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import streamlit as st
import pandas as pd
import requests
# Set the title of the Streamlit app
st.title("SuperKart sales Prediction")
# Section for online prediction
st.subheader("Online Prediction")
# Collect user input for Sales features
# Collect user input
product_Weight=st.number_input("Product_Weight", min_value=1, value=30.00)
product_Sugar_Content=st.selectbox("Product Sugar Content", ["Low Sugar", "No Sugar", "Regular"])
product_Allocated_Area=st.number_input("Product Allocate Area", min_value=0.001, value=0.09)
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=1, value=30.00)
store_Id=st.selectbox("Store ID", ["OUT001", "OUT002", "OUT003", "OUT004"])
store_Establishment_Year=st.number_input("Store Establishment year", min_value=1987, value=2009)
store_Size=st.selectbox("Store Size", ["High", "Medium", "Small"])
store_Location_City_Type=st.selectbox("Store location City", ["Tier1", "Tier2", "Tier3"])
store_Type=st.selectbox("Store Type", ["Departmental Store", "Supermarket Type 1", "Supermarket Type 2", "Food Mart"])
# Convert user input into a DataFrame
input_data = pd.DataFrame([{
'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
}])
# Make prediction when the "Predict" button is clicked
if st.button("Predict"):
response = requests.post("https://LalithaShiva-SalesAnalysisPredictionFrontend.hf.space/v1/sksales", json=input_data.to_dict(orient='records')[0]) # Send data to Flask API
if response.status_code == 200:
result = response.json()['Predicted Price (in dollars)']
st.success(f"Predicted Rental Price (in dollars): {prediction}")
else:
st.error("Error making prediction.")
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