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app.py

@@ -1,29 +1,44 @@
-# app.py
 import streamlit as st
 import joblib
 import pandas as pd
 
 # Load the model
-model = joblib.load("best_sales_model.pkl")
-
-st.title("SuperKart Store Sales Forecasting")
-
-# Input form
-st.subheader("Enter Product and Store Details")
-
-product_weight = st.number_input("Product Weight", value=10.0)
-sugar_content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
-allocated_area = st.slider("Product Allocated Area", 0.01, 1.0, 0.1)
-product_type = st.selectbox("Product Type", ["Fruits and Vegetables", "Snack Foods", "Frozen Foods", "Dairy", "Household", "Baking Goods", "Canned", "Health and Hygiene", "Meat", "Soft Drinks", "Breads", "Hard Drinks", "Starchy Foods", "Breakfast", "Seafood", "Others"])
-product_mrp = st.number_input("Product MRP", value=100.0)
-store_size = st.selectbox("Store Size", ["Small", "Medium", "High"])
-store_city = st.selectbox("Store City Type", ["Tier 1", "Tier 2", "Tier 3"])
-store_type = st.selectbox("Store Type", ["Supermarket Type2", "Supermarket Type1", "Departmental Store", "Food Mart"])
-store_id = st.selectbox("Store ID", ["OUT001", "OUT002", "OUT003", "OUT004"])
-store_age = st.slider("Store Age (Years)", 0, 50, 10)
-
-# Predict
-if st.button("Predict Sales"):
+@st.cache_resource
+def load_model():
+    return joblib.load("best_sales_model.pkl")
+
+model = load_model()
+
+# App title
+st.set_page_config(page_title="SuperKart Sales Forecast", layout="centered")
+st.title("🛍️ SuperKart Stores Sales Forecasting")
+
+# User input form
+st.header("📋 Input Product & Store Details")
+
+with st.form("sales_form"):
+    col1, col2 = st.columns(2)
+
+    with col1:
+        product_weight = st.number_input("Product Weight (in grams)", min_value=0.0, step=0.1)
+        sugar_content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
+        allocated_area = st.slider("Product Allocated Area (ratio)", min_value=0.0, max_value=1.0, step=0.01)
+        product_type = st.selectbox("Product Type", [
+            "Fruits and Vegetables", "Snack Foods", "Frozen Foods", "Dairy", "Household", "Baking Goods", "Canned", "Health and Hygiene", "Meat", "Soft Drinks", "Breads", "Hard Drinks", "Starchy Foods", "Breakfast", "Seafood", "Others"
+        ])
+        product_mrp = st.number_input("Product MRP", min_value=0, max_value=50, value=10)
+
+    with col2:
+        store_size = st.selectbox("Store Size", ["Small", "Medium", "High"])
+        store_city = st.selectbox("Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"])
+        store_type = st.selectbox("Store Type", ["Supermarket Type2", "Supermarket Type1", "Departmental Store", "Food Mart"])
+        store_id = st.selectbox("Store ID", ["OUT001", "OUT002", "OUT003", "OUT004"])
+        store_age = st.slider("Store Age (Years)", min_value=0.1, value=100.0)
+
+    submitted = st.form_submit_button("🔍 Predict Sales")
+
+# Predict sales
+if submitted:
     input_data = pd.DataFrame([{
         'Product_Weight': product_weight,
         'Product_Sugar_Content': sugar_content,
@@ -36,6 +51,6 @@ if st.button("Predict Sales"):
         'Store_Id': store_id,
         'Store_Age': store_age
     }])
-    
+
     prediction = model.predict(input_data)[0]
-    st.success(f"Predicted Sales: {round(prediction, 2)}")
+    st.success(f"✅ Predicted Product Sales: ₹ {round(prediction, 2)}")