app.py

import streamlit as st
import pandas as pd
import requests

# Set the title of the Streamlit app
st.title("Super Kart Sales Prediction")

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

# Collect user input for Product features
Product_Weight = st.number_input("Product_Weight in KG", min_value=4.00, max_value=22.00, step=0.01, value=12.65)
Product_Sugar_Content = st.selectbox("Sugar Content", ["Low Sugar", "No Sugar", "Regular","reg"])
Product_Allocated_Area = st.number_input("Product Display Area", min_value=0.004, max_value=0.298, step=0.001, value=0.068)
Product_Type = st.selectbox("Type of Product", ["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 Price", min_value=31.00, max_value=266.00, step=0.01, value=147.00)
Store_Id = st.selectbox("Store ID", ["OUT001", "OUT002", "OUT003","OUT004"])
Store_Establishment_Year = st.selectbox("Store Opening Year", [1987, 1998, 1999, 2009])
Store_Size = st.selectbox("Store Size Category", ["Small", "High", "Medium"])
Store_Location_City_Type = st.selectbox("Store Location City Tier", ["Tier 1", "Tier 2", "Tier 3"])
Store_Type = st.selectbox("Type of Store", ["Departmental Store", "Supermarket Type1", "Supermarket Type2", "Food Mart"])

# 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_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://SRGL-SuperKartSalesPredictionBackend.hf.space/v1/superkart", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
    if response.status_code == 200:
        prediction = response.json()['Predicted Super Kart Sale (in dollars)']
        st.success(f"Predicted Super Kart Sale (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://raj2809-SuperKartSalesPredictionBackend.hf.space/v1/superkartbatch", files={"file": (uploaded_file.name,uploaded_file.getvalue(),uploaded_file.type)})
  # 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.")