app.py

import streamlit as st
import pandas as pd
import requests

# Set the title of the Streamlit app
st.title("Superkart Revenue Prediction")

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

# Collect user input for property features
product_weight = st.number_input("Product_Weight", min_value=0.0, max_value=1000.0, step=0.1, value=12.66)
product_sugar_content = st.selectbox("Product_Sugar_Content", ["Low Sugar", "Regular", "No Sugar"])
product_allocated_area = st.number_input("Product_Allocated_Area", min_value=0.0, max_value=1.0, step=0.001, value=0.027)
product_type = st.selectbox("Product_Type", ["Frozen Foods", "Dairy", "Canned", "Baking Goods", "Health and Hygiene", "Snack Foods"])
product_mrp = st.number_input("Product_MRP", min_value=0.0, max_value=1000.0, step=0.1, value=117.08)
store_id = st.text_input("Store_Id", ["OUT001", "OUT002", "OUT003", "OUT004"])
store_establishment_year = st.number_input("Store_Establishment_Year", min_value=1900, max_value=2027, step=1, value=2009)
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"])
#product_store_sales_total = st.number_input("Product_Store_Sales_Total", min_value=0.0, max_value=10000.0, step=0.1, value=2842.4)

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://<username>-<repo_id>.hf.space/v1/revenue", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
    if response.status_code == 200:
        prediction = response.json()['Predicted Revenue (in dollars)']
        st.success(f"Predicted Rental Revenue (in dollars): {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://<username>-<repo_id>.hf.space/v1/revenuebatch", 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.")