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app.py

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+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()