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ca86eea | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 | import gradio as gr
from predictive_ml_workbench.service import PredictiveMLWorkbenchService
service = PredictiveMLWorkbenchService()
def run_workbench(file_obj, workflow, target_column, test_size, cv_folds, max_clusters):
file_path = getattr(file_obj, "name", file_obj)
return service.run(
csv_path=file_path,
workflow=workflow,
target_column=target_column,
test_size=float(test_size),
cv_folds=int(cv_folds),
max_clusters=int(max_clusters),
)
with gr.Blocks(
title="Predictive ML Workbench",
theme=gr.themes.Soft(primary_hue="green", secondary_hue="blue"),
) as demo:
gr.Markdown(
"""
# Predictive ML Workbench
Upload a CSV dataset and run end-to-end machine learning workflows for regression,
classification, clustering, dimensionality reduction, preprocessing, model selection,
and evaluation.
"""
)
with gr.Accordion("What this project covers", open=False):
gr.Markdown(
"""
- Regression
- Classification
- Clustering
- Dimensionality reduction
- Preprocessing with numeric and categorical pipelines
- Model selection with cross-validation
- Evaluation with workflow-specific metrics and plots
"""
)
dataset_input = gr.File(label="CSV Dataset", file_types=[".csv"])
with gr.Row():
workflow_input = gr.Dropdown(
choices=["Classification", "Regression", "Clustering", "Dimensionality Reduction"],
value="Classification",
label="Workflow",
)
target_input = gr.Textbox(
label="Target Column",
placeholder="Required for regression/classification; optional otherwise",
)
with gr.Row():
test_size_input = gr.Slider(0.1, 0.4, value=0.2, step=0.05, label="Test Split")
cv_input = gr.Slider(2, 5, value=3, step=1, label="CV Folds")
cluster_input = gr.Slider(3, 8, value=6, step=1, label="Max Clusters")
run_button = gr.Button("Run Workflow", variant="primary")
model_output = gr.Textbox(label="Selected Model / Method", lines=2)
metrics_output = gr.Textbox(label="Metrics", lines=10)
preview_output = gr.Textbox(label="Data Preview", lines=10)
plot_output = gr.Plot(label="Visualization")
status_output = gr.Textbox(label="Status", lines=3)
run_button.click(
fn=run_workbench,
inputs=[
dataset_input,
workflow_input,
target_input,
test_size_input,
cv_input,
cluster_input,
],
outputs=[
model_output,
metrics_output,
preview_output,
plot_output,
status_output,
],
)
if __name__ == "__main__":
demo.launch()
|