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

client = supabase.create_client(
   "https://uddbzgupnpujsabrgbyk.supabase.co",
   "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJzdXBhYmFzZSIsInJlZiI6InVkZGJ6Z3VwbnB1anNhYnJnYnlrIiwicm9sZSI6InNlcnZpY2Vfcm9sZSIsImlhdCI6MTcxODgxOTE3NywiZXhwIjoyMDM0Mzk1MTc3fQ.OH-NJ64dHhr0WPMZU05TsSBfRkoiX29W_cFHYqnN0us"

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

df = read_data()
#df.head()

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

# 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

#Function to create Line Plot
def create_lineplot(x_column, y_column):
    plt.figure(figsize=(10, 6))
    sns.lineplot(data=df,x=x_column, y=y_column)
    plt.title(f'Line  Plot of {y_column} vs {x_column}')
    plt.xlabel(x_column)
    plt.ylabel(y_column)
    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)
    elif plot_type == "Line Plot":
        return create_lineplot(x_column, y_column)

# Create Gradio interface
iface = gr.Interface(
    fn=visualize,
    inputs=[
        gr.Dropdown(["Histogram", "Scatter Plot", "Box Plot", "Bar Plot","Line 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/Line Plot)")

    ],
    outputs="plot",
    title="Plant Growth Data Visualization Dashboard",
    description="This dashboard displays various visualizations of the plant growth dataset.",
)

# Launch the interface
iface.launch()