Create app.py

app.py

@@ -0,0 +1,49 @@
+import streamlit as st
+import joblib
+import numpy as np
+
+# Load the trained model and scaler
+# Use @st.cache_resource to load them only once for performance
+@st.cache_resource
+def load_model():
+    model = joblib.load('svm_model.pkl')
+    scaler = joblib.load('scaler.pkl')
+    return model, scaler
+
+try:
+    model, scaler = load_model()
+except FileNotFoundError:
+    st.error("Model files not found. Please run train_model.py first.")
+    st.stop()
+
+st.title("Purchase Intention Predictor")
+st.write("Adjust the sliders below to predict the user's Purchase Intention (PI).")
+
+# Sidebar for inputs
+st.sidebar.header("User Inputs")
+
+# Create sliders for each feature based on the data's 1-7 scale
+att = st.sidebar.slider("Attitude (ATT)", min_value=1.0, max_value=7.0, value=4.0, step=0.1)
+sns = st.sidebar.slider("Subjective Norms (SNs)", min_value=1.0, max_value=7.0, value=4.0, step=0.1)
+pbc = st.sidebar.slider("Perceived Behavioral Control (PBC)", min_value=1.0, max_value=7.0, value=4.0, step=0.1)
+eo = st.sidebar.slider("Environmental Outcome (EO)", min_value=1.0, max_value=7.0, value=4.0, step=0.1)
+ec = st.sidebar.slider("Environmental Concern (EC)", min_value=1.0, max_value=7.0, value=4.0, step=0.1)
+
+# Prepare input data
+input_data = np.array([[att, sns, pbc, eo, ec]])
+
+# Scale the input
+input_scaled = scaler.transform(input_data)
+
+# Predict
+if st.button("Predict Purchase Intention"):
+    prediction = model.predict(input_scaled)
+    st.subheader(f"Predicted Purchase Intention Score: {prediction[0]:.2f}")
+    
+    # Optional: Interpretation
+    if prediction[0] > 5.5:
+        st.success("High Purchase Intention")
+    elif prediction[0] < 3.5:
+        st.warning("Low Purchase Intention")
+    else:
+        st.info("Moderate Purchase Intention")