# app.py

import streamlit as st
import pandas as pd
import numpy as np
import joblib
from datetime import datetime

# Assuming the necessary EDA functions are defined in eda.py and imported here
from eda import (average_sales_by_region, average_sales_and_profit_over_time,
                 segment_vs_region_distribution, sales_vs_profit_across_segments,
                 category_composition_for_profit_and_sales)

# Load the model for predictions
model = joblib.load('best_model.pkl')

# Load the dataset for EDA
@st.cache_data
def load_data():
    return pd.read_csv('superstore_clean.csv')

df = load_data()

# Sidebar for navigation
st.sidebar.title("Navigation")
selection = st.sidebar.radio("Go to", ["Home", "EDA", "Make a Prediction"])

if selection == "Home":
    st.title("Welcome to the Superstore Sales Dashboard")

elif selection == "EDA":
    st.title("Exploratory Data Analysis (EDA)")
    # Display EDA plots directly here or call a function that does
    average_sales_by_region(df)
    average_sales_and_profit_over_time(df)
    segment_vs_region_distribution(df)
    sales_vs_profit_across_segments(df)
    category_composition_for_profit_and_sales(df)

elif selection == "Make a Prediction":
    st.title("Make a Sales Prediction")
    with st.form("input_form"):

        # Capture all inputs as per the original dataset
        order_date = st.date_input('Order Date', datetime.now())
        ship_date = st.date_input('Ship Date', datetime.now() + timedelta(days=1))  # Assume shipping the next day
        ship_mode = st.selectbox('Ship Mode', ['First Class', 'Second Class', 'Standard Class', 'Same Day'], index=2)  # Default to Standard Class
        segment = st.selectbox('Segment', ['Consumer', 'Corporate', 'Home Office'], index=0)  # Default to Consumer
        country = st.text_input('Country', value='United States')
        city = st.text_input('City', value='Los Angeles')  # Example city
        state = st.text_input('State', value='California')  # Example state
        postal_code = st.text_input('Postal Code', value='90001')  # Example postal code
        region = st.selectbox('Region', ['South', 'West', 'Central', 'East'], index=1)  # Default to West
        category = st.selectbox('Category', ['Furniture', 'Office Supplies', 'Technology'], index=1)  # Default to Office Supplies
        sub_category = st.selectbox('Sub-Category', ['Bookcases', 'Chairs', 'Labels', 'Tables', 'Storage', 'Furnishings', 'Art', 'Phones', 'Binders', 'Appliances', 'Paper', 'Accessories', 'Envelopes', 'Fasteners', 'Supplies', 'Machines', 'Copiers'], index=10)  # Default to Paper
        product_name = st.text_input('Product Name', value='Staple papers')  # Example product
        sales = st.number_input('Sales', value=100.0, format="%.2f")  # Example sales amount
        quantity = st.number_input('Quantity', value=2, format="%d")  # Example quantity
        discount = st.number_input('Discount', value=0.0, format="%.2f")  # Example discount


        submit_button = st.form_submit_button("Predict")

    if submit_button:
        # Construct the input DataFrame
        input_features = pd.DataFrame([[sub_category, sales, quantity, discount
        ]], columns=[
            'Sub-Category', 'Product Name', 'Sales', 'Quantity', 'Discount'
        ])
        
        # Preprocess and predict (You'll need to adjust this part based on how your model expects input)
        # For example, you might need to transform 'input_features' to match the expected input format of your model
        predicted_profit = model.predict(input_features)  # Adjust this line as necessary
        
        st.write(f'Predicted Profit: {predicted_profit:.2f}')