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JuanJoseMV commited on 17 days ago

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1 Parent(s): 5a24282

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Files changed (10) hide show

.gitignore +19 -0
README.md +3 -3
app.py +49 -0
requirements.txt +2 -0
sections/demo_section.html +13 -0
sections/project_description.html +43 -0
sections/styles/blocks.css +39 -0
sections/try_it_yourself.html +10 -0
sections/welcome_section.html +5 -0
src/visualization.py +20 -0

.gitignore ADDED Viewed

	@@ -0,0 +1,19 @@

+# Python
+__pycache__/
+*.py[cod]
+*.pyo
+*.pyd
+*.env
+*.venv
+*.egg
+*.egg-info/
+dist/
+build/
+*.log
+# Virtual Environment
+.venv/
+# VSCode
+.vscode/
+*.code-workspace

README.md CHANGED Viewed

@@ -1,8 +1,8 @@
 ---
 title: News Time Series Anomaly Detection
-emoji: 👀
-colorFrom: purple
-colorTo: yellow
 sdk: gradio
 sdk_version: 5.29.0
 app_file: app.py

 ---
 title: News Time Series Anomaly Detection
+emoji: 📉
+colorFrom: green
+colorTo: green
 sdk: gradio
 sdk_version: 5.29.0
 app_file: app.py

app.py ADDED Viewed

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+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)

requirements.txt ADDED Viewed

	@@ -0,0 +1,2 @@


1	+ gradio==5.29.0
2	+ plotly==6.0.1

sections/demo_section.html ADDED Viewed

	@@ -0,0 +1,13 @@

+<section>
+    <h2>🔍 What This Demo Does</h2>
+    <ul>
+        <li><strong>📊 Time Series Visualization</strong><br>Upload your CSV file containing dates and disease mention counts, and visualize the temporal patterns using interactive Plotly charts.</li>
+        <li><strong>🔍 Anomaly Detection</strong><br>Choose from multiple detection methods to identify unusual patterns in your time series:
+            <ul>
+                <li><strong>LSTM:</strong> Uses deep learning to model sequential data and detect anomalies based on deviations from predicted patterns</li>
+                <li><strong>ARIMA:</strong> Employs statistical methods to forecast time series and identify anomalies by comparing actual values to predictions</li>
+                <li><strong>IQR:</strong> Flags anomalies by identifying data points that fall outside the interquartile range</li>
+            </ul>
+        </li>
+    </ul>
+</section>

sections/project_description.html ADDED Viewed

	@@ -0,0 +1,43 @@

+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>TrustAlert: News Anomaly Detection</title>
+    <style>
+        body {
+            font-family: 'Segoe UI', sans-serif;
+            line-height: 1.6;
+            margin: 0;
+            padding: 0;
+            color: #333;
+        }
+        header {
+            background-color: #2a4d69;
+            color: white !important;
+            padding: 20px;
+            text-align: center;
+        }
+        section {
+            padding: 20px;
+            margin: 20px auto;
+            color-scheme: light;
+        }
+        ul {
+            padding-left: 20px;
+        }
+        body, p, ul, li, strong, code {
+            color: #333;
+        }
+    </style>
+</head>
+<body>
+    <header>
+        <h1 style="color:white !important;">🛡️ TrustAlert: News Time Series Anomaly Detection</h1>
+        <p style="color:white !important;">Detecting anomalies in disease-related news coverage using advanced time series analysis</p>
+    </header>
+    <section>
+        <p>This tool analyzes temporal patterns in disease-related news coverage to identify potential outbreaks or unusual events. By detecting anomalies in the frequency of disease mentions, we can help public health officials spot emerging health concerns early.</p>
+    </section>
+</body>
+</html>

sections/styles/blocks.css ADDED Viewed

	@@ -0,0 +1,39 @@

+body {
+    font-family: 'Segoe UI', sans-serif;
+    line-height: 1.6;
+    margin: 0;
+    padding: 0;
+    color: #333;
+}
+h1, h2 {
+    font-family: 'Segoe UI', sans-serif;
+    color: #2a4d69;
+}
+h3 {
+    font-family: 'Segoe UI', sans-serif;
+    color: #4b86b4;
+}
+.gr-button {
+    background: linear-gradient(to right, #4facfe 0%, #00f2fe 100%);
+    font-weight: bold;
+    border-radius: 12px;
+    padding: 10px 16px;
+}
+.gr-button:hover {
+    background: #00c9fe;
+}
+textarea {
+    border-radius: 10px;
+    border: 1px solid #ccc;
+    padding: 12px;
+}
+.gr-box {
+    border-radius: 10px;
+    box-shadow: 0 2px 5px rgba(0,0,0,0.05);
+}

sections/try_it_yourself.html ADDED Viewed

	@@ -0,0 +1,10 @@

+<section>
+    <h2>🧪 Try It Yourself</h2>
+    <ul>
+        <li>📈 Upload a CSV file with two columns: dates in the first column and disease mention counts in the second</li>
+        <li>🎯 Click "Plot Time Series" to visualize your data</li>
+        <li>🔍 Select an anomaly detection method from the dropdown</li>
+        <li>⚡ Click "Detect Anomalies" to identify unusual patterns in your time series</li>
+    </ul>
+    <p>This tool combines time series analysis and anomaly detection to help identify potential disease outbreaks based on news coverage patterns. 💡</p>
+</section>

sections/welcome_section.html ADDED Viewed

	@@ -0,0 +1,5 @@

+<section>
+    <h2>Welcome! 👋</h2>
+    <p>This demo is part of the <strong>TrustAlert: Empowering Public Health with Real-Time Insights and Future Preparedness</strong> project, where we detect unusual patterns in disease-related news coverage that might indicate potential outbreaks or emerging health concerns.</p>
+    <p><strong>AI for early health alerts</strong>, powered by time series analysis and anomaly detection.</p>
+</section>

src/visualization.py ADDED Viewed

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+import pandas as pd
+import plotly.express as px
+def plot_time_series(file):
+    """
+    Plots a time series graph from a CSV file.
+    This function reads the CSV file and generates a line plot
+    showing the disease mentions over time.
+    """
+    df = pd.read_csv(file.name)
+    fig = px.line(
+        df,
+        x=df.columns[0],
+        y=df.columns[1],
+        title='Disease Mentions Over Time'
+    )
+    return fig