Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,49 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import joblib
+import numpy as np
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+# 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")