Consumer_App/app.py at main · Harshita-Pawar/Consumer_App · GitHub

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import streamlit as st
import joblib
import plotly.graph_objects as go
import numpy as np
import pandas as pd

# Load model, scaler, and features
model = joblib.load("purchase_model.pkl")
scaler = joblib.load("scaler.pkl")
feature_names = joblib.load("feature_names.pkl")

st.title("🛒 Consumer Purchase Propensity Prediction")
st.write("Predict whether a customer is likely to make a purchase based on their online behavior.")

# Input fields
st.subheader("📋 Enter Customer Details:")
time_on_site = st.number_input("Time on Site (seconds)", min_value=0)
pages_viewed = st.number_input("Pages Viewed", min_value=0)
cart_additions = st.number_input("Cart Additions", min_value=0)
ad_clicked = st.selectbox("Ad Clicked", [False, True])
discount_viewed = st.selectbox("Discount Viewed", [False, True])
previous_purchases = st.number_input("Previous Purchases", min_value=0)
session_duration = st.number_input("Session Duration (seconds)", min_value=0)
gender = st.selectbox("Gender", ["Male", "Female"])
region = st.selectbox("Region", ["North", "South", "East", "West"])
device_type = st.selectbox("Device Type", ["Mobile", "Tablet", "Desktop"])

# Prepare input data (no manual encoding)
input_df = pd.DataFrame({
    "Time_on_Site": [time_on_site],
    "Pages_Viewed": [pages_viewed],
    "Cart_Additions": [cart_additions],
    "Ad_Clicked": [int(ad_clicked)],
    "Discount_Viewed": [int(discount_viewed)],
    "Previous_Purchases": [previous_purchases],
    "Session_Duration": [session_duration],
    "Gender": [gender],
    "Region": [region],
    "Device_Type": [device_type]
})

# Apply same encoding as training
input_df = pd.get_dummies(input_df, columns=["Gender", "Region", "Device_Type"], drop_first=True)

# Add missing columns if any
for col in feature_names:
    if col not in input_df.columns:
        input_df[col] = 0

# Reorder columns
input_df = input_df[feature_names]

# Scale numeric features
num_cols = ["Time_on_Site", "Pages_Viewed", "Cart_Additions", "Previous_Purchases", "Session_Duration"]
input_df[num_cols] = scaler.transform(input_df[num_cols])

# Predict
import plotly.graph_objects as go

# Predict
if st.button("🔍 Predict Purchase Propensity"):
    prediction = model.predict(input_df)[0]
    prob = model.predict_proba(input_df)[0][1]
    prob_percent = prob * 100

    # Text output
    if prediction == 1:
        st.success(f"✅ This customer is likely to make a purchase! (Confidence: {prob:.2f})")
    else:
        st.warning(f"❌ This customer is unlikely to make a purchase. (Confidence: {prob:.2f})")

    # Thin-needle gauge
    fig = go.Figure(go.Indicator(
        mode="gauge+number",
        value=prob_percent,
        domain={'x': [0, 1], 'y': [0, 1]},
        title={'text': "🛒 Purchase Probability (%)", 'font': {'size': 24}},
        number={'font': {'size': 28, 'color': 'darkblue'}},
        gauge={
            'axis': {'range': [0, 100]},
            'bar': {'color': "rgba(0,0,0,0)"},  # hide thick bar
            'steps': [
                {'range': [0, 50], 'color': 'red'},
                {'range': [50, 75], 'color': 'yellow'},
                {'range': [75, 100], 'color': 'green'}
            ],
            'threshold': {
                'line': {'color': 'black', 'width': 6},  # thin needle
                'thickness': 1.0,
                'value': prob_percent
            }
        }
    ))

    st.plotly_chart(fig, use_container_width=True)