import streamlit as st
import pandas as pd
import requests
import json

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

# Section for online prediction
st.subheader("Predict Single Product Sales")

# Collect user input for product and store features
product_id = st.text_input("Product ID", value="FD6114")
product_weight = st.number_input("Product Weight", min_value=0.0, value=12.66, step=0.1)
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, value=0.027, step=0.001)
product_type = st.selectbox("Product Type", ['Frozen Foods', 'Dairy', 'Canned', 'Baking Goods', 'Health and Hygiene', 'Household', 'Meat', 'Soft Drinks', 'Breads', 'Hard Drinks', 'Others', 'Starchy Foods', 'Breakfast', 'Seafood', 'Fruits and Vegetables', 'Snack Foods'])
product_mrp = st.number_input("Product MRP", min_value=0.0, value=117.08, step=0.01)
store_id = st.selectbox("Store ID", ["OUT004", "OUT003", "OUT001", "OUT002"])
store_establishment_year = st.number_input("Store Establishment Year", min_value=1900, max_value=2025, value=2009, step=1)
store_size = st.selectbox("Store Size", ["Medium", "High", "Small"])
store_location_city_type = st.selectbox("Store Location City Type", ["Tier 2", "Tier 1", "Tier 3"])
store_type = st.selectbox("Store Type", ["Supermarket Type2", "Departmental Store", "Supermarket Type1", "Food Mart"])


# Convert user input into a dictionary
input_data = {
    "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"):
    # Replace with your actual Hugging Face Space backend URL
    backend_url = "https://retheesh-superkartsalesprediction.hf.space/predict_sales" # Replace with your space URL and endpoint

    try:
        response = requests.post(backend_url, json=input_data)

        if response.status_code == 200:
            prediction = response.json().get('predicted_sales')
            if prediction is not None:
                st.success(f"Predicted Product Store Sales Total: {prediction:.2f}")
            else:
                st.error("Prediction not found in the response.")
                st.json(response.json()) # Display the full response for debugging
        else:
            st.error(f"Error predicting sales. Status code: {response.status_code}")
            st.write("Response body:", response.text) # Display response text for debugging
            st.json(response.json()) # Display response json for debugging
    except requests.exceptions.RequestException as e:
        st.error(f"Error connecting to the backend API: {e}")