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import streamlit as st
import requests
st.title("SuperKart Sales Predictor")
# Input fields for product and store data
Product_Weight = st.number_input("Product Weight", min_value=0.0, value=12.66)
Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
Product_Allocated_Area = st.number_input("Product Allocated Area", min_value=0.0, value=20.0)
Product_MRP = st.number_input("Product MRP", min_value=0.0, value=100.0)
Store_Size = st.selectbox("Store Size", ["Small", "Medium", "High"])
Store_Location_City_Type = st.selectbox("Store Location City Type", ["Urban", "Semi-Urban", "Tier 3"])
Store_Type = st.selectbox("Store Type", ["Type 1", "Type 2", "Type 3", "Type 4"])
Product_Id_char = st.selectbox("Product ID Prefix", ["FD", "DR", "NC"]) # Example prefixes
Store_Age_Years = st.number_input("Store Age (Years)", min_value=0, value=10)
Product_Type_Category = st.selectbox("Product Type Category", ["Food", "Drinks", "Non-Consumable"]) # Example categories
# Prepare data for POST request
product_data = {
"Product_Weight": Product_Weight,
"Product_Sugar_Content": Product_Sugar_Content,
"Product_Allocated_Area": Product_Allocated_Area,
"Product_MRP": Product_MRP,
"Store_Size": Store_Size,
"Store_Location_City_Type": Store_Location_City_Type,
"Store_Type": Store_Type,
"Product_Id_char": Product_Id_char,
"Store_Age_Years": Store_Age_Years,
"Product_Type_Category": Product_Type_Category
}
# Predict button and API call
if st.button("Predict", type='primary'):
response = requests.post(
"https://DD8943-superkart-regression-app.hf.space/v1/predict",
json=product_data
)
if response.status_code == 200:
result = response.json()
predicted_sales = result["Sales"]
st.write(f"Predicted Product Store Sales Total: ₹{predicted_sales:.2f}")
else:
st.error("Error in API request")
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