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import gradio as gr
from src import plot_time_series
from src import detect_anomalies
from src.utils import update_controls
PROJECT_HTML_PATH = "sections/project_description.html"
WELCOME_HTML_PATH = "sections/welcome_section.html"
DEMO_HTML_PATH = "sections/demo_section.html"
TRY_IT_YOURSELF_HTML_PATH = "sections/try_it_yourself.html"
CSS_FILE_PATH = "sections/styles/blocks.css"
ANOMALY_METHODS = ["LSTM", "ARIMA", "IQR"]
with open(PROJECT_HTML_PATH, "r") as file:
project_description_html = file.read()
with open(WELCOME_HTML_PATH, "r") as file:
welcome_html = file.read()
with open(DEMO_HTML_PATH, "r") as file:
demo_section_html = file.read()
with open(TRY_IT_YOURSELF_HTML_PATH, "r") as file:
try_it_yourself_html = file.read()
with open(CSS_FILE_PATH, "r") as css_file:
blocks_css = css_file.read()
with gr.Blocks(css=blocks_css) as demo:
gr.HTML(project_description_html)
gr.Markdown(welcome_html)
gr.Markdown(demo_section_html)
gr.HTML(try_it_yourself_html)
file_input = gr.File(
label="Upload Time Series CSV",
file_types=[".csv"],
value="examples/mpox.csv" # Set default example file
)
plot_btn = gr.Button("Plot Time Series")
plot_output = gr.Plot(label="Time Series Plot")
with gr.Row():
method_input = gr.Dropdown(
choices=ANOMALY_METHODS,
interactive=True,
label="Select Anomaly Detection Method",
value="LSTM"
)
k_input = gr.Slider(
minimum=1,
maximum=3,
step=0.1,
label="k",
value=1.5
)
percentile_input = gr.Slider(
minimum=0,
maximum=100,
step=1,
label="Percentile",
value=95,
interactive=True
)
threshold_method_input = gr.Dropdown(
choices=[
"IQR on (ground truth - forecast)",
"IQR on |ground truth - forecast|",
"IQR on |ground truth - forecast|/forecast",
"Percentile threshold on absolute loss",
"Percentile threshold on raw loss"
],
label="Threshold Method",
value="IQR on (ground truth - forecast)",
interactive=True
)
analyze_btn = gr.Button("Detect Anomalies")
anomaly_output = gr.Plot(label="Anomaly Detection Results")
plot_btn.click(
fn=plot_time_series,
inputs=[file_input],
outputs=[plot_output]
)
method_input.change(
fn=update_controls,
inputs=[method_input],
outputs=[percentile_input, threshold_method_input]
)
analyze_btn.click(
fn=detect_anomalies,
inputs=[file_input, method_input, k_input, percentile_input, threshold_method_input],
outputs=[anomaly_output]
)
demo.launch(show_api=False) |