Update app.py

app.py

@@ -1,85 +1,166 @@
 import streamlit as st
 import pandas as pd
 import joblib
+from PIL import Image
+
+# Custom styling for the app
+st.markdown(
+    """
+    <style>
+    .main {
+        background-color: #f4f4f9;
+    }
+    .stButton > button {
+        background-color: #4CAF50;
+        color: white;
+        border-radius: 8px;
+        padding: 10px 20px;
+        font-size: 16px;
+    }
+    .stButton > button:hover {
+        background-color: #45a049;
+        color: white;
+    }
+    .stSidebar {
+        background-color: #F7F7F7;
+    }
+    .header {
+        font-size: 36px;
+        font-weight: bold;
+        color: #4CAF50;
+        text-align: center;
+    }
+    .subheader {
+        font-size: 20px;
+        color: #333;
+        margin-top: 0;
+    }
+    .footer {
+        font-size: 14px;
+        color: #666;
+        text-align: center;
+        margin-top: 30px;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True,
+)
 
 # Load the model
 @st.cache_resource
 def load_model():
-    return joblib.load('best_model.pkl')
+    return joblib.load("best_model.pkl")
+
 
 model = load_model()
 
-# Streamlit app title and description
-st.title("Retention Probability Predictor")
-st.markdown("""
-Predict the probability of retention based on patient and treatment details. 
-Provide the required inputs below to see the prediction.
-""")
+# Sidebar customization
+with st.sidebar:
+    st.image("MAT_primary-logo@2x.jpg", use_container_width=True)
+    st.header("Retention Predictor")
+    st.write("Enter the required details below to predict retention probability.")
 
-# Sidebar for inputs
-st.sidebar.header("Input Features")
+# Main app title
+st.markdown('<div class="header">Medication Retention/Discontinuation Probability Predictor</div>', unsafe_allow_html=True)
+#st.markdown('<p class="subheader">Analyze patient retention using advanced ML models.</p>', unsafe_allow_html=True)
 
 # Input fields for features
-st.sidebar.subheader("Episode Details")
+st.sidebar.subheader("Patient Episode Details")
 previous_bup_episodes = st.sidebar.number_input(
-    "Number of Previous Buprenorphine Episodes:", min_value=0, value=20, step=1
+    "Number of Previous Buprenorphine Episodes", min_value=0, value=20, step=1
 )
 previous_failed_bup_episodes = st.sidebar.number_input(
-    "Number of Previous Failed Buprenorphine Episodes:", min_value=0, value=20, step=1
+    "Number of Previous Failed Buprenorphine Episodes", min_value=0, value=20, step=1
 )
 
-st.sidebar.subheader("Days Details")
+st.sidebar.subheader("Duration Details")
 days_covered = st.sidebar.number_input(
-    "Days Covered in Current Episode:", min_value=0, value=30, step=1
+    "Days Covered in Current Episode", min_value=0, value=30, step=1
 )
 days_since_start = st.sidebar.number_input(
-    "Days Since Start of the Episode:", min_value=1, value=90, step=1
+    "Days Since Start of the Episode", min_value=1, value=90, step=1
 )
 
 st.sidebar.subheader("Other Features")
 prior_BUP_days_supply = st.sidebar.number_input(
-    "Prior BUP Days Supply:", min_value=0, value=30, step=1
+    "Prior BUP Days Supply", min_value=0, value=30, step=1
 )
 YearOfInitiation = st.sidebar.number_input(
-    "Year of Initiation:", min_value=1900, max_value=2100, value=2023, step=1
+    "Year of Initiation", min_value=1900, max_value=2100, value=2023, step=1
 )
 Previous_Meth_Episodes = st.sidebar.radio(
-    "Previous Methadone Episodes (Yes/No):",
-    options=[0, 1],  # Binary options
-    format_func=lambda x: "Yes" if x == 1 else "No"  # Display "Yes" or "No" instead of 0/1
+    "Previous Methadone Episodes (Yes/No)",
+    options=[0, 1],
+    format_func=lambda x: "Yes" if x == 1 else "No",
 )
 
