app.py

import streamlit as st
import pandas as pd
import requests

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

# Collect user input, default value as average of provided values, or alphabetical order
product_weight = st.number_input("Weight of the product", min_value=0.0, value=12.0)
product_allocated_area = st.number_input("Product Allocated Area",min_value=0.0,value=0.068,step=0.001,format="%.3f")
product_mrp = st.number_input("Product MRP", min_value=1.0, step=1.0, value=147.0)
store_establishment_year = st.selectbox("Store Establishment Year", [1987, 1998, 1999, 2009])
product_sugar_content = st.selectbox("Product Sugar Content", ["Low Sugar", "No Sugar", "Regular"])
product_type = st.selectbox("Product Type", ["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"])
store_id = st.selectbox("Store ID", ["OUT004", "OUT003", "OUT001","OUT002"])
store_size = st.selectbox("Store Size", ["Small", "Medium", "High"])
store_location_city_type = st.selectbox("Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"])
store_type = st.selectbox("Store Type", ["Departmental Store", "Food Mart", "Supermarket Type1", "Supermarket Type2"])

# Create input dictionary
input_data = {
    'Product_Weight': product_weight,
    'Product_Allocated_Area': product_allocated_area,
    'Product_MRP': product_mrp,
    'Store_Establishment_Year': store_establishment_year,
    'Product_Sugar_Content': product_sugar_content,
    'Product_Type': product_type,
    'Store_Id': store_id,
    '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"):
    try:
        response = requests.post(
            "https://maddykan101-SalesPredictionBackend.hf.space/v1/prediction",
            json=input_data
        )
        if response.status_code == 200:
            prediction = response.json()['Predicted Sales ']  # Adjust key if needed
            st.success(f"Predicted Sales: {prediction}")
        else:
            st.error(f"Prediction failed: {response.status_code}")
            st.error(f"Prediction failed--: {response}")
    except Exception as e:
        st.error(f"An error occurred: {str(e)}")