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import streamlit as st
import requests
st.title("SuperKart Sales Forecasting App")
# 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=16.0)
Product_MRP = st.number_input("Product MRP", min_value=0.0, value=250.0)
Store_Size = st.selectbox("Store Size", ["Small", "Medium", "High"])
Store_Location_City_Type = st.selectbox("Store Location City Type", ["Tier1", "Tier2", "Tier3"])
Store_Type = st.selectbox("Store Type", ["Grocery Store", "Supermarket Type1", "Supermarket Type2", "Supermarket Type3", "Food Mart"])
Product_Id_char = st.selectbox("Product Id Char", ["FD", "DR", "NC"])
Store_Age_Years = st.number_input("Store Age (Years)", min_value=0, value=14, step=1)
Store_Age_Years = int(Store_Age_Years)
Product_Type_Category = st.selectbox("Product Type Category", ["Perishable", "Non Perishable"])
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
}
if st.button("Predict", type="primary"):
response = requests.post(
"https://nimerml-backend.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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