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Update app.py
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app.py
CHANGED
@@ -135,33 +135,93 @@ def process_live_frame(frame):
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return processed, status_txt, None
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if __name__ == "__main__":
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logging.info("Launching Gradio appβ¦")
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else:
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return processed, status_txt, None
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# βββββββββββββββββββββββββββββ UI Definition
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def create_readme_tab():
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"""Creates the content for the 'About' tab."""
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with gr.Blocks(title="Drive Paddy - About Page") as readme_tab:
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gr.Markdown(
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"""
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<div align="center">
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<img src="https://em-content.zobj.net/source/samsung/380/automobile_1f697.png" alt="Car Emoji" width="100"/>
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<h1>Drive Paddy</h1>
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<p><strong>Your Drowsiness Detection Assistant</strong></p>
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</div>
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---
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## π Features
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- **Real-Time Webcam Streaming**: Directly processes your live camera feed for immediate feedback.
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- **Efficient Geometric Analysis**: Uses `MediaPipe` for high-performance facial landmark detection.
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- **Multi-Signal Analysis**: Detects eye closure (EAR), yawns (MAR), and head-nodding.
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- **Stateful Alert System**: Plays a clear audio alert for new drowsiness events and intelligently re-arms itself, preventing alert fatigue.
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- **Low-Light Warning**: Automatically detects and warns about poor lighting conditions.
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- **Configurable**: Key detection thresholds and settings can be tuned via `config.yaml`.
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---
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## π οΈ How It Works
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1. **Video Streaming**: The `gradio.Image` component captures the camera feed.
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2. **Frame Processing**: Each frame is sent to the `GeometricProcessor`.
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3. **Stateful Alerting**: The `AlertManager` class uses internal state to decide if a *new* alert should be triggered.
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4. **Dynamic Updates**: The processed video, status text, and audio alerts are sent back to the frontend for a seamless real-time experience.
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---
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## π‘ Understanding the Live Status
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The status panel provides real-time feedback on the following parameters:
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- **`Lighting`**: Indicates the ambient light conditions.
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- `Good`: Sufficient light for reliable detection.
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- `Low`: Insufficient light. Detection is paused as the results would be unreliable.
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- **`Status`**: The overall assessed level of driver alertness.
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- `Awake`: The driver appears alert.
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- `Slightly Drowsy`: Early signs of fatigue have been detected.
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- `Very Drowsy`: Strong indicators of drowsiness are present. An alert is triggered.
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- **`Score`**: A numerical value representing the accumulated evidence of drowsiness based on the weighted indicators (eye closure, yawning, head pose). A higher score corresponds to a greater level of detected drowsiness.
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"""
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)
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return readme_tab
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# βββββββββββββββββββββββββββββ UI <--- CHANGE
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def create_detection_tab():
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with gr.Blocks(title="Drive Paddy β πΉ Live Drowsiness Detection Tab") as detection_tab:
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gr.Markdown("## πΉ Live Drowsiness Detection")
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gr.Markdown("Press 'START' to activate your camera and begin monitoring. The console will show real-time logs.")
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with gr.Row():
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with gr.Column(scale=2):
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cam = gr.Image(sources=["webcam"], streaming=True, label="Live Camera Feed")
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with gr.Column(scale=1):
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out_img = gr.Image(label="Processed Feed")
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out_text = gr.Textbox(label="Live Status", lines=3, interactive=False)
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# This audio component now acts as a placeholder.
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# We make it invisible because we don't need to show the player controls.
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# The backend will dynamically send a new, playable component to it.
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out_audio = gr.Audio(
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label="Alert",
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autoplay=True,
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visible=False, # Hiding the component for a cleaner UX
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)
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# The gr.State for managing the timestamp is no longer needed, simplifying the stream call.
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cam.stream(
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fn=process_live_frame,
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inputs=[cam],
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outputs=[out_img, out_text, out_audio] # The output now targets the placeholder
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)
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with gr.Blocks(title="π Drive Paddy β Drowsiness Detection", theme=gr.themes.Soft()) as app:
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gr.Markdown("# π **Drive Paddy**")
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with gr.Tabs():
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with gr.TabItem("Live Detection"):
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create_detection_tab()
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with gr.TabItem("About this App"):
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create_readme_tab()
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if __name__ == "__main__":
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logging.info("Launching Gradio appβ¦")
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