app.py

import streamlit as st
import pandas as pd
import requests

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

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

# Collect user input for the product and store features

Product_Weight = st.number_input("Weight Of The Product (kg)", min_value=0.0, max_value=25.0, value=5.0, step=0.1)
Product_Sugar_Content = st.selectbox("Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
Product_Allocated_Area = st.number_input("Allocated Display Area Ratio", min_value=0.001, max_value=.5, value=0.01, step=0.001)
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",
                                             "Starchy Foods", "Breakfast", "Seafood", "Others"])
Product_MRP = st.number_input("Maximum Retail Price", min_value=20.0, max_value=300.0, value=100.0, step=1.0)
Store_Id = st.selectbox("Store ID", ["OUT001", "OUT002", "OUT003", "OUT004"])
Store_Size = st.selectbox("Store Size", ["High", "Medium", "Small"])
Store_Location_City_Type = st.selectbox("City Tier", ["Tier 1", "Tier 2", "Tier 3"])
Store_Type = st.selectbox("Store Type", ["Departmental Store", "Supermarket Type1", "Supermarket Type2", "Food Mart"])
Store_Establishment_Year = st.number_input("Store Establishment Year", min_value=1980, max_value=2025, value=2005, step=1)

# 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_Size': Store_Size,
    'Store_Location_City_Type': Store_Location_City_Type,
    'Store_Type': Store_Type,
    'Store_Establishment_Year': Store_Establishment_Year
}])

# Make prediction when the "Predict" button is clicked
if st.button("Predict"):
    response = requests.post("https://sumansaha1980-SuperKartSalesPredictBackend.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 Forcasted 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://sumansaha1980-SuperKartSalesPredictBackend.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.")