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 Prediction")

# Collect user input for property features
Product_Weight = st.number_input(
    "Product Weight (kg)", 
    min_value=0.0, step=0.01, value=12.65
)

Product_Sugar_Content = st.selectbox(
    "Product Sugar Content", 
    ["Low Sugar", "Regular", "No Sugar"]   # cleaned (removed 'reg')
)

Product_Allocated_Area = st.number_input(
    "Product Allocated Area (sq. m)", 
    min_value=0.0, step=0.001, value=0.068
)

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"]
)

Product_MRP = st.number_input(
    "Product MRP (₹)", 
    min_value=0.0, step=0.5, value=147.0
)

# --------------------------
# Store Features
# --------------------------
Store_Id = st.selectbox("Store ID", ["OUT001", "OUT002", "OUT003", "OUT004"])

Store_Establishment_Year = st.number_input(
    "Store Establishment Year", 
    min_value=1987, max_value=2025, step=1, value=2002
)

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_Sugar_Content": Product_Sugar_Content,
    "Product_Allocated_Area": Product_Allocated_Area,
    "Product_Type": Product_Type,
    "Product_MRP": Product_MRP,
    "Store_Id": Store_Id,
    "Store_Establishment_Year": Store_Establishment_Year,
    "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://Jagadesswar-SuperKartSalesPredictionBackend.hf.space/v1/sales_revenue", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
    if response.status_code == 200:
        prediction = response.json()['Predicted Revenue (INR (₹))']
        st.success(f"Predicted Revenue (INR (₹)): {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://Jagadesswar-SuperKartSalesPredictionBackend.hf.space/v1/sales_revenue_batch", 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.")