Update app.py
Browse files
app.py
CHANGED
@@ -210,9 +210,9 @@ with gr.Blocks(title="F1 Telemetry Data Analyzer", theme=gr.themes.Soft()) as de
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gr.Markdown("# ๐๏ธ F1 Telemetry Data Analyzer")
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gr.Markdown("Advanced AI-powered analysis of Formula 1 telemetry data with anomaly detection and predictive modeling.")
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with gr.Tab("
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gr.Markdown("### Generate and analyze telemetry data")
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analyze_btn = gr.Button("
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with gr.Row():
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with gr.Column(scale=2):
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@@ -225,7 +225,7 @@ with gr.Blocks(title="F1 Telemetry Data Analyzer", theme=gr.themes.Soft()) as de
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outputs=[plot_output, report_output]
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)
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with gr.Tab("
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gr.Markdown("### Predict tire performance and fuel consumption")
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gr.Markdown("*Note: Run the analysis first to train the models*")
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@@ -249,29 +249,29 @@ with gr.Blocks(title="F1 Telemetry Data Analyzer", theme=gr.themes.Soft()) as de
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outputs=[tire_pred_output, fuel_pred_output]
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)
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with gr.Tab("
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gr.Markdown("""
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## About This Tool
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This F1 Telemetry Data Analyzer demonstrates advanced AI techniques used in Formula 1 racing:
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-
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- Uses Isolation Forest algorithm to detect unusual patterns in telemetry data
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- Identifies potential mechanical issues or performance anomalies
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- Helps engineers spot problems before they become critical
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-
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- Machine learning models predict tire degradation and fuel consumption
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- Based on real-time telemetry inputs (speed, throttle, temperatures)
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- Enables strategic decision-making during races
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-
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- Real-time telemetry processing simulation
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- Advanced visualization of racing data
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- Performance prediction for race strategy
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- Anomaly detection for preventive maintenance
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-
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- Python with scikit-learn for ML models
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- Isolation Forest for anomaly detection
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- Linear regression for performance prediction
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gr.Markdown("# ๐๏ธ F1 Telemetry Data Analyzer")
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gr.Markdown("Advanced AI-powered analysis of Formula 1 telemetry data with anomaly detection and predictive modeling.")
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with gr.Tab("Data Analysis"):
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gr.Markdown("### Generate and analyze telemetry data")
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analyze_btn = gr.Button("Analyze Telemetry Data", variant="primary")
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with gr.Row():
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with gr.Column(scale=2):
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outputs=[plot_output, report_output]
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)
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with gr.Tab("Performance Prediction"):
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gr.Markdown("### Predict tire performance and fuel consumption")
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gr.Markdown("*Note: Run the analysis first to train the models*")
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outputs=[tire_pred_output, fuel_pred_output]
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)
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with gr.Tab("About"):
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gr.Markdown("""
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## About This Tool
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This F1 Telemetry Data Analyzer demonstrates advanced AI techniques used in Formula 1 racing:
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**Anomaly Detection:**
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- Uses Isolation Forest algorithm to detect unusual patterns in telemetry data
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- Identifies potential mechanical issues or performance anomalies
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- Helps engineers spot problems before they become critical
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**Predictive Modeling:**
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- Machine learning models predict tire degradation and fuel consumption
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- Based on real-time telemetry inputs (speed, throttle, temperatures)
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- Enables strategic decision-making during races
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**Key Features:**
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- Real-time telemetry processing simulation
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- Advanced visualization of racing data
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- Performance prediction for race strategy
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- Anomaly detection for preventive maintenance
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**Technical Stack:**
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- Python with scikit-learn for ML models
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- Isolation Forest for anomaly detection
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- Linear regression for performance prediction
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