Update app.py

app.py

@@ -8,48 +8,34 @@ encoder = joblib.load("encoder_d.joblib")
 scaler = joblib.load("scaler.joblib")
 
 # Streamlit app
-st.title("Diabetes Risk Prediction Tool")
+st.title("🔍 Smart Diabetes Risk Assessment System")
 st.write("Provide the following details to assess risk factors for diabetes.")
 
-# Row 1
-col1, col2, col3, col4, col5 = st.columns(5)
+# Create 3 columns
+col1, col2, col3 = st.columns(3)
+
 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:
+    fasting_blood_sugar = st.number_input("Fasting Blood Sugar (mg/dL):", min_value=50, max_value=300, step=1)
+    hba1c = st.number_input("HBA1C (%):", min_value=3.0, max_value=15.0, step=0.1)
+    age = st.number_input("Age (years):", min_value=1, max_value=100, step=1)
+
+with col2:
     physical_activity = st.selectbox("Physical Activity Level:", options=encoder["Physical_Activity"].classes_)
     physical_activity = encoder["Physical_Activity"].transform([physical_activity])[0]
-with col4:
+    postprandial_blood_sugar = st.number_input("Postprandial Blood Sugar (mg/dL):", min_value=50, max_value=400, step=1)
+    waist_hip_ratio = st.number_input("Waist-to-Hip Ratio:", min_value=0.5, max_value=2.0, step=0.01)
+    vitamin_d_level = st.number_input("Vitamin D Level (ng/mL):", min_value=5.0, max_value=100.0, step=0.1)
+
+with col3:
     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
@@ -59,10 +45,13 @@ values = [bmi, family_history, physical_activity, diet_type, stress_level, fasti
 
 # Submit button
 if st.button("Submit"):
+    # Preprocess input data
     values = scaler.transform([values])
     prediction = model.predict(values)
 
+    # Display result
     if prediction == 1:
-        st.success("Risk Detected: The person is at risk of diabetes.")
+        st.error("⚠️ **Risk Alert:** Based on the input data, there is a significant likelihood that the person may be at risk of developing diabetes. It is recommended to consult with a healthcare professional for further evaluation and possible diagnostic tests.")
     else:
-        st.success("No Risk Detected: The person is not at risk of diabetes.")
+        st.success("✅ **Good News:** Based on the input data, there appears to be no immediate risk of diabetes. Maintaining a healthy lifestyle and regular check-ups are still important for long-term wellness.")
+