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app.py

@@ -5,12 +5,19 @@ import joblib
 # Load model and data
 @st.cache_resource
 def load_model_and_data():
-    model = joblib.load("Quarterly_Sales_Revenue_model_v1_0.joblib")
-    data = pd.read_csv("SuperKart_local.csv")
+    model = joblib.load("deployment_files/Quarterly_Sales_Revenue_model_v1_0.joblib")
+    #model = joblib.load("Quarterly_Sales_Revenue_model_v1_0.joblib")
+
+    data = pd.read_csv("deployment_files/SuperKart_local.csv")
+
     return model, data
 
 # Prediction logic
-def predict_total_sales_by_store(store_ids, df, model):
+def predict_quarterly_sales_by_store_and_product(store_ids, df, model):
+    store_results = []
+    product_results = []
+    total_sales = 0.0
+
     feature_cols = [
         'Product_Id', 'Store_Id', 'Product_Type', 'Product_Sugar_Content',
         'Store_Type', 'Store_Location_City_Type', 'Store_Size',
@@ -18,31 +25,60 @@ def predict_total_sales_by_store(store_ids, df, model):
         'Store_Establishment_Year', 'Total_Historical_Sales',
         'Num_Past_Transactions', 'Average_Historical_Sales'
     ]
-    results = []
+
     for store_id in store_ids:
         store_df = df[df['Store_Id'] == store_id].copy()
+
         if store_df.empty:
-            results.append((store_id, 0.0))
+            store_results.append((store_id, 0.0))
             continue
+
         X_input = store_df[feature_cols]
-        predicted_sales = model.predict(X_input).sum()
-        results.append((store_id, round(predicted_sales, 2)))
-    return results
+        y_pred = model.predict(X_input)
+
+        store_total_sales = sum(y_pred)
+        store_results.append((store_id, round(store_total_sales, 2)))
+        total_sales += store_total_sales
 
-# Load
+        for product_id, sales in zip(store_df['Product_Id'], y_pred):
+            product_results.append((store_id, product_id, round(sales, 2)))
+
+    return store_results, product_results, round(total_sales, 2)
+
+# Load model and data
 model, df = load_model_and_data()
 
 # UI
-st.title("🧾 Predict Total Quarterly Sales per Store")
-store_ids = st.multiselect("Select Store ID(s)", sorted(df["Store_Id"].unique()))
+st.title("📈 Predict Quarterly Sales by Store and Product")
+
+# Dropdowns
+store_ids = st.multiselect("🏬 Select Store ID(s)", sorted(df["Store_Id"].unique()))
+product_ids = st.multiselect("📦 Select Product ID(s) (optional)", sorted(df["Product_Id"].unique()))
 
-if st.button("Predict Sales"):
+# Predict button
+if st.button("Predict Quarterly Sales"):
     if not store_ids:
         st.warning("Please select at least one Store ID.")
     else:
-        results = predict_total_sales_by_store(store_ids, df, model)
-        st.subheader("📊 Predicted Sales:")
-        for store_id, sales in results:
-            #st.write(f"🛒 Store {store_id}: ${sales:,.2f}")
-            st.write(f"🛒 Store {store_id}: ₹{sales:,.2f}")
+        store_results, product_results, total_sales = predict_quarterly_sales_by_store_and_product(store_ids, df, model)
+
+        # Display Store-wise sales
+        st.subheader("🏪 Store-wise Total Quarterly Sales")
+        for store, sales in store_results:
+            st.write(f"🛒 Store {store}: ₹{sales:,.2f}")
+
+        # Display Product-wise sales (optional filter)
+        st.subheader("📦 Product-wise Sales (Filtered by Selected Stores)")
+        filtered_products = product_results
+        if product_ids:
+            filtered_products = [r for r in product_results if r[1] in product_ids]
+
+        if filtered_products:
+            product_df = pd.DataFrame(filtered_products, columns=["Store_Id", "Product_Id", "Predicted_Sales"])
+            st.dataframe(product_df)
+        else:
+            st.info("No product sales to display for selected filters.")
 
+        # Display total sales
+        st.subheader("📊 Total Quarterly Sales Across All Selected Stores")
+        st.success(f"₹{total_sales:,.2f}")