added a classifier v1

nuse_modules/classifier.py

@@ -0,0 +1,97 @@
+import os
+import re
+import requests
+from dotenv import load_dotenv
+
+load_dotenv()
+
+HF_TOKEN = os.getenv("HF_TOKEN")
+HF_ZERO_SHOT_MODEL = "facebook/bart-large-mnli"
+
+# Map readable categories to numeric IDs
+QUESTION_TYPES = {
+    "recent_update": 1,
+    "explainer": 2,
+    "timeline": 3,
+    "person_in_news": 4,
+    "policy_or_law": 5,
+    "election_poll": 6,
+    "business_market": 7,
+    "sports_event": 8,
+    "pop_culture": 9,
+    "science_health": 10,
+    "fact_check": 11,
+    "compare_entities": 12
+}
+
+REVERSE_MAP = {v: k for k, v in QUESTION_TYPES.items()}
+
+
+# ---------- Step 1: Fast Rule-Based Classification ----------
+def rule_based_classify(prompt: str) -> int:
+    p = prompt.lower()
+
+    if any(x in p for x in ["latest", "recent", "update", "breaking", "happened today"]):
+        return QUESTION_TYPES["recent_update"]
+    if any(x in p for x in ["explain", "why", "what is", "background", "summary"]):
+        return QUESTION_TYPES["explainer"]
+    if "timeline" in p or re.search(r"how .* changed", p):
+        return QUESTION_TYPES["timeline"]
+    if re.search(r"why .* in the news", p) or "trending" in p:
+        return QUESTION_TYPES["person_in_news"]
+    if any(x in p for x in ["bill", "policy", "law", "executive order", "passed", "signed"]):
+        return QUESTION_TYPES["policy_or_law"]
+    if any(x in p for x in ["election", "poll", "vote", "candidate", "ballot"]):
+        return QUESTION_TYPES["election_poll"]
+    if any(x in p for x in ["stock", "inflation", "economy", "market", "job report"]):
+        return QUESTION_TYPES["business_market"]
+    if any(x in p for x in ["score", "match", "tournament", "game", "league"]):
+        return QUESTION_TYPES["sports_event"]
+    if any(x in p for x in ["celebrity", "actor", "album", "movie", "music", "show"]):
+        return QUESTION_TYPES["pop_culture"]
+    if any(x in p for x in ["health", "covid", "science", "study", "research", "doctor"]):
+        return QUESTION_TYPES["science_health"]
+    if any(x in p for x in ["true", "false", "hoax", "real", "claim", "fact check"]):
+        return QUESTION_TYPES["fact_check"]
+    if any(x in p for x in ["compare", "vs", "difference between"]):
+        return QUESTION_TYPES["compare_entities"]
+
+    return -1
+
+
+# ---------- Step 2: HF Zero-Shot Fallback ----------
+def zero_shot_classify(prompt: str) -> int:
+    candidate_labels = list(QUESTION_TYPES.keys())
+    payload = {
+        "inputs": prompt,
+        "parameters": {
+            "candidate_labels": candidate_labels
+        }
+    }
+
+    headers = {
+        "Authorization": f"Bearer {HF_TOKEN}",
+        "Content-Type": "application/json"
+    }
+
+    url = f"https://api-inference.huggingface.co/models/{HF_ZERO_SHOT_MODEL}"
+
+    try:
+        res = requests.post(url, headers=headers, json=payload, timeout=20)
+        res.raise_for_status()
+        data = res.json()
+        if isinstance(data, dict) and "labels" in data:
+            top_label = data["labels"][0]
+            return QUESTION_TYPES.get(top_label, -1)
+    except Exception as e:
+        print("[HF Classifier Error]", str(e))
+    
+    return -1
+
+
+# ---------- Public Hybrid Classifier ----------
+def classify_question(prompt: str) -> int:
+    rule_result = rule_based_classify(prompt)
+    if rule_result != -1:
+        return rule_result
+    return zero_shot_classify(prompt)

question.py

@@ -8,6 +8,7 @@ from redis_client import redis_client as r
 from dotenv import load_dotenv
 from urllib.parse import quote
 import json
+from nuse_modules.classifier import classify_question, REVERSE_MAP
 
 load_dotenv()
 
@@ -92,6 +93,10 @@ def fetch_gnews_articles(query: str) -> List[dict]:
 async def ask_question(input: QuestionInput):
     question = input.question
 
+    qid = classify_question(question)
+    print("Intent ID:", qid)
+    print("Category:", REVERSE_MAP.get(qid, "unknown"))
+
     # Step 1: Ask Mistral to extract keywords
     keyword_prompt = (
         f"Extract the 3–6 most important keywords from the following question. "
@@ -114,8 +119,6 @@ async def ask_question(input: QuestionInput):
         query_or = " OR ".join(f'"{kw}"' for kw in keywords)
         articles = fetch_gnews_articles(query_or)
 
-    print("Fetched articles:", articles)
-
     relevant_articles = [a for a in articles if is_relevant(a, keywords)]
 
     context = "\n\n".join([
@@ -144,8 +147,6 @@ async def ask_question(input: QuestionInput):
 
     final_answer = extract_answer_after_label(answer)
 
-    print("Mistral Answer:", answer)
-
     return {
         "question": question,
         "answer": final_answer.strip(),