Update space app

README.md

@@ -1,14 +1,15 @@
----
-title: Tbmeta Space
-emoji: 🌖
-colorFrom: green
-colorTo: red
-sdk: gradio
-sdk_version: 6.8.0
-app_file: app.py
-pinned: false
-license: mit
-short_description: tb transscriptomic met analysis
----
+# Hugging Face Space Setup
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+1. Create a new Space (`Streamlit`) in your Hugging Face account.
+2. Push these files to the Space root:
+- `app.py`
+- `requirements.txt`
+3. Also include this repository content (or mount it as a submodule) so the `tbmeta` CLI and config are available.
+4. Set optional secrets in Space settings:
+- `OPENAI_API_KEY`
+- `OPENCLAW_API_KEY`
+5. Launch the Space and use the "Run Full Pipeline" button.
+
+Note:
+- Free CPU Spaces may be slow for full runs.
+- For heavy runs, execute pipeline in CI/Kaggle and use Space primarily as a dashboard.

app.py

@@ -0,0 +1,46 @@
+from __future__ import annotations
+
+import os
+from pathlib import Path
+import subprocess
+import sys
+
+import streamlit as st
+
+
+ROOT = Path(__file__).resolve().parents[2]
+RESULTS = ROOT / "results" / "tables"
+FIGS = ROOT / "results" / "figures"
+
+
+def _run_pipeline() -> tuple[int, str]:
+    cmd = ["tbmeta", "all", "--config", "configs/config.yaml", "--force"]
+    p = subprocess.run(cmd, cwd=ROOT, capture_output=True, text=True)
+    return p.returncode, (p.stdout + "\n" + p.stderr).strip()
+
+
+st.set_page_config(page_title="TB Meta Dashboard", layout="wide")
+st.title("TB Progression Transcriptome Meta-analysis")
+st.caption("Hugging Face Space wrapper for results exploration and reruns.")
+
+if st.button("Run Full Pipeline"):
+    with st.spinner("Running pipeline..."):
+        code, logs = _run_pipeline()
+    if code == 0:
+        st.success("Pipeline completed.")
+    else:
+        st.error(f"Pipeline failed with code {code}.")
+    st.text_area("Logs", logs, height=320)
+
+st.subheader("Key Tables")
+for name in ["loco_performance.csv", "signature_genes.csv", "pathway_enrichment.csv", "window_sensitivity.csv"]:
+    p = RESULTS / name
+    if p.exists():
+        st.markdown(f"- `{name}`")
+
+st.subheader("Figures")
+if FIGS.exists():
+    for fig in sorted(FIGS.glob("*.png")):
+        st.image(str(fig), caption=fig.name)
+else:
+    st.info("No figure outputs found yet.")

requirements.txt

@@ -0,0 +1,11 @@
+streamlit>=1.40
+pandas>=2.2
+numpy>=1.26
+scikit-learn>=1.5
+matplotlib>=3.9
+scipy>=1.13
+requests>=2.32
+gseapy>=1.1
+GEOparse>=2.0
+pyarrow>=17.0
+tbmeta