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
    total_orders = df['order_id'].nunique()                                # number of unique orders
    total_revenue = df['total_price'].sum()                                # total revenue from orders
    most_popular_size = df['pizza_size'].value_counts().idxmax()           # pizza size with highest frequency
    most_frequent_category = df['pizza_category'].value_counts().idxmax()  # pizza category with highest frequency
    total_pizzas_sold = df['quantity'].sum()                               # Total quantity of pizza Sold  

    # Sidebar with key metrics
    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_id", "y": "quantity", "top": 5},
      {"title": "Quantity of Pizzas Sold by Category and Time of the Day","x": "pizza_category", "y": "quantity" , "hue": "time_of_day" , "estimator": "sum"},
      {"title": "Quantity of Pizzas Sold by Size and Time of the Day","x": "pizza_size", "y": "quantity" , "hue": "time_of_day" , "estimator": "sum"},
      {"title": "Monthly Revenue Trends by Pizza Category", "x": "order_month", "y": "total_price", "hue": "pizza_category", "estimator": "sum", "style": "pizza_category"},
    ]

    for plot in plots:
      st.header(plot["title"])
      
      fig, ax = plt.subplots()
      
      if "Top Selling Pizzas" in plot["title"]:
        data_aux= df.groupby(plot["x"])[plot["y"]].sum().reset_index().sort_values(by=plot["y"],ascending=False).head(plot["top"])
        sns.barplot(data=data_aux, x=plot["x"], y=plot["y"], errorbar=None , order=data_aux[plot["x"]] , ax=ax) 

        plt.xticks(rotation=45)
        plt.tight_layout()  # Adjust spacing so labels fit

      if  "Quantity of Pizzas" in plot["title"]:
         sns.barplot(data=df, x=plot["x"], y=plot["y"], hue=plot["hue"], estimator=plot["estimator"], errorbar=None , ax=ax)

      if "Monthly Revenue" in plot["title"]:
         sns.lineplot(data=df, x=plot["x"], y=plot["y"], hue=plot["hue"], estimator=plot["estimator"] , style=plot["style"] , errorbar=None, markers=True , ax=ax)  
         plt.xticks(rotation=90);

      ax.set_xlabel(" ".join(plot["x"].split("_")).capitalize())
      if "y" in plot.keys():
        ax.set_ylabel(" ".join(plot["y"].split("_")).capitalize())
      else:
        ax.set_ylabel("Quantity")

      if plot.get("hue"):
         ax.legend(bbox_to_anchor=(1, 1))

      st.pyplot(fig)
    

if __name__ == "__main__":
    app()