app.py

import gradio as gr
from src.visualization import plot_time_series

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")
    plot_btn = gr.Button("Plot Time Series")
    plot_output = gr.Plot(label="Time Series Plot")

    method_input = gr.Dropdown(choices=ANOMALY_METHODS, label="Select Anomaly Detection Method", value="LSTM")
    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]
    )

    # analyze_btn.click(
    #     fn=detect_anomalies, 
    #     inputs=[file_input, method_input], 
    #     outputs=[anomaly_output]
    # )

demo.launch(show_api=False)