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import gradio as gr |
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import numpy as np |
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import pandas as pd |
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import matplotlib.pyplot as plt |
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import joblib |
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from preprocessing.numeric_selector import NumericSelector |
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from cluster_ops.clustering import explore_clusters, final_clustering |
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with gr.Blocks(title="PERCEUL: Perception-Based Worker Profiler") as app: |
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gr.Markdown("# 🧠 PERCEUL: Profiler of Perception and Cognitive Ergonomics in the Workplace") |
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file_input = gr.File(label="Upload CSV") |
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with gr.Tab("Cluster Exploration"): |
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btn = gr.Button("Explore Clusters") |
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plot_output = gr.Plot() |
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cluster_summary_output = gr.JSON(label="Cluster Sizes") |
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outlier_output = gr.Markdown("### Exploration Report") |
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btn.click( |
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fn=explore_clusters, |
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inputs=[file_input], |
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outputs=[plot_output, cluster_summary_output, outlier_output] |
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) |
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with gr.Tab("Final Clustering"): |
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top_features = gr.Number( |
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value=5, |
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label="Number of Features to Display", |
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minimum=3, |
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maximum=10, |
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step=1, |
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precision=0 |
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) |
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run_btn = gr.Button("Run Final Clustering") |
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best_k_out = gr.Number(label="Number of Clusters `K` (Read-Only)", interactive=False, precision=0) |
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gr.Markdown("### Cluster Characteristics") |
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deviations_out = gr.Markdown() |
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run_btn.click( |
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final_clustering, |
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inputs=[file_input, top_features], |
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outputs=[best_k_out, deviations_out] |
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) |
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app.launch() |
