Delete app.py

app.py

@@ -1,50 +0,0 @@
-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)
-
-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.")