Create check_keys.py

check_keys.py

@@ -0,0 +1,72 @@
+"""
+check_keys.py — paste into a HF Space file and read the startup logs,
+OR add as a temporary check tab in app.py.
+Run: python check_keys.py
+"""
+import os
+
+print("\n" + "="*50)
+print("KEY & MODEL HEALTH CHECK")
+print("="*50)
+
+# ── 1. Check raw env vars ─────────────────────────────────────────────────────
+keys = {
+    "MISTRAL_API_KEY": os.getenv("MISTRAL_API_KEY"),
+    "GROQ_API_KEY":    os.getenv("GROQ_API_KEY"),
+    "GEMINI_API_KEY":  os.getenv("GEMINI_API_KEY"),
+    "GOOGLE_API_KEY":  os.getenv("GOOGLE_API_KEY"),
+    "OPENAI_API_KEY":  os.getenv("OPENAI_API_KEY"),   # should be absent now
+}
+print("\n── Secrets present ──")
+for k, v in keys.items():
+    status = "✅ set" if v else "❌ missing"
+    masked = (v[:6] + "..." + v[-3:]) if v and len(v) > 9 else "(empty)"
+    print(f"  {k:25s} {status}   {masked if v else ''}")
+
+# remap for google
+if not os.getenv("GOOGLE_API_KEY") and os.getenv("GEMINI_API_KEY"):
+    os.environ["GOOGLE_API_KEY"] = os.environ["GEMINI_API_KEY"]
+    print("\n  ↳ Remapped GEMINI_API_KEY → GOOGLE_API_KEY")
+
+# ── 2. Test Mistral ───────────────────────────────────────────────────────────
+print("\n── Mistral (mistral-small-latest) ──")
+try:
+    from langchain_mistralai import ChatMistralAI
+    from langchain_core.messages import HumanMessage
+    llm = ChatMistralAI(model="mistral-small-latest", temperature=0)
+    r = llm.invoke([HumanMessage(content="Reply with just: OK")])
+    print(f"  ✅ Response: {r.content.strip()[:60]}")
+except Exception as e:
+    print(f"  ❌ FAILED: {e}")
+
+# ── 3. Test Gemini ────────────────────────────────────────────────────────────
+print("\n── Gemini (gemini-1.5-flash-latest) ──")
+try:
+    from langchain_google_genai import ChatGoogleGenerativeAI
+    llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash-latest", temperature=0)
+    r = llm.invoke([HumanMessage(content="Reply with just: OK")])
+    print(f"  ✅ Response: {r.content.strip()[:60]}")
+except Exception as e:
+    print(f"  ❌ FAILED: {e}")
+
+# ── 4. Test Groq ──────────────────────────────────────────────────────────────
+print("\n── Groq (llama-3.3-70b-versatile) ──")
+try:
+    from langchain_groq import ChatGroq
+    llm = ChatGroq(model="llama-3.3-70b-versatile", temperature=0)
+    r = llm.invoke([HumanMessage(content="Reply with just: OK")])
+    print(f"  ✅ Response: {r.content.strip()[:60]}")
+except Exception as e:
+    print(f"  ❌ FAILED: {e}")
+
+# ── 5. Test MiniLM ───────────────────────────────────────────────────────────
+print("\n── MiniLM (all-MiniLM-L6-v2) ──")
+try:
+    from sentence_transformers import SentenceTransformer
+    m = SentenceTransformer("all-MiniLM-L6-v2")
+    e = m.encode(["test sentence"], normalize_embeddings=True)
+    print(f"  ✅ Embedding shape: {e.shape}  (expected: (1, 384))")
+except Exception as e:
+    print(f"  ❌ FAILED: {e}")
+
+print("\n" + "="*50 + "\n")