app.py

import streamlit as st
import pandas as pd
import requests

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

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

# Collect user input for product features
Product_Allocated_Area = st.number_input("Product Allocated Area", min_value=0.0, max_value=0.3,format="%.3f")
Product_Group = st.selectbox("Product Group", ["Packaged/Processed Foods", "Perishable Foods", "Non-Food/Household"])
Product_MRP = st.number_input("Product MRP", min_value=10.0, max_value=300.0,format="%.2f")
Store_Id = st.selectbox("Store ID", ["OUT004","OUT003","OUT001","OUT002"])
Store_Age = st.number_input("Store Age", min_value=1, max_value=38,format="%d")
Store_Size = st.selectbox("Store Size", ["Medium","High","Small"])
Store_Location_City_Type = st.selectbox("Store Location City Type", ["Tier 1","Tier 2","Tier 3"])
Store_Type = st.selectbox("Store Type", ["Supermarket Type2","Departmental Store","Supermarket Type1","Food Mart"])
Product_Weight = st.number_input("Product Weight", min_value=4.0, max_value=22.0,format="%.2f")
Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low Sugar","Regular","No Sugar"])

# 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_Age': Store_Age,
    'Product_Group': Product_Group,
    'Product_Sugar_Content': Product_Sugar_Content,
    '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://mkrish2025-SKSalesPredict-Backend.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 Sales']
        st.success(f"Predicted 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://mkrish2025-SKSalesPredict-Backend.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.")