import sys
import os
import json
import re
from langchain_groq import ChatGroq
from langchain_core.messages import HumanMessage

# Add project root to path
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

from app.core.config import settings
from app.services.neocortex import _is_valid_node, _sanitize_relation

def extract_and_debug(text: str, user_id: str = "default_user"):
    api_key = settings.GROQ_API_KEY if settings.GROQ_API_KEY else "dummy_key"
    llm = ChatGroq(model="llama-3.1-8b-instant", api_key=api_key)
    
    owner = user_id.upper()
    prompt = f"""You are a child's brain learning about the world. Read the text and pick out SIMPLE facts as connections between concepts.

Think like a child drawing a mind-map:
- "{owner}" is the person speaking. If they say "I like X" → {owner} --LIKES--> X
- Extract only SHORT concept names (1-3 words). Never use full sentences as names.
- Focus on: people, places, things, hobbies, foods, animals, feelings, skills, jobs

RULES:
1. Nodes must be 1-3 word concept names, ALL CAPS. Example: "CRICKET", "DELHI", "MOM", "CODING"
2. Relations must be simple verbs: LIKES, IS_A, LIVES_IN, PLAYS, WORKS_AT, HAS, KNOWS, STUDIES, etc.
3. "I" or "my" in the text refers to "{owner}" — always use "{owner}" as the node name for the speaker.
4. DO NOT create nodes named "USER", "SOMA", "AI", "ASSISTANT", or any chat/bot terms.
5. If the text is just greetings or small talk with zero factual content, return an empty triples list.

Text:
{text}

Return the extracted facts ONLY as a valid JSON block in this exact format:
{{
  "triples": [
    {{"subject": "SUBJECT", "relation": "RELATION", "object": "OBJECT"}}
  ]
}}
Do not write any other explanation or thoughts outside the JSON block. If there are no facts, return: {{"triples": []}}"""

    response = llm.invoke([HumanMessage(content=prompt)])
    content = response.content.strip()
    
    print("RAW RESPONSE:")
    print(content)
    
    match = re.search(r'\{.*\}', content, re.DOTALL)
    if match:
        data = json.loads(match.group(0))
        for t in data.get("triples", []):
            subj = str(t.get("subject", "")).strip().upper()
            rel = _sanitize_relation(str(t.get("relation", "")))
            obj = str(t.get("object", "")).strip().upper()
            print(f"\nTriple: {subj} --{rel}--> {obj}")
            print(f"  Valid Subject ({subj}): {_is_valid_node(subj)}")
            print(f"  Valid Object ({obj}): {_is_valid_node(obj)}")
            print(f"  Equal: {subj == obj}")

extract_and_debug("Dr. Aris is the creator of the Soma Project. The Soma Project is an advanced cognitive architecture.")