-# Calculate derived features
-if st.sidebar.button("Predict Retention Probability"):
-    # Calculate Failed Buprenorphine Rate
+# Calculate derived features and predict
+if st.sidebar.button("🔍 Predict Retention Probability"):
+    # Calculate derived features
     if previous_bup_episodes > 0:
         Failed_Bup_Rate = previous_failed_bup_episodes / previous_bup_episodes
     else:
-        Failed_Bup_Rate = 0.0  # No prior episodes, so no failures
+        Failed_Bup_Rate = 0.0
 
-    # Calculate Proportion of Days Covered
     if days_since_start > 0:
         proportionofDaysCovered = days_covered / days_since_start
     else:
-        proportionofDaysCovered = 0.0  # Avoid division by zero
+        proportionofDaysCovered = 0.0
 
-    # Prepare input data for prediction
-    input_data = pd.DataFrame({
-        'proportionofDaysCovered': [proportionofDaysCovered],
-        'Failed_Bup_Rate': [Failed_Bup_Rate],
-        'prior_BUP_days_supply': [prior_BUP_days_supply],
-        'YearOfInitiation': [YearOfInitiation],
-        'Previous_Meth_Episodes': [Previous_Meth_Episodes]
-    })
+    # Prepare input data
+    input_data = pd.DataFrame(
+        {
+            "proportionofDaysCovered": [proportionofDaysCovered],
+            "Failed_Bup_Rate": [Failed_Bup_Rate],
+            "prior_BUP_days_supply": [prior_BUP_days_supply],
+            "YearOfInitiation": [YearOfInitiation],
+            "Previous_Meth_Episodes": [Previous_Meth_Episodes],
+        }
+    )
 
     # Make prediction
-    retention_probability = model.predict_proba(input_data)[:, 1][0]
+    proba_output = model.predict_proba(input_data)
+    print("Predict_proba output shape:", proba_output.shape)
 
-    # Display the prediction
-    st.subheader("Prediction Result")
-    st.success(f"The predicted retention probability is: **{retention_probability:.2f}**")
+    # Handle both 1D and 2D outputs
+    retention_probability = (
+        proba_output[:, 1][0] if proba_output.ndim == 2 else proba_output[0]
+    )
 
-    # Display calculated features
-    st.subheader("Calculated Features")
-    st.write(f"**Failed Buprenorphine Rate:** {Failed_Bup_Rate:.2%}")
-    st.write(f"**Proportion of Days Covered:** {proportionofDaysCovered:.2%}")
+    # Display the prediction result in a styled card
+    st.markdown(
+        f"""
+        <div style="padding: 20px; border: 1px solid #4CAF50; border-radius: 8px; background-color: #FFFFFF;">
+            <h2 style="color: #4CAF50; text-align: center;">Prediction Result</h2>
+            <p style="font-size: 18px; color: #333; text-align: center;">
+                The predicted retention probability is:
+            </p>
+            <p style="font-size: 28px; font-weight: bold; color: #4CAF50; text-align: center;">
+                {retention_probability:.2f}
+            </p>
+        </div>
+        """,
+        unsafe_allow_html=True,
+    )
+
+    # Display calculated features in a visually appealing manner
+    st.markdown("### **Calculated Features**")
+    st.markdown(
+        f"""
+        <ul style="font-size: 18px; color: #333;">
+            <li><strong>Failed Buprenorphine Rate:</strong> {Failed_Bup_Rate:.2%}</li>
+            <li><strong>Proportion of Days Covered:</strong> {proportionofDaysCovered:.2%}</li>
+        </ul>
+        """,
+        unsafe_allow_html=True,
+    )
+
+# Footer section
+st.markdown(
+    """
+    <div style="text-align: center; font-size: 14px; color: #666; margin-top: 30px;">
+        Developed by <strong>Nahiyan Noor</strong>
+    </div>
+    """,
+    unsafe_allow_html=True,
+)