Update src/qa.py

src/qa.py

@@ -3,7 +3,8 @@
 # ----------------------------
 import os
 
-CACHE_DIR = "/home/user/huggingface"
+uid = os.getuid()  # current user id (0, 1000, etc.)
+CACHE_DIR = f"/home/user-{uid}/huggingface"
 os.makedirs(CACHE_DIR, exist_ok=True)
 
 os.environ["HF_HOME"] = CACHE_DIR
@@ -11,6 +12,8 @@ os.environ["TRANSFORMERS_CACHE"] = CACHE_DIR
 os.environ["HF_DATASETS_CACHE"] = CACHE_DIR
 os.environ["HF_MODULES_CACHE"] = CACHE_DIR
 
+print(f"✅ qa.py using Hugging Face cache at {CACHE_DIR}")
+
 # ----------------------------
 # Imports AFTER cache bootstrap
 # ----------------------------
@@ -18,8 +21,6 @@ from sentence_transformers import SentenceTransformer
 from transformers import pipeline
 from vectorstore import search_faiss
 
-print("✅ qa.py loaded from:", __file__)
-
 # ----------------------------
 # Query embedding model
 # ----------------------------
@@ -48,4 +49,12 @@ def retrieve_chunks(query, index, chunks, top_k=3):
 
 def generate_answer(query, retrieved_chunks):
     if not retrieved_chunks:
-        return "Sorry,
+        return "Sorry, I could not find relevant information."
+
+    context = " ".join(retrieved_chunks)
+    prompt = (
+        "You are an assistant. Use the context to answer the question clearly.\n"
+        f"Context:\n{context}\n\nQuestion:\n{query}\n\nAnswer:"
+    )
+    result = _answer_model(prompt, max_length=300, do_sample=False)
+    return result[0]["generated_text"].strip()