Update src/streamlit_app.py

src/streamlit_app.py

@@ -3,7 +3,7 @@ import joblib
 import numpy as np
 import plotly.graph_objects as go
 
-# 1. Page Configuration (Must be the first command)
+# 1. Page Configuration
 st.set_page_config(
     page_title="Purchase Intention AI",
     page_icon="🛍️",
@@ -12,8 +12,14 @@ st.set_page_config(
 )
 
 # Custom CSS for styling
+# UPDATED: Added 'color: white;' to .main-header and .sub-text
 st.markdown("""
     <style>
+    /* Main background dark mode adjustment (optional, for contrast) */
+    .stApp {
+        background-color: #0E1117;
+    }
+    
     .stButton>button {
         width: 100%;
         background-color: #FF4B4B;
@@ -22,17 +28,22 @@ st.markdown("""
         padding: 0.5rem;
         border-radius: 10px;
     }
+    
+    /* TARGET 1: Main Title */
     .main-header {
         font-size: 2.5rem;
         font-weight: 700;
-        color: #333;
+        color: white !important; /* Forced white color */
         text-align: center;
-        margin-bottom: 2rem;
+        margin-bottom: 1rem;
     }
+    
+    /* TARGET 2: Sub-header text */
     .sub-text {
         text-align: center;
-        color: #666;
+        color: white !important; /* Forced white color */
         font-size: 1.1rem;
+        margin-bottom: 2rem;
     }
     </style>
     """, unsafe_allow_html=True)
@@ -40,16 +51,15 @@ st.markdown("""
 # 2. Load Model
 @st.cache_resource
 def load_model():
-    # Handles both the pipeline (smart) version and separate files version
     try:
         # Try loading the smart pipeline first
-        model = joblib.load('src/svm_model.pkl')
-        return model, None # No separate scaler needed
+        model = joblib.load('svm_pipeline_model.pkl')
+        return model, None 
     except:
         try:
             # Fallback to separate files
-            model = joblib.load('src/svm_model.pkl')
-            scaler = joblib.load('src/scaler.pkl')
+            model = joblib.load('svm_model.pkl')
+            scaler = joblib.load('scaler.pkl')
             return model, scaler
         except FileNotFoundError:
             return None, None
@@ -60,7 +70,7 @@ if model is None:
     st.error("🚨 Model files not found! Please run `train_model.py` first.")
     st.stop()
 
-# 3. Header Section
+# 3. Header Section (Applies the CSS classes defined above)
 st.markdown('<div class="main-header">🛍️ Purchase Intention Predictor</div>', unsafe_allow_html=True)
 st.markdown('<p class="sub-text">Adjust the psychometric drivers in the sidebar to predict user behavior.</p>', unsafe_allow_html=True)
 st.markdown("---")
@@ -95,17 +105,16 @@ input_values = np.array([[att, sns, pbc, eo, ec]])
 if scaler:
     final_input = scaler.transform(input_values)
 else:
-    final_input = input_values # Pipeline handles scaling internally
+    final_input = input_values 
 
-# Real-time Prediction (or on button click)
+# Real-time Prediction
 prediction = model.predict(final_input)[0]
-# Clip prediction to 1-7 range for visuals
 prediction = max(1.0, min(7.0, prediction))
 
 with col1:
     st.subheader("📊 Prediction Result")
     
-    # Gauge Chart for PI
+    # Gauge Chart
     fig_gauge = go.Figure(go.Indicator(
         mode = "gauge+number",
         value = prediction,
@@ -131,16 +140,16 @@ with col1:
 
     # Text Interpretation
     if prediction >= 5.5:
-        st.success("**High Probability**: User is very likely to purchase.")
+        st.success("High Probability: User is likely to purchase.")
     elif prediction >= 3.5:
-        st.info("**Moderate Probability**: User is undecided.")
+        st.info("Moderate Probability: User is undecided.")
     else:
-        st.warning("**Low Probability**: User is unlikely to purchase.")
+        st.warning("Low Probability: User is unlikely to purchase.")
 
 with col2:
     st.subheader("🕸️ User Profile Analysis")
     
-    # Radar Chart for Inputs
+    # Radar Chart
     categories = ['Attitude', 'Social Norms', 'Control', 'Outcome', 'Concern']
     r_values = [att, sns, pbc, eo, ec]