Upload folder using huggingface_hub (#1)

- Upload folder using huggingface_hub (05fb1eb862e18658310220a565b80a242dfd7e4b)


Co-authored-by: Rajdeep Jain <raj2261992@users.noreply.huggingface.co>

app.py

@@ -1,44 +1,32 @@
-import requests
 import streamlit as st
 import pandas as pd
-import json
+import requests
 
-st.title("SuperKart Sales Forecast Demo")
+# Streamlit UI for Sales price Prediction
+st.title("SuperKart Sales Prediction App")
+st.write("This tool predicts sales price based on product details. Enter the required information below.")
 
-# Single Product Prediction
-st.subheader("Single Prediction")
+# Collect user input based on dataset columns
+ProductWeight = st.number_input("Product Weight", min_value=0.1, max_value=500.0)
+ProductSugarContent = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
+ProductAllocatedArea = st.number_input("Product Allocated Area", min_value=0.01, max_value=500.0)
+ProductType = st.selectbox("Product Type", ["Fruits and Vegetables","Snack Foods","Frozen Foods","Dairy","Household","Baking Goods","Canned","Health and Hygiene","Meat","Soft Drinks","Breads","Hard Drinks","Others","Starchy Foods","Breakfast","Seafood"])
+ProductMRP = st.number_input("Product MRP", min_value=0.1, max_value=5000000.0)
+StoreId = st.selectbox("Store Id", ["OUT001", "OUT002", "OUT003","OUT004"])
+StoreEstablishmentYear = st.selectbox("Store Establishment Year", ["1987", "1998", "1999","2009"])
+StoreSize = st.selectbox("Store Size", ["Small", "Medium", "High"])
+StoreLocationCityType = st.selectbox("Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"])
+StoreType = st.selectbox("Store Type", ["Supermarket Type1", "Supermarket Type2", "Departmental Store", "Food Mart"])
 
-# Input fields for product and store data
-Product_Weight = st.number_input("Product Weight (weight of the product in kg)", min_value=0.1, max_value=50.0, value=15.5)
-Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low Fat", "Regular"])
-Product_Allocated_Area = st.number_input("Product Allocated Area (area allocated to the product)", min_value=0.001, max_value=1.0, value=0.045, step=0.001)
-Product_Type = st.selectbox("Product Type", ["Baking Goods", "Breads", "Breakfast", "Canned", "Dairy", "Frozen Foods", "Fruits and Vegetables", "Hard Drinks", "Health and Hygiene", "Household", "Meat", "Others", "Seafood", "Snack Foods", "Soft Drinks"])
-Product_MRP = st.number_input("Product MRP (maximum retail price in ₹)", min_value=10.0, max_value=500.0, value=150.75)
-Store_Id = st.selectbox("Store ID", ["OUT010", "OUT013", "OUT017", "OUT018", "OUT019", "OUT027", "OUT035", "OUT045", "OUT046", "OUT049"])
-Store_Establishment_Year = st.number_input("Store Establishment Year", min_value=1985, max_value=2025, value=2005)
-Store_Size = st.selectbox("Store Size", ["Small", "Medium", "High"])
-Store_Location_City_Type = st.selectbox("Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"])
-Store_Type = st.selectbox("Store Type", ["Grocery Store", "Supermarket Type1", "Supermarket Type2", "Supermarket Type3"])
+# Convert categorical inputs to match model training
+product_data = {'Product_Weight': ProductWeight,'Product_Sugar_Content': ProductSugarContent,'Product_Allocated_Area': ProductAllocatedArea,'Product_Type': ProductType,'Product_MRP':ProductMRP,'Store_Id': StoreId,'Store_Establishment_Year': StoreEstablishmentYear,'Store_Size': StoreSize,'Store_Location_City_Type': StoreLocationCityType,'Store_Type': StoreType}
 
-product_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_Id': Store_Id,
-    'Store_Establishment_Year': Store_Establishment_Year,
-    'Store_Size': Store_Size,
-    'Store_Location_City_Type': Store_Location_City_Type,
-    'Store_Type': Store_Type
-}
 
 if st.button("Predict", type='primary'):
-    response = requests.post("https://rj2261992-app-backend-2.hf.space/v1/totalsales", json=product_data)
+    response = requests.post("https://rahulg1987-app-backend.hf.space/v1/totalsales", json=product_data)
     if response.status_code == 200:
         result = response.json()
-        sales_prediction = result["predicted_sales"]
-        #st.write(f"Based on the information provided above...")
-        st.metric(f"Predicted Sales Revenue", f"₹{sales_prediction:.2f}")
+        prediction = result["predicted_sales"]  # Extract only the value
+        st.write(f"Based on the product information provided, The sales price will be {prediction}.")
     else:
-        st.error("Error in API request")
+        st.error(response.status_code)