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
Product_Id=st.text_input("Product_Id")
Product_Weight=st.number_input("Product_Weight", min_value=0.0, step=0.1, value=1.0)
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, step=0.1, value=1.0)
Product_Type=st.selectbox("Product_Type", ["Baking Goods", "Breads", "Breakfast", "Canned", "Diary", "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, step=0.1, value=1.0)
Store_Id=st.selectbox("Store_Id", ["OUT001", "OUT002", "OUT003", "OUT004"])
Store_Establishment_Year=st.number_input("Store_Establishment_Year", min_value=0, step=1, value=1980)
Store_Size=st.selectbox("Store_Size", ["High", "Medium", "Small"])
Store_Location_City_Type=st.selectbox("Store_Location_City_Type", ["Tier 1", "Tier 2", "Tier 3"])
Store_Type=st.selectbox("Store_Type", ["Supermarket Type1", "Grocery Store", "Departmental Store", "Food Mart", "Supermarket Type2"])


# Convert user input into a DataFrame
input_data = pd.DataFrame([{
    'Product_Id': Product_Id,
    '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://crdeepa-SuperKartPredictionBackend.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://crdeepa-SuperKartPredictionBackend.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.")