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app.py

@@ -0,0 +1,52 @@
+
+import streamlit as st
+import pandas as pd
+import joblib
+import requests
+
+# Title
+st.title("🛒 SuperKart Sales Forecasting")
+
+st.markdown("Enter product and store details to predict sales.")
+
+# Input fields
+product_weight = st.number_input("Product Weight (kg)", min_value=1.0, step=0.1)
+product_sugar_content = st.selectbox("Sugar Content", ["Low Sugar", "Regular", "No Sugar", "Sugar Free"])
+product_allocated_area = st.slider("Display Area (%)", 0.0, 0.3, 0.05)
+product_type = st.selectbox("Product Type", [
+    "Meat", "Snack Foods", "Hard Drinks", "Dairy", "Canned", "Soft Drinks", "Health and Hygiene",
+    "Baking Goods", "Bread", "Breakfast", "Frozen Foods", "Fruits and Vegetables",
+    "Household", "Seafood", "Starchy Foods", "Others"
+])
+product_mrp = st.number_input("Product MRP (₹)", min_value=10.0, step=1.0)
+store_size = st.selectbox("Store Size", ["Small", "Medium", "High"])
+store_location_city_type = st.selectbox("City Tier", ["Tier 1", "Tier 2", "Tier 3"])
+store_type = st.selectbox("Store Type", [
+    "Departmental Store", "Supermarket Type1", "Supermarket Type2", "Food Mart"
+])
+store_age = st.slider("Store Age (Years)", 1, 40, 15)
+
+# Submit
+if st.button("Predict Sales"):
+    input_data = {
+        "Product_Weight": product_weight,
+        "Product_Sugar_Content": product_sugar_content,
+        "Product_Allocated_Area": product_allocated_area,
+        "Product_Type": product_type,
+        "Product_MRP": product_mrp,
+        "Store_Size": store_size,
+        "Store_Location_City_Type": store_location_city_type,
+        "Store_Type": store_type,
+        "Store_Age": store_age
+    }
+
+    # Call the backend API
+    try:
+        response = requests.post("https://<your-backend-username>.huggingface.space/<backend-space-name>/predict", json=input_data)
+        result = response.json()
+        if "predicted_sales" in result:
+            st.success(f"📈 Predicted Sales: ₹{result['predicted_sales']}")
+        else:
+            st.error(f"Error: {result.get('error', 'Unknown')}")
+    except Exception as e:
+        st.error(f"API call failed: {e}")

requirements.txt

@@ -1,3 +1,3 @@
-altair
-pandas
-streamlit
+streamlit==1.35.0
+requests==2.32.3
+pandas==2.2.2