Update app.py

app.py

@@ -1,49 +1,68 @@
-import streamlit as st
-import joblib
-import pandas as pd
-
-# Load models
-model = joblib.load("lr_model.joblib")
-encoder = joblib.load("encoder_d.joblib")
-scaler = joblib.load("scaler.joblib")
-
-# Streamlit app
-st.title("Diabetes Risk Prediction Tool")
-st.write("Provide the following details to assess risk factors for diabetes.")
-
-# User input fields
-bmi = st.number_input("Body Mass Index (BMI):", min_value=10.0, max_value=50.0, step=0.1)
-family_history = st.selectbox("Family History of Diabetes:", options=encoder["Family_History"].classes_)
-family_history = encoder["Family_History"].transform([family_history])[0]
-physical_activity = st.selectbox("Physical Activity Level:", options=encoder["Physical_Activity"].classes_)
-physical_activity = encoder["Physical_Activity"].transform([physical_activity])[0]
-diet_type = st.selectbox("Diet Type:", options=encoder["Diet_Type"].classes_)
-diet_type = encoder["Diet_Type"].transform([diet_type])[0]
-stress_level = st.selectbox("Stress Level:", options=encoder["Stress_Level"].classes_)
-stress_level = encoder["Stress_Level"].transform([stress_level])[0]
-fasting_blood_sugar = st.number_input("Fasting Blood Sugar (mg/dL):", min_value=50, max_value=300, step=1)
-postprandial_blood_sugar = st.number_input("Postprandial Blood Sugar (mg/dL):", min_value=50, max_value=400, step=1)
-hba1c = st.number_input("HBA1C (%):", min_value=3.0, max_value=15.0, step=0.1)
-waist_hip_ratio = st.number_input("Waist-to-Hip Ratio:", min_value=0.5, max_value=2.0, step=0.01)
-glucose_tolerance = st.number_input("Glucose Tolerance Test Result (mg/dL):", min_value=50, max_value=300, step=1)
-age = st.number_input("Age (years):", min_value=1, max_value=100, step=1)
-vitamin_d_level = st.number_input("Vitamin D Level (ng/mL):", min_value=5.0, max_value=100.0, step=0.1)
-c_protein_level = st.number_input("C-Reactive Protein Level (mg/L):", min_value=0.1, max_value=20.0, step=0.1)
-cholesterol_level = st.number_input("Cholesterol Level (mg/dL):", min_value=100, max_value=400, step=1)
-
-# Collect input values into a list
-values = [bmi, family_history, physical_activity, diet_type, stress_level, fasting_blood_sugar,
-          postprandial_blood_sugar, hba1c, waist_hip_ratio, glucose_tolerance, age,
-          vitamin_d_level, c_protein_level, cholesterol_level]
-
-# Submit button
-if st.button("Submit"):
-    # Preprocess input data
-    values = scaler.transform([values])  # Apply scaling
-    prediction = model.predict(values)  # Make prediction
-    
-    # Display result
-    if prediction == 1:
-        st.success("Risk Detected: The person is at risk of diabetes.")
-    else:
-        st.success("No Risk Detected: The person is not at risk of diabetes.")
+import streamlit as st
+import joblib
+import pandas as pd
+
+# Load models
+model = joblib.load("lr_model.joblib")
+encoder = joblib.load("encoder_d.joblib")
+scaler = joblib.load("scaler.joblib")
+
+# Streamlit app
+st.title("Diabetes Risk Prediction Tool")
+st.write("Provide the following details to assess risk factors for diabetes.")
+
+# Row 1
+col1, col2, col3, col4, col5 = st.columns(5)
+with col1:
+    bmi = st.number_input("Body Mass Index (BMI):", min_value=10.0, max_value=50.0, step=0.1)
+with col2:
+    family_history = st.selectbox("Family History of Diabetes:", options=encoder["Family_History"].classes_)
+    family_history = encoder["Family_History"].transform([family_history])[0]
+with col3:
+    physical_activity = st.selectbox("Physical Activity Level:", options=encoder["Physical_Activity"].classes_)
+    physical_activity = encoder["Physical_Activity"].transform([physical_activity])[0]
+with col4:
+    diet_type = st.selectbox("Diet Type:", options=encoder["Diet_Type"].classes_)
+    diet_type = encoder["Diet_Type"].transform([diet_type])[0]
+with col5:
+    stress_level = st.selectbox("Stress Level:", options=encoder["Stress_Level"].classes_)
+    stress_level = encoder["Stress_Level"].transform([stress_level])[0]
+
+# Row 2
+col6, col7, col8, col9, col10 = st.columns(5)
+with col6:
+    fasting_blood_sugar = st.number_input("Fasting Blood Sugar (mg/dL):", min_value=50, max_value=300, step=1)
+with col7:
+    postprandial_blood_sugar = st.number_input("Postprandial Blood Sugar (mg/dL):", min_value=50, max_value=400, step=1)
+with col8:
+    hba1c = st.number_input("HBA1C (%):", min_value=3.0, max_value=15.0, step=0.1)
+with col9:
+    waist_hip_ratio = st.number_input("Waist-to-Hip Ratio:", min_value=0.5, max_value=2.0, step=0.01)
+with col10:
+    glucose_tolerance = st.number_input("Glucose Tolerance Test Result (mg/dL):", min_value=50, max_value=300, step=1)
+
+# Row 3
+col11, col12, col13, col14 = st.columns(4)
+with col11:
+    age = st.number_input("Age (years):", min_value=1, max_value=100, step=1)
+with col12:
+    vitamin_d_level = st.number_input("Vitamin D Level (ng/mL):", min_value=5.0, max_value=100.0, step=0.1)
+with col13:
+    c_protein_level = st.number_input("C-Reactive Protein Level (mg/L):", min_value=0.1, max_value=20.0, step=0.1)
+with col14:
+    cholesterol_level = st.number_input("Cholesterol Level (mg/dL):", min_value=100, max_value=400, step=1)
+
+# Collect input values into a list
+values = [bmi, family_history, physical_activity, diet_type, stress_level, fasting_blood_sugar,
+          postprandial_blood_sugar, hba1c, waist_hip_ratio, glucose_tolerance, age,
+          vitamin_d_level, c_protein_level, cholesterol_level]
+
+# Submit button
+if st.button("Submit"):
+    values = scaler.transform([values])
+    prediction = model.predict(values)
+
+    if prediction == 1:
+        st.success("Risk Detected: The person is at risk of diabetes.")
+    else:
+        st.success("No Risk Detected: The person is not at risk of diabetes.")