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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()
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