app.py

import streamlit as st
import pandas as pd
import requests

# Set the title of the Streamlit app
st.title("Super kart Sales Prediction")

# Section for sales prediction
st.subheader("Sales Prediction")

# Collect user input for property features
Product_Id = st.text_input("Product ID")
Product_Weight = st.number_input("Product Weight ", min_value=1, value=2)
Product_Sugar_Content = st.selectbox("Product Sugar Content", ["No Sugar", "Low Sugar", "Regular", "reg"])
Product_Allocated_Area = st.number_input("Product Allocated area ", min_value=1, value=2)
Product_Type = 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",            
"Starchy Foods",            
"Breakfast",          
"Seafood",
"Household",                
"Baking Goods",             
"Canned",           
"Health and Hygiene",       
"Meat",     
"Soft Drinks",              
"Breads",            
"Hard Drinks",            
"Starchy Foods",            
"Breakfast",          
"Seafood",
"Others"])
Product_MRP = st.number_input("Product MRP ", min_value=1, value=2)
Store_Id = st.selectbox ("Store ID", ["OUT001","OUT002","OUT003","OUT004"])
Store_Establishment_Year = st.number_input("Store Establishement year ", min_value=1980, max_value=2025)
Store_Size = t.selectbox ("Store Size", ["Small","Medium","High"])
Store_Location_City_Type = t.selectbox ("Store Location city", ["Tier 1","Tier 2","Tier 3"])
Store_Type = t.selectbox ("Store Type", ["Departmental Store","Food Mart","Supermarket Type1","Supermarket Type2"])

# Convert user input into a DataFrame
input_data = pd.DataFrame([{
   'Product_Id': Product_Id.
   '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://Shanmuganathan75-SuperKartPredictionBackend.hf.space/v1/sales", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
    if response.status_code == 200:
        prediction = response.json()['Predicted Price (in dollars)']
        st.success(f"Predicted Rental Price (in dollars): {prediction}")
    else:
        st.error("Error making prediction.")