1.1

src/api/notes_routes.py

@@ -305,10 +305,10 @@ async def process_video_task(task_id: str, youtube_url: str, language: str, user
             if isinstance(seg, dict) and seg.get("key_insight"):
                 key_points_list.append(seg["key_insight"])
 
-        # Step 2: Generate the categories (keep in memory)
-        from src.summarization.topic_classifier import classify_topics
+        # Step 2: Generate the single primary category (keep in memory)
+        from src.summarization.topic_classifier import get_primary_category
         raw_topics = summary_json.get("topics", [])
-        categories = classify_topics(raw_topics) if raw_topics else ["Education & Science"]
+        category = get_primary_category(raw_topics) if raw_topics else "Education & Science"
 
         # Step 3: Combine everything into ONE single dictionary
         note_data = {
@@ -317,7 +317,7 @@ async def process_video_task(task_id: str, youtube_url: str, language: str, user
             "videoTitle": video_title,
             "notes": final_markdown,
             "thumbnail": f"https://img.youtube.com/vi/{video_id}/mqdefault.jpg" if video_id else "",
-            "category": categories,
+            "category": category,
             "keyPoints": key_points_list,
             "createdAt": datetime.utcnow(),
             "updatedAt": datetime.utcnow(),
@@ -328,8 +328,8 @@ async def process_video_task(task_id: str, youtube_url: str, language: str, user
         # The Flutter app will perform the final save after user edits.
         tasks[task_id]["status"] = "completed"
         tasks[task_id]["notes"] = final_markdown
-        tasks[task_id]["topics"] = categories
-        tasks[task_id]["category"] = categories
+        tasks[task_id]["topics"] = raw_topics
+        tasks[task_id]["category"] = category
         tasks[task_id]["keyPoints"] = key_points_list
         logger.info(f"✅ Task {task_id} completed successfully!")
 

src/summarization/topic_classifier.py

@@ -2,11 +2,11 @@
 Topic Classifier — maps dynamic LLM-extracted topics to predefined UI categories.
 
 Usage:
-    from src.summarization.topic_classifier import classify_topics
+    from src.summarization.topic_classifier import classify_topic, get_primary_category
 
     topics = ["Python", "Machine Learning", "Neural Networks"]
-    result = classify_topics(topics)
-    # => ["Technology & AI"]
+    result = classify_topic(topics)
+    # => "Technology & AI"
 
 Categories:
     Technology & AI | Business & Finance | Education & Science
@@ -199,22 +199,24 @@ _register("Health & Sports", [
 # PUBLIC API
 # ─────────────────────────────────────────────────────────────────────────────
 
-def classify_topics(topics: List[str]) -> List[str]:
+def classify_topic(topics: List[str]) -> str:
     """
-    Map a list of dynamically extracted topics to predefined UI categories.
+    Map a list of dynamically extracted topics to a SINGLE predefined UI category.
+
+    Returns the single best-matching category (the first match in CATEGORIES order).
+    Falls back to "Education & Science" if no match is found.
 
     Args:
         topics: List of topic strings from the LLM (e.g. ["Python", "Deep Learning"]).
 
     Returns:
-        A deduplicated, ordered list of matching category names.
-        Falls back to ["Education & Science"] if no match is found.
+        A single category string.
 
     Example:
-        >>> classify_topics(["Python", "Machine Learning", "Neural Networks"])
-        ["Technology & AI"]
-        >>> classify_topics(["Investing", "AI Stocks"])
-        ["Business & Finance", "Technology & AI"]
+        >>> classify_topic(["Python", "Machine Learning", "Neural Networks"])
+        "Technology & AI"
+        >>> classify_topic(["Investing", "AI Stocks"])
+        "Business & Finance"
     """
     matched: Set[str] = set()
 
@@ -233,17 +235,25 @@ def classify_topics(topics: List[str]) -> List[str]:
                 break
 
     if not matched:
-        matched.add("Education & Science")
+        return "Education & Science"
+
+    # Return the first match in CATEGORIES order for consistency
+    for cat in CATEGORIES:
+        if cat in matched:
+            return cat
 
-    # Return in the same order as CATEGORIES for consistency
-    return [cat for cat in CATEGORIES if cat in matched]
+    return "Education & Science"
+
+
+def classify_topics(topics: List[str]) -> List[str]:
+    """Backward-compatible wrapper — returns a single-element list."""
+    return [classify_topic(topics)]
 
 
 def get_primary_category(topics: List[str]) -> str:
     """
     Return the single best-matching category for the given topics.
 
-    Useful when only one category tag is needed (e.g. a badge in the UI).
+    Alias for classify_topic().
     """
-    categories = classify_topics(topics)
-    return categories[0] if categories else "Education & Science"
+    return classify_topic(topics)