = st.button('Run Demo')
-
-    if demo_button:
-        demo_input_data = [
-            default_values["Number of Pregnancies"],
-            default_values["Glucose Level"],
-            default_values["Blood Pressure Value"],
-            default_values["Skin Thickness Value"],
-            default_values["Insulin Level"],
-            default_values["BMI Value"],
-            default_values["Diabetes Pedigree Function Value"],
-            default_values["Age of the Person"]
-        ]
-        demo_diagnosis = diabetes_prediction(demo_input_data, selected_model)
+    if st.button('Run Demo'):
+        demo_input_data = np.array([
+            default_values["Number of Pregnancies"], default_values["Glucose Level"],
+            default_values["Blood Pressure Value"], default_values["Skin Thickness Value"],
+            default_values["Insulin Level"], default_values["BMI Value"],
+            default_values["Diabetes Pedigree Function Value"], default_values["Age of the Person"]
+        ])
+        demo_diagnosis = diabetes_prediction(demo_input_data, loaded_model)
         st.success(f'Demo Result: {demo_diagnosis}')
 
 if __name__ == '__main__':