Create app.py

app.py

@@ -0,0 +1,133 @@
+import os
+import platform
+from pathlib import Path
+import time
+import traceback
+
+import streamlit as st
+import pandas as pd
+
+from src.paths import base_dir, guidelines_dir, faiss_index_dir, exports_dir
+
+st.set_page_config(page_title="AI-Native E-Consult Prototype (V1)", page_icon="🩺", layout="wide")
+st.title("AI‑Native E‑Consult Prototype (V1)")
+st.caption("Step 0 — Environment Setup & Health Check")
+
+# ---------- Helper ----------
+def _try_import(modname: str):
+    try:
+        m = __import__(modname)
+        ver = getattr(m, "__version__", "n/a")
+        return True, ver, None
+    except Exception as e:
+        return False, None, str(e)
+
+def _hf_whoami():
+    try:
+        from huggingface_hub import whoami
+        token = os.getenv("HF_TOKEN") or os.getenv("HUGGINGFACEHUB_API_TOKEN")
+        if not token:
+            return False, None, "No HF token found. Add HF_TOKEN in Space Settings → Variables."
+        me = whoami(token=token)
+        return True, me, None
+    except Exception as e:
+        return False, None, str(e)
+
+# ---------- Persistent dirs ----------
+bdir = base_dir()
+gdir = guidelines_dir()
+idir = faiss_index_dir()
+xdir = exports_dir()
+
+with st.expander("📁 Storage locations (persistent)"):
+    st.write({
+        "base_dir": str(bdir),
+        "guidelines_dir": str(gdir),
+        "faiss_index_dir": str(idir),
+        "exports_dir": str(xdir),
+    })
+    st.caption("These live on the Space's persistent volume so your RAG index survives restarts.")
+
+# ---------- Diagnostics ----------
+colA, colB = st.columns(2)
+
+with colA:
+    st.subheader("System")
+    st.write({
+        "python": platform.python_version(),
+        "platform": platform.platform(),
+        "cwd": str(Path.cwd()),
+        "time": time.strftime("%Y-%m-%d %H:%M:%S"),
+    })
+    ok_torch, torch_ver, torch_err = _try_import("torch")
+    if ok_torch:
+        import torch
+        cuda = torch.cuda.is_available()
+        device = torch.cuda.get_device_name(0) if cuda else "CPU"
+        st.success(f"torch {torch_ver} — CUDA: {'✅' if cuda else '❌'} — device: {device}")
+    else:
+        st.error(f"torch import failed: {torch_err}")
+
+with colB:
+    st.subheader("Core libraries")
+    rows = []
+    for name in ["transformers", "accelerate", "bitsandbytes", "faiss", "pypdf", "pandas", "numpy", "huggingface_hub", "sentence_transformers"]:
+        ok, ver, err = _try_import(name)
+        rows.append({"library": name, "status": "ok" if ok else "error", "version_or_error": ver if ok else err})
+    st.dataframe(pd.DataFrame(rows), hide_index=True, use_container_width=True)
+
+st.divider()
+st.subheader("Hugging Face auth check (for later model pulls)")
+if st.button("Check HF token"):
+    ok, me, err = _hf_whoami()
+    if ok:
+        who = me.get("name") or me.get("email") or me.get("username", "unknown")
+        st.success(f"HF token valid ✅ — signed in as: {who}")
+    else:
+        st.warning(f"HF token not verified: {err}")
+
+st.subheader("Quick functionality tests")
+if st.button("Run health checks"):
+    results = []
+
+    # 1) Write to persistent storage
+    try:
+        testfile = bdir / "healthcheck.txt"
+        testfile.write_text("ok\n")
+        results.append(("write_persistent", True, f"wrote {testfile}"))
+    except Exception as e:
+        results.append(("write_persistent", False, str(e)))
+
+    # 2) FAISS in-memory index sanity test
+    try:
+        import numpy as np, faiss
+        xb = np.random.random((50, 8)).astype("float32")
+        idx = faiss.IndexFlatL2(8)
+        idx.add(xb)
+        D, I = idx.search(xb[:1], 5)
+        results.append(("faiss_search", True, f"top5 ids: {I[0].tolist()}"))
+    except Exception as e:
+        results.append(("faiss_search", False, str(e)))
+
+    # 3) bitsandbytes soft check (import + CUDA capability if torch has it)
+    try:
+        import bitsandbytes as bnb  # noqa
+        cuda_msg = ""
+        try:
+            import torch
+            if torch.cuda.is_available():
+                # light test: allocate a tiny 4-bit linear layer if available
+                from bitsandbytes.nn import Linear4bit
+                _ = Linear4bit(8, 8, bias=False)
+                cuda_msg = "CUDA-backed 4-bit layer constructed."
+        except Exception:
+            pass
+        results.append(("bitsandbytes", True, f"import ok. {cuda_msg}"))
+    except Exception as e:
+        results.append(("bitsandbytes", False, str(e)))
+
+    st.success("Health checks complete.")
+    st.dataframe(pd.DataFrame([{"check": k, "ok": ok, "detail": d} for (k, ok, d) in results]),
+                 hide_index=True, use_container_width=True)
+
+st.info("If the checks are green, the Space is ready for Step 1 (RAG Corpus Prep).")