Delete utils/faq.py

utils/faq.py

@@ -1,112 +0,0 @@
-import json
-import os
-from sentence_transformers import SentenceTransformer, util
-import torch
-
-FAQ_PATHS = ["data/faq_data.json", "data/clinical_faq.json"]
-_FAQ_CACHE = None
-_FAQ_EMBEDDINGS = None
-_MODEL = None
-
-
-def _get_model():
-    """Load and cache the embedding model (shared with main app if possible)."""
-    global _MODEL
-    if _MODEL is None:
-        print("📦 [faq] Loading embedding model: all-MiniLM-L6-v2 ...")
-        _MODEL = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
-    return _MODEL
-
-
-def load_faqs():
-    """Load FAQ data from JSON files and cache them."""
-    global _FAQ_CACHE
-    if _FAQ_CACHE is not None:
-        return _FAQ_CACHE
-
-    all_faqs = []
-    for path in FAQ_PATHS:
-        if os.path.exists(path):
-            try:
-                with open(path, "r", encoding="utf-8") as f:
-                    data = json.load(f)
-                if isinstance(data, list):
-                    all_faqs.extend(data)
-                elif isinstance(data, dict):
-                    for k, v in data.items():
-                        all_faqs.append({"question": k, "answer": v})
-            except Exception as e:
-                print(f"⚠️ Failed to load FAQ file {path}: {e}")
-
-    _FAQ_CACHE = all_faqs
-    print(f"✅ [faq] Loaded {len(_FAQ_CACHE)} FAQ entries.")
-    return _FAQ_CACHE
-
-
-def _build_embeddings():
-    """Precompute embeddings for all FAQ questions."""
-    global _FAQ_EMBEDDINGS
-    faqs = load_faqs()
-    if not faqs:
-        _FAQ_EMBEDDINGS = torch.empty(0)
-        return _FAQ_EMBEDDINGS
-
-    model = _get_model()
-    questions = [f["question"] for f in faqs if f.get("question")]
-    _FAQ_EMBEDDINGS = model.encode(questions, convert_to_tensor=True, show_progress_bar=False)
-    print(f"✅ [faq] Encoded {len(_FAQ_EMBEDDINGS)} FAQ embeddings.")
-    return _FAQ_EMBEDDINGS
-
-
-def get_faq_answer(query: str, top_k: int = 1) -> str:
-    """
-    Return the most semantically similar FAQ answer to the query.
-    Uses MiniLM embeddings and cosine similarity.
-    """
-    faqs = load_faqs()
-    if not faqs:
-        return ""
-
-    if _FAQ_EMBEDDINGS is None:
-        _build_embeddings()
-
-    model = _get_model()
-    query_emb = model.encode(query, convert_to_tensor=True)
-    sims = util.cos_sim(query_emb, _FAQ_EMBEDDINGS)[0]
-    top_idx = int(torch.argmax(sims))
-
-    best_score = float(sims[top_idx])
-    best_item = faqs[top_idx]
-
-    if best_score < 0.45:  # threshold to avoid weak matches
-        return ""
-
-    answer = best_item.get("answer", "")
-    print(f"💡 [faq] Best match: \"{best_item.get('question')}\" (score={best_score:.2f})")
-    return answer
-
-
-def lookup_faq(query: str, top_k: int = 3) -> str:
-    """
-    Return HTML-formatted list of top-k semantically similar FAQ matches.
-    Useful for admin or verbose display.
-    """
-    faqs = load_faqs()
-    if not faqs:
-        return "<i>No FAQ data loaded.</i>"
-
-    if _FAQ_EMBEDDINGS is None:
-        _build_embeddings()
-
-    model = _get_model()
-    query_emb = model.encode(query, convert_to_tensor=True)
-    sims = util.cos_sim(query_emb, _FAQ_EMBEDDINGS)[0]
-    top_indices = torch.topk(sims, k=min(top_k, len(faqs))).indices.tolist()
-
-    html = []
-    for idx in top_indices:
-        score = float(sims[idx])
-        item = faqs[idx]
-        html.append(f"<b>{item['question']}</b><br>{item['answer']}<br><i>(score={score:.2f})</i>")
-
-    return "<br><br>".join(html)