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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()
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