app.py

import requests
import streamlit as st
import pandas as pd

st.title("SuperKart Sales Prediction")

# Single Item Prediction
st.subheader("Single Item Prediction")

# Input fields for product and store data based on SuperKart dataset features
product_weight = st.number_input("Product Weight", min_value=0.0, 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, value=0.027)
product_type = st.selectbox("Product Type", ['Baking Goods', 'Breads', 'Breakfast', 'Canned', 'Dairy', 'Frozen Foods', 'Fruits and Vegetables', 'Hard Drinks', 'Health and Hygiene', 'Household', 'Meat', 'Others', 'Seafood', 'Snack Foods', 'Soft Drinks', 'Starchy Foods'])
product_mrp = st.number_input("Product MRP", min_value=0.0, value=117.08)
store_id = st.selectbox("Store ID", ['OUT001', 'OUT002', 'OUT003', 'OUT004'])
store_establishment_year = st.number_input("Store Establishment Year", min_value=1985, max_value=datetime.now().year, 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", ["Departmental Store", "Supermarket Type1", "Supermarket Type2", "Food Mart"])

item_data = {
    '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,
}

# Replace with your Hugging Face Space URL for the backend
backend_url = "https://Amittripipathi-SuperKart.hf.space"

if st.button("Predict Sales", type='primary'):
    response = requests.post(f"{backend_url}/v1/predict_sale", json=item_data)
    if response.status_code == 200:
        result = response.json()
        predicted_sales = result["Predicted_Sales"]
        st.write(f"The predicted sales for this item is: {predicted_sales:.2f}")
    else:
        st.error(f"Error in API request: {response.status_code} - {response.text}")

# Batch Prediction
st.subheader("Batch Prediction")

file = st.file_uploader("Upload CSV file for Batch Prediction", type=["csv"])
if file is not None:
    if st.button("Predict for Batch", type='primary'):
        response = requests.post(f"{backend_url}/v1/predict_sale_batch", files={"file": file})
        if response.status_code == 200:
            result = response.json()
            st.header("Batch Prediction Results")
            # Display batch predictions
            predictions_df = pd.DataFrame(result['Batch_Predictions'], columns=['Predicted_Sales'])
            st.dataframe(predictions_df)
        else:
            st.error(f"Error in API request: {response.status_code} - {response.text}")