Delete src/qa.py

src/qa.py

@@ -1,63 +0,0 @@
-import os
-from sentence_transformers import SentenceTransformer
-from transformers import pipeline
-from vectorstore import search_faiss
-
-print("✅ qa.py loaded from:", __file__)
-
-# Always redirect Hugging Face caches to /tmp
-CACHE_DIR = "/tmp/huggingface"
-os.environ["HF_HOME"] = CACHE_DIR
-os.environ["TRANSFORMERS_CACHE"] = CACHE_DIR
-os.environ["HF_DATASETS_CACHE"] = CACHE_DIR
-
-# ----------------------------
-# Embedding model for queries
-# ----------------------------
-_query_model = SentenceTransformer(
-    "sentence-transformers/all-MiniLM-L6-v2",
-    cache_folder=CACHE_DIR
-)
-
-# ----------------------------
-# LLM for answers
-# ----------------------------
-MODEL_NAME = "google/flan-t5-small"
-MODEL_PATH = os.path.join(CACHE_DIR, MODEL_NAME)
-
-if not os.path.exists(MODEL_PATH):
-    print(f"⬇️ Downloading {MODEL_NAME} to {MODEL_PATH}")
-    _answer_model = pipeline(
-        "text2text-generation",
-        model=MODEL_NAME,
-        cache_dir=CACHE_DIR
-    )
-    # Save pipeline model locally
-    _answer_model.model.save_pretrained(MODEL_PATH)
-    _answer_model.tokenizer.save_pretrained(MODEL_PATH)
-else:
-    print(f"✅ Loading {MODEL_NAME} from {MODEL_PATH}")
-    _answer_model = pipeline(
-        "text2text-generation",
-        model=MODEL_PATH,
-        cache_dir=CACHE_DIR
-    )
-
-# ----------------------------
-# Functions
-# ----------------------------
-def retrieve_chunks(query, index, chunks, top_k=3):
-    q_emb = _query_model.encode([query], convert_to_numpy=True)[0]
-    return search_faiss(q_emb, index, chunks, top_k)
-
-def generate_answer(query, retrieved_chunks):
-    if not retrieved_chunks:
-        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()