import gradio as gr
import supabase
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

client = supabase.create_client(
   "https://tmjhrfjckqnlvqnsspnr.supabase.co",
    "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJzdXBhYmFzZSIsInJlZiI6InRtamhyZmpja3FubHZxbnNzcG5yIiwicm9sZSI6ImFub24iLCJpYXQiOjE3MjExMjE1NTgsImV4cCI6MjAzNjY5NzU1OH0.E34R6qPWavp2uRWKinZQICgdEqRjov46VnE38F24Al8"

                   )

def read_data():
    response = client.table('Customer_purchase_dataset').select("*").execute()
    df = pd.DataFrame(response.data)
    return df

df= read_data()
#print(df.head)
#print(df.dtypes)

# Convert Gender to categorical
df['Gender'] = df['Gender'].map({0: 'Female', 1: 'Male'})

# Convert LoyaltyProgram to categorical
df['LoyaltyProgram'] = df['LoyaltyProgram'].map({0: 'No', 1: 'Yes'})

# Convert PurchaseStatus to categorical
df['PurchaseStatus'] = df['PurchaseStatus'].map({0: 'Not Purchased', 1: 'Purchased'})

# Function to create histogram
def create_histogram(column):
    plt.figure(figsize=(10, 6))
    sns.histplot(data=df, x=column, kde=True)
    plt.title(f'Histogram of {column}')
    plt.xlabel(column)
    plt.ylabel('Count')
    return plt

# Function to create scatter plot
def create_scatter(x_column, y_column, hue_column):
    plt.figure(figsize=(10, 6))
    sns.scatterplot(data=df, x=x_column, y=y_column, hue=hue_column)
    plt.title(f'{x_column} vs {y_column} (colored by {hue_column})')
    plt.xlabel(x_column)
    plt.ylabel(y_column)
    return plt

# Function to create box plot
def create_boxplot(x_column, y_column):
    plt.figure(figsize=(10, 6))
    sns.boxplot(data=df, x=x_column, y=y_column)
    plt.title(f'Box Plot of {y_column} by {x_column}')
    plt.xlabel(x_column)
    plt.ylabel(y_column)
    return plt

# Function to create bar plot
def create_barplot(x_column, y_column):
    plt.figure(figsize=(10, 6))
    sns.barplot(data=df, x=x_column, y=y_column)
    plt.title(f'Bar Plot of {y_column} by {x_column}')
    plt.xlabel(x_column)
    plt.ylabel(y_column)
    plt.xticks(rotation=45)
    return plt

# Gradio interface
def visualize(plot_type, x_column, y_column, hue_column):
    if plot_type == "Histogram":
        return create_histogram(x_column)
    elif plot_type == "Scatter Plot":
        return create_scatter(x_column, y_column, hue_column)
    elif plot_type == "Box Plot":
        return create_boxplot(x_column, y_column)
    elif plot_type == "Bar Plot":
        return create_barplot(x_column, y_column)

# Create Gradio interface
iface = gr.Interface(
    fn=visualize,
    inputs=[
        gr.Dropdown(["Histogram", "Scatter Plot", "Box Plot", "Bar Plot"], label="Plot Type"),
        gr.Dropdown(df.columns.tolist(), label="X-axis"),
        gr.Dropdown(df.columns.tolist(), label="Y-axis"),
        gr.Dropdown(df.columns.tolist(), label="Hue (for Scatter Plot)")

    ],
    outputs="plot",
    title="Customer Purchase Data Visualization Dashboard",
    description="Explore the customer purchase dataset through various visualizations."
)

# Launch the interface
iface.launch()