Create app.py

app.py

@@ -0,0 +1,44 @@
+import streamlit as st
+import requests
+
+# Backend URL (Flask API you deployed earlier)
+BACKEND_URL = "https://vilas97gupta-superkartbackend.hf.space/predict"
+
+st.set_page_config(page_title="SuperKart Sales Predictor", layout="centered")
+
+st.title("🛒 SuperKart Sales Prediction")
+
+st.write("Fill in the details below to predict sales:")
+
+# Input fields
+product_sugar = st.selectbox("Product Sugar Content", ["Low", "Medium", "High"])
+product_type = st.selectbox("Product Type", ["Snack", "Beverage", "Grocery", "Other"])
+store_id = st.text_input("Store Id", "S1")
+store_size = st.selectbox("Store Size", ["Small", "Medium", "Large"])
+store_city_type = st.selectbox("Store City Type", ["Urban", "Suburban", "Rural"])
+
+product_weight = st.number_input("Product Weight (kg)", min_value=0.0, step=0.1)
+product_mrp = st.number_input("Product MRP (₹)", min_value=0.0, step=1.0)
+store_year = st.number_input("Store Establishment Year", min_value=1900, max_value=2025, step=1)
+
+if st.button("🔮 Predict Sales"):
+    payload = {
+        "Product_Sugar_Content": product_sugar,
+        "Product_Type": product_type,
+        "Store_Id": store_id,
+        "Store_Size": store_size,
+        "Store_Location_City_Type": store_city_type,
+        "Product_Weight": product_weight,
+        "Product_MRP": product_mrp,
+        "Store_Establishment_Year": store_year
+    }
+
+    try:
+        resp = requests.post(BACKEND_URL, json=payload)
+        if resp.status_code == 200:
+            prediction = resp.json()["predictions"][0]
+            st.success(f"💰 Predicted Sales: {prediction:,.2f}")
+        else:
+            st.error(f"Backend error: {resp.text}")
+    except Exception as e:
+        st.error(f"Request failed: {e}")