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
Store_Location_City_Type = st.selectbox("Store_Location_City_Type", ["Tier 1", "Tier 2", "Tier 3"])
Store_Size = st.selectbox("Store_Size", ["Small", "Medium", "High"])
Store_Establishment_Year = st.selectbox("Store_Establishment_Year", ["1987", "1998","1999", "2009"])
Store_Type = st.selectbox("Store_Type", ["Supermarket Type2", "Departmental Store", "Supermarket Type1","Food Mart"])
Product_Weight = st.number_input("Product_Weight")
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=0.0, max_value=0.298)
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")
Store_Id = st.selectbox("Store_Id", ["OUT001", "OUT002", "OUT003","OUT004"])

# Convert user input into a DataFrame
input_data = pd.DataFrame([{
    'Store_Location_City_Type' : Store_Location_City_Type,
    'Store_Size' : Store_Size,
    'Store_Establishment_Year' : Store_Establishment_Year,
    'Store_Type' : Store_Type,
    '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
}])

# Make prediction when the "Predict" button is clicked
if st.button("Predict"):
    response = requests.post("https://dishantkalra-salesproject.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']
        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://dishantkalra-salesproject.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.")