"""Extract topic labels for chunks using a local LLM."""
import requests
import os
from .config import CONFIG

# Use a fast model — chosen at config time
TOPIC_MODEL = os.getenv("KB_TOPIC_MODEL", "qwen2.5-coder:7b")


def extract_topic(chunk_text, doc_title=None, doc_category=None):
    """Generate a brief topic label for a chunk.
    
    Returns a single-line description of what specific entities, 
    concepts, and topics this chunk discusses.
    """
    # Truncate very long chunks for the LLM
    if len(chunk_text) > 3000:
        chunk_text = chunk_text[:3000] + "..."
    
    context_hint = ""
    if doc_title:
        context_hint += f"\nThe broader document is titled: {doc_title}"
    if doc_category:
        context_hint += f"\nThe document category: {doc_category}"
    
    prompt = (
        f"<|im_start|>system\n"
        f"You extract topic labels for retrieval. Given a passage, identify the specific "
        f"entities, concepts, names, and topics it discusses. Output a single concise line "
        f"of 10-30 words listing what this passage is specifically about. Include proper "
        f"nouns, technical terms, and metric values. Do not summarize the content; just "
        f"label what it covers.<|im_end|>\n"
        f"<|im_start|>user\n"
        f"Passage:{context_hint}\n\n{chunk_text}<|im_end|>\n"
        f"<|im_start|>assistant\n"
    )
    
    try:
        resp = requests.post(
            f"{CONFIG.ollama_url}/api/generate",
            json={
                "model": TOPIC_MODEL,
                "prompt": prompt,
                "stream": False,
                "options": {
                    "num_predict": 60,
                    "temperature": 0.0,
                    "top_p": 1.0,
                },
                "raw": True,
            },
            timeout=60,
        )
        resp.raise_for_status()
        text = resp.json().get("response", "").strip()
        
        # Take only the first line, drop any "Labels:" prefix
        first_line = text.split("\n")[0].strip()
        for prefix in ("Topics:", "Topic:", "Labels:", "Label:", "Subject:"):
            if first_line.lower().startswith(prefix.lower()):
                first_line = first_line[len(prefix):].strip()
        
        return first_line[:300]  # cap length
    except Exception as e:
        print(f"Topic extraction error: {e}", flush=True)
        return ""