Update app.py

app.py

@@ -1,115 +1,117 @@
 import streamlit as st
 import joblib
 import pandas as pd
+
+# Custom CSS
 st.markdown(
     """
     <style>
-    /* Set background image for the entire app */
     .stApp {
         background: url('https://mandayahospitalgroup.com/wp-content/uploads/2024/05/diabetes.jpg') no-repeat center center fixed;
         background-size: cover;
     }
-     .stApp h1 {
+    .stApp h1 {
         background-color: rgba(0, 0, 128, 0.7); 
         color: #ffffff; 
         padding: 10px;
         border-radius: 5px;
         font-size: 2.2em; 
         text-align: center;
-        white-space: nowrap; /* Prevents line break */
+        white-space: nowrap;
         overflow: hidden;
         text-overflow: ellipsis;
         max-width: 100%;
         margin: 0 auto;
     }
-    /* Style for the button */
     .stButton>button {
-        background-color: #4CAF50; /* Green */
+        background-color: #4CAF50;
         color: white;
         font-size: 1.2em;
         border-radius: 10px;
         padding: 10px 24px;
         border: none;
     }
-    /* Center the button */
     .stButton {
         display: flex;
         justify-content: center;
     }
-    /* Style for the output container */
-    .output-container {
-        background-color: lightpink;
-        color: black;
+    .positive-result {
+        background-color: rgba(0, 128, 0, 0.8);
+        color: white;
         font-size: 1.5em;
-        padding: 15px;
-        border-radius: 10px;
-        margin-top: 20px;
-        box-shadow: 0 4px 8px rgba(0,0,0,0.1);
-        width: 200%;
-        margin-left: auto;
-        margin-right: auto;
+        padding: 20px;
+        border-radius: 12px;
+        margin-top: 25px;
         text-align: center;
+        box-shadow: 0 4px 10px rgba(0, 0, 0, 0.2);
     }
-        </style>
+    .negative-result {
+        background-color: rgba(220, 20, 60, 0.85);
+        color: white;
+        font-size: 1.5em;
+        padding: 20px;
+        border-radius: 12px;
+        margin-top: 25px;
+        text-align: center;
+        box-shadow: 0 4px 10px rgba(0, 0, 0, 0.2);
+    }
+    </style>
     """,
     unsafe_allow_html=True
 )
 
-
 # Load models
 model = joblib.load("lr_model.joblib")
 encoder = joblib.load("encoder_d.joblib")
 scaler = joblib.load("scaler.joblib")
 
-# Streamlit app
+# Title
 st.title("🔍 Smart Diabetes Risk Assessment System")
 st.write("Provide the following details to assess risk factors for diabetes.")
 
-# Create 3 columns
+# Input 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)
-    family_history = st.selectbox("Family History of Diabetes:", options=encoder["Family_History"].classes_)
+    bmi = st.number_input("Body Mass Index (BMI):", 10.0, 50.0, step=0.1)
+    family_history = st.selectbox("Family History of Diabetes:", encoder["Family_History"].classes_)
     family_history = encoder["Family_History"].transform([family_history])[0]
-    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)
+    fasting_blood_sugar = st.number_input("Fasting Blood Sugar (mg/dL):", 50, 300, step=1)
+    hba1c = st.number_input("HBA1C (%):", 3.0, 15.0, step=0.1)
+    age = st.number_input("Age (years):", 1, 100, step=1)
 
 with col2:
-    physical_activity = st.selectbox("Physical Activity Level:", options=encoder["Physical_Activity"].classes_)
+    physical_activity = st.selectbox("Physical Activity Level:", encoder["Physical_Activity"].classes_)
     physical_activity = encoder["Physical_Activity"].transform([physical_activity])[0]
-    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)
+    postprandial_blood_sugar = st.number_input("Postprandial Blood Sugar (mg/dL):", 50, 400, step=1)
+    waist_hip_ratio = st.number_input("Waist-to-Hip Ratio:", 0.5, 2.0, step=0.01)
+    vitamin_d_level = st.number_input("Vitamin D Level (ng/mL):", 5.0, 100.0, step=0.1)
 
 with col3:
-    diet_type = st.selectbox("Diet Type:", options=encoder["Diet_Type"].classes_)
+    diet_type = st.selectbox("Diet Type:", 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 = st.selectbox("Stress Level:", encoder["Stress_Level"].classes_)
     stress_level = encoder["Stress_Level"].transform([stress_level])[0]
-    glucose_tolerance = st.number_input("Glucose Tolerance Test Result (mg/dL):", min_value=50, max_value=300, step=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)
+    glucose_tolerance = st.number_input("Glucose Tolerance Test Result (mg/dL):", 50, 300, step=1)
+    c_protein_level = st.number_input("C-Reactive Protein Level (mg/L):", 0.1, 20.0, step=0.1)
+    cholesterol_level = st.number_input("Cholesterol Level (mg/dL):", 100, 400, step=1)
 
-# Collect input values into a list
+# Prediction logic
 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])
-    prediction = model.predict(values)
+    scaled_values = scaler.transform([values])
+    prediction = model.predict(scaled_values)
 
-    # Display result
-    if prediction == 1:
-        st.markdown('<div class="result-container">⚠️ **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.</div>', unsafe_allow_html=True)
-        st.success("")
-        # 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.")
+    if prediction[0] == 1:
+        st.markdown(
+            '<div class="negative-result">⚠️ <strong>Risk Alert:</strong> Based on the input data, there is a <strong>significant likelihood</strong> of diabetes. Please consult a healthcare provider for further evaluation.</div>',
+            unsafe_allow_html=True
+        )
     else:
-        st.markdown('<div class="result-container">✅ **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.</div>', unsafe_allow_html=True)
-        st.success("")
-        # 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.")
-
+        st.markdown(
+            '<div class="positive-result">✅ <strong>Good News:</strong> Based on the input data, there appears to be <strong>no immediate risk</strong> of diabetes. Keep maintaining a healthy lifestyle!</div>',
+            unsafe_allow_html=True
+        )