app.py

import streamlit as st
import pandas as pd
import requests

# Set the title of the Streamlit app
st.title("Product Store Sales Prediction")

# Section for online prediction
st.subheader("Online Prediction")

# Collect user input for property features
Product_Weight = st.number_input("Product Weight", min_value=4, value =12)
Product_Allocated_Area = st.number_input("Product Allocated Area", min_value=0.004, value=0.056)
Product_MRP = st.number_input("Product MRP", min_value=31, step=1, value=146)
Store_Establishment_Year = st.number_input("Store Establishment Year", value=2009)
Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular","No Sugar","reg"])
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", "Others", "Starchy Foods","Breakfast","Seafood"])
Store_Size = st.selectbox("Store_Size", ["Small", "Medium","High"])
Store_Location_City_Type = st.selectbox("Store Location City Type", ["Tier 1", "Tier 2","Tier 3"])
Store_Type = st.selectbox("Store Type", ["Supermarket Type1", "Supermarket Type2","Departmental Store","Food Mart"])

# Convert user input into a DataFrame
input_data = pd.DataFrame([{
    'Product_Weight': Product_Weight,
    'Product_Allocated_Area': Product_Allocated_Area,
    'Product_MRP': Product_MRP,
    'Store_Establishment_Year': Store_Establishment_Year,
    'Product_Sugar_Content': Product_Sugar_Content,
    'Product_Type': Product_Type, 
    'Store_Location_City_Type': Store_Location_City_Type,
    'Store_Size': Store_Size,
    'Store_Type': Store_Type,
}])

# Make prediction when the "Predict" button is clicked
if st.button("Predict"):
    response = requests.post("https://wash9968-ProductStoreSalesPredictionBackend.hf.space//v1/productstoresalesprediction", 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 Product Store Sales: {prediction}")
    else:
        st.error("Error making prediction.")