Create app.py

app.py

@@ -0,0 +1,60 @@
+# app.py
+
+import streamlit as st
+import pandas as pd
+import joblib
+
+# -----------------------------
+# Load Trained Model
+# -----------------------------
+@st.cache_resource
+def load_model():
+    return joblib.load("superkart_sales_forecast.pkl")
+
+model = load_model()
+
+# -----------------------------
+# Streamlit UI Setup
+# -----------------------------
+st.set_page_config(page_title="SuperKart Sales Forecast", layout="centered")
+
+st.title("🛒 SuperKart Sales Forecasting App")
+st.markdown(
+    """
+    Welcome to the SuperKart Sales Predictor!
+    Fill out the form below with your store's details to get an accurate sales forecast for the upcoming quarter.
+    """
+)
+
+# -----------------------------
+# Input Widgets
+# -----------------------------
+with st.form("sales_form"):
+    col1, col2 = st.columns(2)
+
+    with col1:
+        store_size = st.slider("📏 Store Size (sqft)", min_value=500, max_value=10000, value=3000, step=100)
+        city_tier = st.selectbox("🏙️ City Tier", options=[1, 2, 3], index=1)
+
+    with col2:
+        promotion_active = st.radio("📢 Promotion Active?", options=[0, 1], format_func=lambda x: "Yes" if x == 1 else "No")
+        holiday = st.radio("🎉 Holiday Period?", options=[0, 1], format_func=lambda x: "Yes" if x == 1 else "No")
+
+    previous_sales = st.number_input("📊 Previous Quarter Sales", min_value=0.0, value=10000.0, step=500.0)
+
+    submitted = st.form_submit_button("Predict Sales")
+
+# -----------------------------
+# Prediction Logic
+# -----------------------------
+if submitted:
+    input_data = pd.DataFrame([{
+        "store_size": store_size,
+        "city_tier": city_tier,
+        "promotion_active": promotion_active,
+        "holiday": holiday,
+        "previous_quarter_sales": previous_sales
+    }])
+
+    prediction = model.predict(input_data)[0]
+    st.success(f"📈 Predicted Sales for Next Quarter: ₹{prediction:,.2f}")