File size: 1,674 Bytes
3353e64 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | import streamlit as st
import requests
st.title("🛒 SuperKart Sales Predictor")
# Sidebar inputs
Product_Weight = st.sidebar.number_input("Product Weight", min_value=0.0, max_value=100.0, value=10.0)
Product_Sugar_Content = st.sidebar.selectbox("Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
Product_Allocated_Area = st.sidebar.number_input("Allocated Area", min_value=0.0, max_value=10.0, value=0.5)
Product_Type = st.sidebar.text_input("Product Type", "Snack foods")
Product_MRP = st.sidebar.number_input("MRP", min_value=0.0, max_value=1000.0, value=100.0)
Store_Size = st.sidebar.selectbox("Store Size", ["Small", "Medium", "High"])
Store_Location_City_Type = st.sidebar.selectbox("City Type", ["Tier 1", "Tier 2", "Tier 3"])
Store_Type = st.sidebar.text_input("Store Type", "Supermarket Type1")
if st.button("Predict Sales"):
sample = [{
"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
}]
url = "https://akhilraja-superkart-flask-api.hf.space/predict"
try:
response = requests.post(url, json=sample)
response.raise_for_status()
prediction = response.json().get("predictions", [None])[0]
if prediction is not None:
st.success(f"Predicted Sales: {prediction:.2f}")
else:
st.error("No prediction returned.")
except Exception as e:
st.error(f"Error: {e}")
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