import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import gradio as gr

def generate_initial_data(n_rows=50):
    """Generates a synthetic sales DataFrame for simulation."""
    dates = [datetime(2023, 1, 1) + timedelta(days=i) for i in range(n_rows)]
    np.random.seed(42)
    
    data = {
        'Date': dates,
        'Region': np.random.choice(['North', 'South', 'East', 'West'], n_rows),
        'Product': np.random.choice(['A', 'B', 'C', 'D'], n_rows),
        'Sales': np.random.randint(100, 500, n_rows),
        'Quantity': np.random.randint(5, 50, n_rows),
        'Cost': np.random.uniform(10, 50, n_rows).round(2)
    }
    df = pd.DataFrame(data)
    # Calculate Profit
    df['Profit'] = (df['Sales'] - df['Cost']) * df['Quantity']
    return df

def process_data(df, selected_region, min_profit):
    """Filters and aggregates data based on user controls."""
    if df is None:
        return pd.DataFrame(), pd.DataFrame()

    # Ensure data from Gradio component is handled as a DataFrame
    # This is important as gr.State might pass the raw object
    if not isinstance(df, pd.DataFrame):
        df = pd.DataFrame(df)
    
    # Filtering
    if selected_region != "All":
        df = df[df['Region'] == selected_region]
        
    df = df[df['Profit'] >= min_profit]
    
    # Aggregation for Line Plot (Daily Sales Trend)
    if not df.empty:
        df['Date'] = pd.to_datetime(df['Date'])
        daily_summary = df.groupby(df['Date'].dt.date)['Sales'].sum().reset_index()
        daily_summary.rename(columns={'Date': 'Day', 'Sales': 'Total Sales'}, inplace=True)
    else:
        daily_summary = pd.DataFrame(columns=['Day', 'Total Sales'])

    # Aggregation for Bar Plot (Product breakdown)
    product_summary = df.groupby('Product')['Quantity'].sum().reset_index()

    return daily_summary, product_summary

def handle_data_selection(df, evt: gr.SelectData):
    """Handles the selection event on the DataFrame component."""
    if df is None or not evt.index:
        return "No data selected."
        
    if not isinstance(df, pd.DataFrame):
        df = pd.DataFrame(df)

    row_index = evt.index[0]
    
    if row_index >= len(df):
        return "Invalid row selected."
        
    row_data = df.iloc[row_index].to_dict()
    
    output_text = f"Selected Row {row_index} details:\n"
    for key, value in row_data.items():
        # Handle datetime objects for display
        if isinstance(value, (datetime, pd.Timestamp)):
            value = value.strftime('%Y-%m-%d')
        elif isinstance(value, np.generic):
            value = value.item() # Convert numpy types to native Python types
            
        output_text += f"  {key}: {value}\n"
        
    return output_text