File size: 3,543 Bytes
9d4a039 | 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 | import gradio as gr
from bioscore import reproducibility, data_quality, model_readiness
import tempfile
import os
def check_reproducibility(file):
if file is None:
return "Please upload a .ipynb or .py file"
result = reproducibility(file.name)
level_emoji = {"full": "✅", "partial": "⚠️", "minimal": "❌"}
emoji = level_emoji.get(result["level"], "")
output = f"{emoji} Level: **{result['level']}** (score: {result['score']})\n\n"
if result["issues"]:
output += "**Issues found:**\n"
for issue in result["issues"]:
output += f"- {issue}\n"
else:
output += "No issues found — fully reproducible!"
return output
def check_data_quality(file, domain):
if file is None:
return "Please upload a .csv file"
result = data_quality(file.name, domain=domain)
output = f"**Overall quality:** {result['overall']} / 1.0\n\n"
output += f"- Completeness: {result['completeness']}\n"
output += f"- Consistency: {result['consistency']}\n"
if result["overall"] >= 0.8:
output += "\n✅ Quality is good — safe to proceed with training"
elif result["overall"] >= 0.5:
output += "\n⚠️ Quality is moderate — review before training"
else:
output += "\n❌ Quality is low — fix data issues before training"
return output
def check_model_readiness(file):
if file is None:
return "Please upload a .pkl model file"
result = model_readiness(file.name)
status = "✅ Ready for production" if result["ready"] else "❌ Not ready for production"
output = f"{status} (score: {result['score']})\n\n"
if result["gaps"]:
output += "**Gaps to fix:**\n"
for gap in result["gaps"]:
output += f"- {gap}\n"
else:
output += "No gaps — model passes all readiness checks!"
return output
with gr.Blocks(title="bioscore — Biomedical Scoring Toolkit", theme=gr.themes.Base()) as demo:
gr.Markdown("# 🧬 bioscore\nBiomedical scoring toolkit — reproducibility, data quality, model readiness")
gr.Markdown("[pip install bioscore](https://pypi.org/project/bioscore/) · [Source](https://github.com/K-RnD-Lab/SPHERE-III-TECHNOLOGY)")
with gr.Tab("Reproducibility"):
gr.Markdown("Upload a Jupyter notebook (.ipynb) or Python script (.py) to check reproducibility")
rep_file = gr.File(label="Upload notebook/script", file_types=[".ipynb", ".py"])
rep_btn = gr.Button("Check Reproducibility", variant="primary")
rep_out = gr.Markdown()
rep_btn.click(check_reproducibility, inputs=[rep_file], outputs=[rep_out])
with gr.Tab("Data Quality"):
gr.Markdown("Upload a CSV dataset to assess quality")
dq_file = gr.File(label="Upload CSV", file_types=[".csv"])
dq_domain = gr.Dropdown(["general", "oncology", "agriculture"], value="general", label="Domain")
dq_btn = gr.Button("Check Data Quality", variant="primary")
dq_out = gr.Markdown()
dq_btn.click(check_data_quality, inputs=[dq_file, dq_domain], outputs=[dq_out])
with gr.Tab("Model Readiness"):
gr.Markdown("Upload a pickled ML model (.pkl) to check production readiness")
mr_file = gr.File(label="Upload model (.pkl)", file_types=[".pkl"])
mr_btn = gr.Button("Check Model Readiness", variant="primary")
mr_out = gr.Markdown()
mr_btn.click(check_model_readiness, inputs=[mr_file], outputs=[mr_out])
if __name__ == "__main__":
demo.launch()
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