app.py

import streamlit as st
import pandas as pd
import requests

model_root_url = "https://Fitjv-StoresalesPredictionBackend.hf.space"
model_predict_url = model_root_url+"/v1/sales"  # Base URL of the deployed Flask API on Hugging Face Spaces
model_batch_url = model_root_url+"/v1/salesbatch"

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

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

# Collect user input for property features
Product_Weight = st.number_input("Product_Weight", min_value=0.1, max_value=100.0, step=1.0, value=20.0)
Product_Sugar_Content = st.selectbox("Product_Sugar_Content", ["Low Sugar", "Regular", "No Sugar","reg"])
Product_Allocated_Area = st.number_input("Product_Allocated_Area", min_value=0.001, step=0.01, value=0.2,max_value=1.0)
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"])
Product_MRP = st.number_input("Product_MRP", min_value=0.1,max_value=10000.0, step=1.0, value=100.0)
Store_Id = st.selectbox("Store_Id", ["OUT001", "OUT002", "OUT003","OUT004"])
Store_Establishment_Year = st.number_input("Store_Establishment_Year", min_value=1800, max_value=2025,step=1, value=2005)
Store_Size = st.selectbox("Store_Size", ["Small", "Medium", "High"])
Store_Type = st.selectbox("Store_Type", ["Food Mart", "Supermarket Type1", "Supermarket Type2"])
Store_Location_City_Type = st.selectbox("Store_Location_City_Type", ["Tier 1", "Tier 2", "Tier 3"])

# 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_Type': Store_Type,
    'Store_Location_City_Type': Store_Location_City_Type
}])

# Make prediction when the "Predict" button is clicked
if st.button("Predict"):
    response = requests.post(model_predict_url, 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 Price: {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(model_batch_url, 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.")