Upload model and encoders for v1.0

app.py

@@ -0,0 +1,54 @@
+import streamlit as st
+import joblib
+import pandas as pd
+
+# Load model and encoders
+@st.cache_resource
+def load_model_and_data():
+    model = joblib.load("deployment_files/Quarterly_Sales_Revenue_model_v1_0.joblib")
+    data = pd.read_csv("/content/drive/MyDrive/SuperKart.csv")  # Update with your actual data file path
+    return model, data
+   # SuperKart =  pd.read_csv("/content/drive/MyDrive/SuperKart.csv")
+
+# Prediction function
+def predict_total_sales_by_store(store_ids, df, rf_tuned):
+    results = []
+
+    feature_cols = [
+        'Product_Id', 'Store_Id', 'Product_Type', 'Product_Sugar_Content',
+        'Store_Type', 'Store_Location_City_Type', 'Store_Size',
+        'Product_Weight', 'Product_Allocated_Area', 'Product_MRP',
+        'Store_Establishment_Year', 'Total_Historical_Sales',
+        'Num_Past_Transactions', 'Average_Historical_Sales'
+    ]
+
+    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))
+            continue
+
+        X_input = store_df[feature_cols]
+        predicted_sales = rf_tuned.predict(X_input).sum()
+        results.append((store_id, round(predicted_sales, 2)))
+
+    return results
+
+# Load
+rf_tuned, df = load_model_and_data()
+
+# Streamlit UI
+st.title("🧾 Predict Total Quarterly Sales per Store")
+
+# Store ID selection
+store_ids = st.multiselect("Select Store ID(s)", sorted(df["Store_Id"].unique()))
+
+if st.button("Predict Sales"):
+    if not store_ids:
+        st.warning("Please select at least one Store ID.")
+    else:
+        results = predict_total_sales_by_store(store_ids, df, rf_tuned)
+        st.subheader("📊 Predicted Sales:")
+        for store_id, sales in results:
+            st.write(f"🛒 Store {store_id}: ${sales:,.2f}")