Upload pipeline/scam_filter.py with huggingface_hub

pipeline/scam_filter.py

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+"""
+Lightweight scam detection for Lumi.
+
+Two-stage approach:
+  1. Keyword/pattern matching — fast, catches the obvious cases
+  2. Embedding similarity — catches paraphrased variants
+
+If scam_probability >= THRESHOLD, returns a gentle deflection response instead
+of forwarding to the LLM. The deflection never alarms the patient.
+"""
+
+from __future__ import annotations
+
+import re
+
+THRESHOLD = 0.7   # probability above which we deflect
+
+# ---------------------------------------------------------------------------
+# Stage 1 — keyword patterns
+# ---------------------------------------------------------------------------
+
+SCAM_PATTERNS = [
+    # urgent money requests
+    r"\b(send|transfer|wire|give|need)\b.{0,30}(money|cash|\$|pound|dollar)",
+    r"\burgent\b.{0,40}\b(payment|transfer|money|funds)",
+    # prize / lottery
+    r"\b(won|win|winner|lottery|prize|jackpot)\b",
+    r"\bcongratulations\b.{0,60}\b(prize|award|money|gift)",
+    # fake family emergency
+    r"\b(grandson|granddaughter|son|daughter|nephew|niece)\b.{0,40}\b(arrested|accident|hospital|trouble|jail|hurt)",
+    # bank / card details
+    r"\b(bank\s+account|account\s+number|sort\s+code|credit\s+card|debit\s+card|pin\b|password|social\s+security)",
+    # impersonation
+    r"\bthis\s+is\s+(your\s+bank|the\s+(police|irs|hmrc|government|medicare|social\s+security))",
+    # gift card scams
+    r"\b(gift\s+card|itunes|amazon\s+card|google\s+play)\b.{0,30}\b(buy|purchase|send|code)",
+]
+
+_compiled = [re.compile(p, re.IGNORECASE) for p in SCAM_PATTERNS]
+
+
+def _keyword_score(text: str) -> float:
+    """Returns a score 0-1 based on how many patterns match."""
+    hits = sum(1 for p in _compiled if p.search(text))
+    if hits >= 1:
+        return 1.0
+    return 0.0
+
+
+# ---------------------------------------------------------------------------
+# Stage 2 — embedding similarity (optional, loads lazily)
+# ---------------------------------------------------------------------------
+
+_embed_model = None
+_scam_embeddings = None
+
+SCAM_SEED_PHRASES = [
+    "Send me $500 immediately, I'm in trouble",
+    "You have won a lottery prize, claim now",
+    "Give me your bank account number",
+    "This is the IRS, you owe back taxes",
+    "Buy iTunes gift cards and send the codes",
+    "I'm your grandson and I had an accident",
+    "Your credit card has been compromised, verify details",
+]
+
+
+def _load_embeddings():
+    global _embed_model, _scam_embeddings
+    if _embed_model is None:
+        try:
+            from sentence_transformers import SentenceTransformer
+            import numpy as np
+            _embed_model = SentenceTransformer("all-MiniLM-L6-v2")
+            _scam_embeddings = _embed_model.encode(SCAM_SEED_PHRASES, normalize_embeddings=True)
+        except ImportError:
+            _embed_model = False  # flag: not available
+
+
+def _embedding_score(text: str) -> float:
+    _load_embeddings()
+    if not _embed_model:
+        return 0.0
+    import numpy as np
+    emb = _embed_model.encode([text], normalize_embeddings=True)
+    similarities = (_scam_embeddings @ emb.T).flatten()
+    return float(similarities.max())
+
+
+# ---------------------------------------------------------------------------
+# Public API
+# ---------------------------------------------------------------------------
+
+DEFLECTION_RESPONSE = (
+    "That sounds like something we should check with your family first. "
+    "Let me make a note for them. "
+    "You don't need to do anything right now — you're completely safe."
+)
+
+
+def scam_probability(text: str) -> float:
+    kw = _keyword_score(text)
+    em = _embedding_score(text)
+    return max(kw, em * 0.8)   # keyword takes precedence; embedding adds coverage
+
+
+def check_and_deflect(user_text: str) -> tuple[bool, str]:
+    """
+    Returns (is_scam, response).
+    If is_scam is True, response is the safe deflection message.
+    If is_scam is False, response is empty — proceed normally to the LLM.
+    """
+    prob = scam_probability(user_text)
+    if prob >= THRESHOLD:
+        return True, DEFLECTION_RESPONSE
+    return False, ""