Upload app.py

app.py

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+import streamlit as st
+import pandas as pd
+import requests
+
+st.title("🛒 SuperKart Sales Forecast App")
+
+st.markdown("""
+Use this interactive interface to forecast product-level sales for SuperKart stores based on store and product attributes.
+""")
+
+API_URL = "https://jkng77433-Backend.hf.space/v1/forecast/single"
+
+st.header("Single Prediction")
+
+col1, col2 = st.columns(2)
+with col1:
+    product_id = st.text_input("Product ID", "FD6114")
+    product_type = st.selectbox("Product Type", ["Frozen Foods", "Dairy", "Canned", "Snack Foods"])
+    sugar_content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
+    product_weight = st.number_input("Product Weight", min_value=0.1, value=12.66)
+    product_mrp = st.number_input("Product MRP", min_value=0.0, value=117.08)
+
+with col2:
+    store_id = st.text_input("Store ID", "OUT004")
+    store_type = st.selectbox("Store Type", ["Supermarket Type1", "Supermarket Type2", "Departmental Store", "Food Mart"])
+    store_size = st.selectbox("Store Size", ["Small", "Medium", "High"])
+    store_location = st.selectbox("Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"])
+    est_year = st.number_input("Store Establishment Year", min_value=1980, max_value=2025, value=2009)
+    allocated_area = st.number_input("Product Allocated Area", min_value=0.0, max_value=1.0, value=0.027, format="%.3f")
+
+if st.button("Predict Sales"):
+    payload = {
+        "Product_Id": product_id,
+        "Product_Type": product_type,
+        "Product_Sugar_Content": sugar_content,
+        "Product_Weight": product_weight,
+        "Product_MRP": product_mrp,
+        "Store_Id": store_id,
+        "Store_Type": store_type,
+        "Store_Size": store_size,
+        "Store_Location_City_Type": store_location,
+        "Store_Establishment_Year": est_year,
+        "Product_Allocated_Area": allocated_area
+    }
+    response = requests.post(API_URL, json=payload)
+    if response.status_code == 200:
+        result = response.json()
+        st.success(f"Predicted Sales: **${result['Predicted_Product_Store_Sales_Total']:.2f}**")
+    else:
+        st.error("Prediction failed — check input values or backend availability.")