app.py

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 Sales Prediction")

# Collect user input for property features
Product_Sugar_Content = st.selectbox("Product Sugar Content", ['Low Sugar' ,'Regular' ,'No Sugar' ,'reg'])
Product_Weight = st.number_input("Product Weight", min_value=0.0, value=12.66)
Product_Allocated_Area = st.number_input("Product Allocated Area", min_value=0.0,  value=0.027)
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'])
Product_MRP = st.number_input("Product_MRP",value=117.08)
Store_Establishment_Year = st.number_input("Store_Establishment_Year",value=2009)
Store_Size = st.selectbox("Store_Size",['Medium' ,'High' ,'Small'])
Store_Type = st.selectbox("Store_Type", ['Tier 2' ,'Tier 1' ,'Tier 3'])
Store_Location_City_Type = st.selectbox("Store_Location_City_Type", ['Supermarket Type2' ,'Departmental Store' ,'Supermarket Type1', 'Food Mart'])

# Convert user input into a DataFrame
input_data = pd.DataFrame([{
    'Product_Sugar_Content': Product_Sugar_Content,
    'Product_Weight': Product_Weight,
    'Product_Allocated_Area': Product_Allocated_Area,
    'Product_Type': Product_Type,
    'Product_MRP': Product_MRP,
    'Store_Establishment_Year': Store_Establishment_Year,
    #'Store_Age': 2025 - Store_Establishment_Year,
    'Store_Size': Store_Size,
    'Store_Type': Store_Type,
    'Store_Location_City_Type': Store_Location_City_Type
}])

# Make prediction when the "Predict" button is clicked
if st.button("Predict"):
    response = requests.post("https://debrupa24-SuperKartSalesPredictionBackend.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 number of Sales']
        st.success(f"Predicted number of Sales: {prediction}")
    else:
        st.error("Error making prediction.")

# Section for batch prediction
st.subheader("Batch Prediction")

# Allow users to upload a CSV file for batch prediction
uploaded_file = st.file_uploader("Upload CSV file for batch prediction", type=["csv"])

# Make batch prediction when the "Predict Batch" button is clicked
if uploaded_file is not None:
    if st.button("Predict Batch"):
        response = requests.post("https://debrupa24-SuperKartSalesPredictionBackend.hf.space/v1/salesBatch", files={"file": uploaded_file})  # Send file to Flask API
        if response.status_code == 200:
            predictions = response.json()
            st.success("Batch predictions completed!")
            st.write(predictions)  # Display the predictions
        else:
            st.error("Error making batch prediction.")