app.py

import streamlit as st
import pandas as pd
import requests

# Set the title of the Streamlit app
st.title("Total Sales Prediction App for SuperKart")

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

# Collect user input
Product_Weight = st.number_input("Product Weight", min_value=4.0, max_value=22.0, step=0.01, value=4.0)
Product_Allocated_Area = st.number_input("Product Allocated Area", min_value=0.004, max_value=0.298, step=0.001, value=0.004)
Product_MRP = st.number_input("Product MRP", min_value=31.0, max_value=266.0, step=0.01, value=31.0)
Store_Establishment_Year = int(st.number_input("Store_Establishment_Year", min_value=1987, max_value=2009, step=1, value=2009))
Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low", "Regular", "Non Sugar"])
Product_Type = st.selectbox("Product Type", ["Fruits and Vegetables", "Dairy", "Baking Goods", "Others"])
Store_Id = st.selectbox("Store Id", ["OUT001", "OUT002", "OUT003", "OUT004"])
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", ["Departmental Store", "Supermarket Type1", "Supermarket Type2"])

# Convert user input into a DataFrame
input_data = pd.DataFrame([{
    'Product_Weight': Product_Weight,
    'Product_Allocated_Area': Product_Allocated_Area,
    'Product_MRP': Product_MRP,
    'cStore_Establishment_Year': Store_Establishment_Year,
    'Product_Sugar_Content': Product_Sugar_Content,
    'Product_Type': Product_Type,
    'Store_Id': Store_Id,
    '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://RubeenaNouman-PredictionBackend.hf.space/v1/pred", 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 Sales Price (in dollars): {prediction}")
    else:
        st.error("Error making prediction.")