integrating headlines to wa

nuse_modules/classifier.py

@@ -20,7 +20,8 @@ QUESTION_TYPES = {
     "science_health": 10,
     "fact_check": 11,
     "compare_entities": 12,
-    "small_talk": 13
+    "asking_for_headlines": 13,
+    "small_talk": 14
 }
 
 REVERSE_MAP = {v: k for k, v in QUESTION_TYPES.items()}

routes/question.py

@@ -1,5 +1,6 @@
 import os
 import requests
+import datetime
 import json
 from fastapi import APIRouter
 from pydantic import BaseModel
@@ -20,7 +21,7 @@ class QuestionInput(BaseModel):
 
 
 def should_extract_keywords(type_id: int) -> bool:
-    return type_id in {1, 2, 3, 4, 5, 6, 7, 10}
+    return type_id in {1, 2, 3, 4, 5, 6, 7, 10, 11, 12}
 
 
 def extract_answer_after_label(text: str) -> str:
@@ -45,6 +46,32 @@ async def ask_question(input: QuestionInput):
     context = ""
     sources = []
 
+    if qid == 13:
+        date_str = datetime.datetime.utcnow().strftime("%Y-%m-%d")
+        categories = ["world", "india", "finance", "sports", "entertainment"]
+        all_headlines = []
+
+        for cat in categories:
+            key = f"headlines:{date_str}:{cat}"
+            cached = _r.get(key)
+            if cached:
+                articles = json.loads(cached)
+                for art in articles:
+                    all_headlines.append({
+                        "title": art["title"],
+                        "summary": art["summary"],
+                        "url": art["url"],
+                        "image": art.get("image"),
+                        "category": cat,
+                    })
+
+        return {
+            "question": question,
+            "answer": "Here are today’s top headlines:",
+            "headlines": all_headlines  # include this for frontend/bot formatting
+        }
+        
+
     # Step 2: Keyword extraction and news search (if needed)
     if should_extract_keywords(qid):
         keywords = keywords_extractor(question)