Commit
Β·
669cedb
1
Parent(s):
6c5daeb
Update app.py
Browse files
app.py
CHANGED
@@ -47,15 +47,12 @@ def visualize_input_data():
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plt.axis("square")
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plt.legend(handles=handles, labels=["outliers", "inliers"], title="true class")
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plt.title("Gaussian inliers with \nuniformly distributed outliers")
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# plt.show()
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# plt.clear()
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return fig
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title = " An example using IsolationForest for anomaly detection."
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description1 = "The isolation forest is an Ensemble of Isolation trees and it isolates the datapoints using recursive random partitioning."
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description2 = "In case of outliers the number of splits required is greater than those required for inliers."
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@@ -64,22 +61,26 @@ description3 = "We will use the toy dataset as given in the scikit-learn page fo
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with gr.Blocks(title=title) as demo:
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gr.Markdown(f"# {title}")
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gr.Markdown(
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gr.Markdown(" **https://scikit-learn.org/stable/auto_examples/ensemble/plot_isolation_forest.html#sphx-glr-auto-examples-ensemble-plot-isolation-forest-py**")
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loaded_model = load_hf_model_hub()
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with gr.Tab("Visualize Input dataset"):
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btn = gr.Button(value="Visualize input dataset")
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btn.click(visualize_input_data, outputs= gr.Plot(label='Visualizing input dataset') )
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with gr.Tab("Plot Decision Boundary"):
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image_decision = gr.Image('./downloaded-model/decision_boundary.png')
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with gr.Tab("Plot Path"):
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image_path = gr.Image('./downloaded-model/plot_path.png')
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plt.axis("square")
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plt.legend(handles=handles, labels=["outliers", "inliers"], title="true class")
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plt.title("Gaussian inliers with \nuniformly distributed outliers")
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return fig
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title = " An example using IsolationForest for anomaly detection."
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description1 = "The isolation forest is an Ensemble of Isolation trees and it isolates the datapoints using recursive random partitioning."
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description2 = "In case of outliers the number of splits required is greater than those required for inliers."
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with gr.Blocks(title=title) as demo:
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gr.Markdown(f"# {title}")
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gr.Markdown(
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"""
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The isolation forest is an Ensemble of Isolation trees and it isolates the data points using recursive random partitioning.
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In case of outliers the number of splits required is greater than those required for inliers.
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We will use the toy dataset for our educational demo as given in the scikit-learn page for Isolation Forest.
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""")
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gr.Markdown(" **https://scikit-learn.org/stable/auto_examples/ensemble/plot_isolation_forest.html#sphx-glr-auto-examples-ensemble-plot-isolation-forest-py**")
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loaded_model = load_hf_model_hub()
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with gr.Tab("# Visualize Input dataset"):
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btn = gr.Button(value="Visualize input dataset")
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btn.click(visualize_input_data, outputs= gr.Plot(label='Visualizing input dataset') )
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with gr.Tab("# Plot Decision Boundary"):
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image_decision = gr.Image('./downloaded-model/decision_boundary.png')
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with gr.Tab("# Plot Path"):
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image_path = gr.Image('./downloaded-model/plot_path.png')
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