app.py

import streamlit as st
import pandas as pd
import requests

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

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

# Collect user input for sales features

Product_Weight = st.number_input("Weight of the Product", min_value=1.05, max_value=100.00,value=12.66)
Product_Allocated_Area = st.number_input("Product Allocated Area", min_value= 0.001,max_value= 0.999, value= 0.027)
Product_MRP = st.number_input("MRP of the Product",min_value=1.00, max_value=1000.00, value=117.08)
Product_Sugar_Content = st.selectbox("Sugar content in the Product", ["Low Sugar", "Regular", "No Sugar"])
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"])
Store_Id = st.selectbox("Store ID", ["OUT004", "OUT003", "OUT001", "OUT002"])
Store_Establishment_Year = st.selectbox("Store Estabishment Year", [2009, 1999, 1987, 1998])
Store_Size = st.selectbox("Size of the Store", ["Medium", "High", "Small"])
Store_Location_City_Type = st.selectbox("City type of the Location",["Tier 2", "Tier 1", "Tier 3"])
Store_Type = st.selectbox("Type of the Store", ["Supermarket Type2", "Departmental Store", "Supermarket Type1", "Food Mart"])


# Convert user input into a DataFrame
input_data = pd.DataFrame([{
    'Product_Weight': Product_Weight,
    'Product_Allocated_Area': Product_Allocated_Area,
    'Product_MRP': Product_MRP,
    'Product_Sugar_Content': Product_Sugar_Content,
    'Product_Type': Product_Type,
    '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://kpiitkgp-Sales-PredictionBackend.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 amount (unit)']
        st.success(f"Predicted sales amount (in unit): {prediction}")
    else:
       st.error(f"Error making prediction: {response.text}")

# 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://kpiitkgp-Sales-PredictionBackend.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(f"Error making batch prediction: {response.text}")