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
store_type = st.selectbox("Store Type", ["Supermarket Type1", "Supermarket Type2", "Food Mart", "Departmental Store"])
store_location_city_type = st.selectbox("Store Location City Type", ['Tier 2' 'Tier 1' 'Tier 3'])
store_size = st.selectbox("Store Size", ['Medium' 'High' 'Small'])
store_id = st.selectbox("Store Id", ['OUT004' 'OUT003' 'OUT001' 'OUT002'])
product_sugar_content = st.number_input("Product Sugar Content", ['Low Sugar', 'Regular', 'No Sugar'])
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'])

# user_name = 'nrajwani'
# repo_id = "nrajwani/SalesPredictionBackend" 

# Convert user input into a DataFrame
input_data = pd.DataFrame([{
    'store_type': store_type,
    'store_location_city_type': store_location_city_type,
    'store_size': store_size,
    'store_id': store_id,
    'product_sugar_content': product_sugar_content,
    'product_type': product_type
}])

# Make prediction when the "Predict" button is clicked
if st.button("Predict"):
    response = requests.post("https://<username>-<repo_id>.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 (in dollars)']
        st.success(f"Predicted Sales (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/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.")