import streamlit as st
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd

# Load data
def load_data():
    df = pd.read_csv("processed_data.csv")  # Replace with your dataset
    return df

# Create Streamlit app
def app():
    # Title for the app
    st.title("Pizza Sales Data Analysis Dashboard")
    df = load_data()

    df = pd.DataFrame(df)

    """"""""""""
    # Calculate key metrics
    # Write a code snippet to calculate key metrics from the pizza orders dataframe, including the 
    # total number of unique orders, total revenue generated, the most popular pizza size, the most 
    # frequent pizza category, total pizzas sold
    total_orders = df['order_id'].drop_duplicates()    #function which can calculate the number of unique values
    total_revenue = df['total_price'].sum()  #function which can sum the column
    most_popular_size = df['pizza_size'].value_counts().idxmax()    #function which can get the maximum value 
    most_frequent_category = df['pizza_category'].value_counts().idxmax()    #function which can count of value of each product
    total_pizzas_sold = df['quantity'].sum()


    """"""""""""

    # Sidebar with key metrics
    # Write a code snippet to display key metrics in the sidebar of a Streamlit application. 
    # Show the total number of orders, total revenue (formatted as currency), the most popular
    # pizza size, the most popular pizza category, and the total number of pizzas sold 
    # using the st.sidebar.metric function.
    st.sidebar.header("Key Metrics")
    st.sidebar.metric("Total Orders", total_orders)
    st.sidebar.metric("Total Revenue", f"${total_revenue:,.2f}")
    st.sidebar.metric("Most Popular Size", most_popular_size)
    st.sidebar.metric("Most Popular Category", most_frequent_category)
    st.sidebar.metric("Total Pizzas Sold", total_pizzas_sold)

    

    """""""""""""

    # Provide the details of the plots here
    
    plots = [
        {"title": "Top Selling Pizzas (by Quantity)", "x": "pizza_name", "y": "quantity", "top": 5},    #Write the appropriiate column as per the title given
        {"title": "Quantity of Pizzas Sold by Category and Time of the Day", "x": "order_time", "hue": "pizza_category"},   #Write the appropriiate column as per the title given
        {"title": "Quantity of Pizzas Sold by Size and Time of the Day", "x": "order_time", "hue": "pizza_size"},  #Write the appropriiate column as per the title given
        {"title": "Monthly Revenue Trends by Pizza Category", "x": "order_date", "y": "total_price", "hue": "pizza_category", "estimator": "sum", "marker": "o"}, #Write the appropriiate column as per the title given
    ]

    for plot in plots:
      st.header(plot["title"])
      
      fig, ax = plt.subplots()
      
      if "Top Selling" in plot["title"]:
        sns.countplot(data=df, x=plot["x"], y=plot["y"],top=plot["top"])
      if "Category" in plot["title"]:
        sns.countplot(data=df, x=plot["x"], hue=plot["hue"])
      if "Size" in plot["title"]:
        sns.countplot(data=df, x=plot["x"], hue=plot["hue"])
      if "Revenue" in plot["title"]:
        sns.countplot(data=df, x=plot["x"], y=plot["y"],ax=plot["top"], hue=plot["hue"], estimator=plot["estimator"], marker=plot["marker"])

      else:
        ax.set_ylabel("quantity")
      ax.legend(bbox_to_anchor=(1,1)) 
     

      st.pyplot(fig)
      plt.show()
    

if __name__ == "__main__":
    app()