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Update app.py
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app.py
CHANGED
@@ -59,7 +59,7 @@ def compute_f1_score(cm):
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f1 = np.nan_to_num(f1) # Replace NaN with 0
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return np.mean(f1) # Return the mean F1-score across all classes
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def plot_confusion_matrix_beamPred(cm, classes, title, save_path, user_mode="
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# Compute the average F1-score
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avg_f1 = compute_f1_score(cm)
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@@ -559,21 +559,12 @@ with gr.Blocks(css="""
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task_complexity_dropdown = gr.Dropdown(label="Task Complexity (Number of Beams)", choices=[16, 32, 64, 128, 256], value=16)
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with gr.Row():
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mode_input = gr.Textbox(visible=False) # Hidden input to capture user mode
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gr.Markdown("""
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<script>
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const userPrefersDark = window.matchMedia && window.matchMedia('(prefers-color-scheme: dark)').matches;
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document.querySelector('input[name="mode_input"]').value = userPrefersDark ? 'dark' : 'light';
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</script>
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""")
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raw_img_bp = gr.Image(label="Raw Channels", type="pil", width=300, height=500)
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embeddings_img_bp = gr.Image(label="Embeddings", type="pil", width=300, height=500)
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# Update the confusion matrices whenever sliders change
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data_percentage_slider.change(fn=beam_prediction_task, inputs=[data_percentage_slider, task_complexity_dropdown
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task_complexity_dropdown.change(fn=beam_prediction_task, inputs=[data_percentage_slider, task_complexity_dropdown
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# Add a conclusion section at the bottom
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gr.Markdown("""
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f1 = np.nan_to_num(f1) # Replace NaN with 0
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return np.mean(f1) # Return the mean F1-score across all classes
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def plot_confusion_matrix_beamPred(cm, classes, title, save_path, user_mode="dark"):
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# Compute the average F1-score
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avg_f1 = compute_f1_score(cm)
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task_complexity_dropdown = gr.Dropdown(label="Task Complexity (Number of Beams)", choices=[16, 32, 64, 128, 256], value=16)
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with gr.Row():
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raw_img_bp = gr.Image(label="Raw Channels", type="pil", width=300, height=500)
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embeddings_img_bp = gr.Image(label="Embeddings", type="pil", width=300, height=500)
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# Update the confusion matrices whenever sliders change
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data_percentage_slider.change(fn=beam_prediction_task, inputs=[data_percentage_slider, task_complexity_dropdown], outputs=[raw_img_bp, embeddings_img_bp])
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task_complexity_dropdown.change(fn=beam_prediction_task, inputs=[data_percentage_slider, task_complexity_dropdown], outputs=[raw_img_bp, embeddings_img_bp])
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# Add a conclusion section at the bottom
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gr.Markdown("""
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