app.py

import matplotlib.pyplot as plt
import matplotlib
import pandas as pd
import gradio as gr
import random

from utils import logger


# Configure matplotlib to prevent memory warnings and set dark background
matplotlib.rcParams['figure.facecolor'] = '#000000'
matplotlib.rcParams['axes.facecolor'] = '#000000'
matplotlib.rcParams['savefig.facecolor'] = '#000000'
plt.ioff()  # Turn off interactive mode to prevent figure accumulation


# Function to generate random data for the plot
def generate_random_data():
    """Generate random data points for the scatter plot."""
    n_points = random.randint(20, 50)  # Random number of points
    x_data = [random.uniform(-100, 100) for _ in range(n_points)]
    y_data = [random.uniform(-100, 100) for _ in range(n_points)]
    return pd.DataFrame({"x": x_data, "y": y_data})


# Function to get current description text
def get_description_text():
    """Get description text."""
    msg = [
        "Random Data Dashboard",
        "-",
        "Click summary to refresh data",
        "Demo visualization",
    ]
    msg = ["**" + x + "**" for x in msg] + [""]
    msg.append("*Random scatter plot data*<br>*Updated on each click*")
    return "<br>".join(msg)

# Load CSS from external file
def load_css():
    try:
        with open("styles.css", "r") as f:
            css_content = f.read()
        
        return css_content
    except FileNotFoundError:
        logger.warning("styles.css not found, using minimal default styles")
        return "body { background: #000; color: #fff; }"


# Create the Gradio interface with sidebar and dark theme
with gr.Blocks(title="Model Test Results Dashboard", css=load_css(), fill_height=True, fill_width=True) as demo:


    with gr.Row():
        # Sidebar for model selection
        with gr.Column(scale=1, elem_classes=["sidebar"]):
            gr.Markdown("# 🤖 TCID", elem_classes=["sidebar-title"])

            # Description with integrated last update time
            description_text = get_description_text()
            description_display = gr.Markdown(description_text, elem_classes=["sidebar-description"])

            # Summary button
            summary_button = gr.Button(
                "summary\n📊",
                variant="primary",
                size="lg",
                elem_classes=["summary-button"]
            )


        # Main content area
        with gr.Column(elem_classes=["main-content"]):
            # Summary display (default view)
            summary_display = gr.ScatterPlot(
                generate_random_data(),
                x = "x",
                y = "y",
                height="100vh",
                container=False,
                show_fullscreen_button=True,
                elem_classes=["plot-container"],
            )



    # Summary button click handler
    def refresh_plot_data():
        """Generate new random data for the plot."""
        new_data = generate_random_data()
        return new_data, get_description_text()

    summary_button.click(
        fn=refresh_plot_data,
        outputs=[summary_display, description_display]
    )

    # Auto-update description when the interface loads
    demo.load(
        fn=get_description_text,
        outputs=[description_display]
    )


# Gradio entrypoint
if __name__ == "__main__":
    demo.launch()