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Update app.py
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app.py
CHANGED
@@ -548,10 +548,10 @@ def classical_prediction(file_path, model_choice, min_mmsi, max_mmsi, models, lo
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positions_csv_path = tmp_positions_file.name
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logging.info("Classical prediction completed.")
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return metrics_json, positions_csv_path, inference_time
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except Exception as e:
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logging.error(f"An error occurred: {str(e)}")
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return
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# ============================
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# Abnormal Behavior Detection
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@@ -650,10 +650,10 @@ def abnormal_behavior_detection(prediction_file_path, alpha=0.5, threshold=10.0)
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abnormal_csv_path = tmp_abnormal_file.name
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logging.info("Abnormal behavior detection completed.")
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return abnormal_csv_path
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except Exception as e:
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logging.error(f"An error occurred: {str(e)}")
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return
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# ============================
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# Define Gradio Interface
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@@ -698,8 +698,9 @@ def main():
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],
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outputs=[
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gr.JSON(label="Classical Metrics (Degrees)"),
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gr.File(label="Download Predicted & Real Positions CSV"
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gr.Number(label="Inference Time (seconds)")
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],
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title="Classical Prediction & Metrics",
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description="Upload a CSV file and select a model to get classical evaluation metrics such as MAE, MSE, RMSE. The inference time is also provided."
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@@ -714,7 +715,8 @@ def main():
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gr.Number(label="Threshold", value=10.0)
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],
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outputs=[
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gr.File(label="Download Abnormal Behavior CSV",
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],
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title="Abnormal Behavior Detection",
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description=(
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positions_csv_path = tmp_positions_file.name
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logging.info("Classical prediction completed.")
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return metrics_json, positions_csv_path, inference_time, None
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except Exception as e:
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logging.error(f"An error occurred: {str(e)}")
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return None, None, None, str(e)
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# ============================
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# Abnormal Behavior Detection
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abnormal_csv_path = tmp_abnormal_file.name
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logging.info("Abnormal behavior detection completed.")
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return abnormal_csv_path, None
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except Exception as e:
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logging.error(f"An error occurred: {str(e)}")
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return None, str(e)
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# ============================
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# Define Gradio Interface
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],
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outputs=[
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gr.JSON(label="Classical Metrics (Degrees)"),
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gr.File(label="Download Predicted & Real Positions CSV"),
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gr.Number(label="Inference Time (seconds)"),
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gr.Textbox(label="Error Message", lines=2, visible=False)
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],
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title="Classical Prediction & Metrics",
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description="Upload a CSV file and select a model to get classical evaluation metrics such as MAE, MSE, RMSE. The inference time is also provided."
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gr.Number(label="Threshold", value=10.0)
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],
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outputs=[
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gr.File(label="Download Abnormal Behavior CSV"),
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gr.Textbox(label="Error Message", lines=2, visible=False)
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],
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title="Abnormal Behavior Detection",
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description=(
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