intent_classifier.py

import ollama

# Step 1: Define the refined prompt template
INTENT_PROMPT = """
You are an intent classifier for a YouTube content copilot. 
Return EXACTLY ONE label from this set (no extra words, no punctuation):
get_recommendation | facts_check | comments_summary | summarize_video | rank_ideas | add_to_notion | none

GATING RULE:
- If the message is NOT clearly about a YouTube video, YouTube channel, a YouTube link, or the user’s ideas for their NEXT VIDEO, return: none.

INTENT DEFINITIONS (use the first that matches):
- get_recommendation → User asks for videos/topics/channels to watch or make, or “which should I do next?” based on trends/subscriptions/ideas list.
- facts_check → User asks to verify truth/accuracy/claims of a specific video or idea.
- comments_summary → User wants a summary of comments or “what are people saying?” on a video.
- summarize_video → User wants a summary/tl;dr/recap/outline of a specific video.
- rank_ideas → User wants to prioritize/sort/pick the best from their VIDEO IDEAS list.
- add_to_notion → User wants toUser wants to save/log an idea to Notion.

ANTI-TRIGGERS:
- Greetings, small talk, unrelated questions → none.
- If ambiguous or missing video/YouTube/next-video-idea context → none.

EXAMPLES:
Q: “hi there” → none
Q: “Summarize this https://youtu.be/abc123” → summarize_video
Q: “What are people saying under this video? https://youtube.com/watch?v=xyz” → comments_summary
Q: “Can you fact check this video’s claims?” → facts_check
Q: “I have three ideas for my next YouTube video, which should I do first?” → rank_ideas
Q: “Recommend trending topics from my subscriptions” → get_recommendation
Q: “Add this idea to Notion: ‘How to edit faster in CapCut’” → add_to_notion
Q: “What’s your name?” → none

User message: "{MESSAGE}"

Return ONLY one of:
get_recommendation | facts_check | comments_summary | summarize_video | rank_ideas | add_to_notion | none
"""

# Step 2: Intent classifier function
def classify_intent(message: str) -> str:
    prompt = INTENT_PROMPT.replace("{MESSAGE}", message)
    resp = ollama.chat(
        model="qwen2.5:3b",
        messages=[{"role": "user", "content": prompt}],
        options={"temperature": 0.1, "top_p": 0.9}
    )
    label = resp["message"]["content"].strip()

    valid = {
        "get_recommendation",
        "facts_check",
        "comments_summary",
        "summarize_video",
        "rank_ideas",
        "add_to_notion",
        "none",
    }
    return label if label in valid else "none"