streamlit_app.py

import streamlit as st
import pandas as pd
import requests

st.title('SuperKart Sales Forecast')

# Input fields with refined ranges based on df.describe()
product_weight = st.number_input('Product Weight', min_value=4.555, max_value=21.35, value=12.8289)
sugar_content = st.selectbox('Sugar Content', ['Low Sugar', 'Regular', 'No Sugar', 'reg'])
allocated_area = st.number_input('Allocated Area', min_value=0.013, max_value=0.295, value=0.1393)
product_type = st.selectbox('Product Type', ['Frozen Foods', 'Dairy', 'Canned', 'Baking Goods', 'Health and Hygiene', 'Snack Foods', 'Meat', 'Household', 'Hard Drinks', 'Fruits and Vegetables', 'Breads', 'Soft Drinks', 'Breakfast', 'Others', 'Starchy Foods', 'Seafood'])
mrp = st.number_input('Product MRP', min_value=31.29, max_value=266.89, value=141.15)
est_year = st.number_input('Store Establishment Year', min_value=1985, max_value=2015, value=1999)  # Changed to integer
store_size = st.selectbox('Store Size', ['Medium', 'High', 'Small'])
city_type = st.selectbox('City Type', ['Tier 1', 'Tier 2', 'Tier 3'])
store_type = st.selectbox('Store Type', ['Supermarket Type1', 'Supermarket Type2', 'Departmental Store', 'Food Mart'])

if st.button('Predict'):
    data = {
        'Product_Weight': product_weight,
        'Product_Sugar_Content': sugar_content,
        'Product_Allocated_Area': allocated_area,
        'Product_Type': product_type,
        'Product_MRP': mrp,
        'Store_Establishment_Year': est_year,
        'Store_Size': store_size,
        'Store_Location_City_Type': city_type,
        'Store_Type': store_type,
        'Store_Age': 2025 - est_year
    }
    try:
        response = requests.post('https://saibsund-superkart-flask-api.hf.space/predict', json=data)
        response.raise_for_status()
        prediction = response.json()['prediction']
        st.write(f'Predicted Sales: ${prediction:.2f}')
    except requests.exceptions.RequestException as e:
        st.write(f"Error: {e}")