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