# backend/relation_extractor.py
import json
import os
import re

import requests
from dotenv import load_dotenv

load_dotenv()

# same env vars as backend/llm.py so one setting controls every local call
# (the old hardcoded mistral:7b default silently 404'd on machines that
# never pulled that exact model — the fallback looked enabled but never ran)
_OLLAMA_BASE = os.getenv("OLLAMA_URL", "http://localhost:11434")
OLLAMA_URL = f"{_OLLAMA_BASE}/api/generate"
OLLAMA_MODEL = os.getenv("OLLAMA_MODEL", "qwen2.5:1.5b")
OLLAMA_TIMEOUT = 60  # seconds per call — CPU inference, not GPU

# ===== Pattern-based extraction (runs first, free, instant) =====
# Each pattern: (regex, rel_type). {A} and {B} are org mention placeholders
# already substituted before matching — see extract_patterns().
RELATION_PATTERNS = [
    (r"\bsubsidiary of\b", "SUBSIDIARY_OF"),
    (r"\bwholly[\s-]owned subsidiary of\b", "SUBSIDIARY_OF"),
    (r"\bparent company\b", "SUBSIDIARY_OF"),
    (r"\bcompetes? with\b", "COMPETITOR_OF"),
    (r"\bcompetitor[s]? (?:of|to|include[s]?)\b", "COMPETITOR_OF"),
    (r"\brivals?\b", "COMPETITOR_OF"),
    (r"\bsupplies? (?:to|for)\b", "SUPPLIER_TO"),
    (r"\bsupplier (?:of|to|for)\b", "SUPPLIER_TO"),
    (r"\bvendor (?:of|to|for)\b", "SUPPLIER_TO"),
    (r"\bpartnered? with\b", "PARTNERED_WITH"),
    (r"\bpartnership with\b", "PARTNERED_WITH"),
    (r"\bjoint venture with\b", "PARTNERED_WITH"),
    (r"\bcollaborat\w* with\b", "PARTNERED_WITH"),
    (r"\bacquired\b", "ACQUIRED"),
    (r"\bacquisition of\b", "ACQUIRED"),
]

def _find_mentions(text: str, org_texts: list) -> list:
    """Find character spans of each org mention in text. Returns list of
    (start, end, org_text), sorted by position, with overlapping spans
    collapsed to the LONGEST match (so 'Apple' inside 'Apple Inc.' doesn't
    also register as a separate standalone mention)."""
    raw_spans = []
    for org in org_texts:
        for m in re.finditer(re.escape(org), text, re.IGNORECASE):
            raw_spans.append((m.start(), m.end(), org))

    raw_spans.sort(key=lambda s: (s[0], -(s[1] - s[0])))  # longest first per start

    spans = []
    for start, end, org in raw_spans:
        overlaps = any(
            not (end <= s_start or start >= s_end)
            for s_start, s_end, _ in spans
        )
        if not overlaps:
            spans.append((start, end, org))

    spans.sort(key=lambda s: s[0])
    return spans


def extract_patterns(text: str, org_texts: list) -> list:
    """Pattern-based pass. Returns list of (org_a, org_b, rel_type).
    Only considers org pairs that co-occur within the SAME SENTENCE —
    char-distance alone isn't enough since two unrelated mentions in
    adjacent sentences can still fall inside a tight char window."""
    spans = _find_mentions(text, org_texts)

    # map each span to the sentence index it falls in
    sentence_bounds = []
    pos = 0
    for sent in re.split(r"(?<=[.!?])\s+", text):
        sentence_bounds.append((pos, pos + len(sent)))
        pos += len(sent) + 1  # approximate, accounts for the split separator

    def sentence_index(char_pos):
        for idx, (s_start, s_end) in enumerate(sentence_bounds):
            if s_start <= char_pos < s_end:
                return idx
        return len(sentence_bounds)  # past the end, treat as unique bucket

    found = {}  # pair_key -> (org_a, org_b, rel_type), first match wins

    for i in range(len(spans)):
        for j in range(i + 1, len(spans)):
            start_a, end_a, org_a = spans[i]
            start_b, end_b, org_b = spans[j]

            if org_a.lower() == org_b.lower():
                continue

            if sentence_index(start_a) != sentence_index(start_b):
                continue  # different sentences, don't connect them

            gap_start, gap_end = end_a, start_b
            if gap_end < gap_start:
                continue

            between = text[gap_start:gap_end].lower()

            matched_rel = None
            for pattern, rel_type in RELATION_PATTERNS:
                if re.search(pattern, between):
                    matched_rel = rel_type
                    break

            if matched_rel:
                pair_key = frozenset([org_a.lower(), org_b.lower()])
                if pair_key not in found:
                    found[pair_key] = (org_a, org_b, matched_rel)

    return list(found.values())


# ===== LLM fallback (local Ollama, only for unmatched co-occurring pairs) =====
LLM_PROMPT_TEMPLATE = """You are extracting relationships between two companies mentioned in a financial document excerpt.

Excerpt:
\"\"\"{chunk_text}\"\"\"

Company A: {org_a}
Company B: {org_b}

Based ONLY on the excerpt above, what is the relationship between Company A and Company B?
Choose exactly one label from this list: SUBSIDIARY_OF, COMPETITOR_OF, SUPPLIER_TO, PARTNERED_WITH, ACQUIRED, BOARD_OVERLAP_WITH, NONE

Respond with ONLY a JSON object, nothing else, in this exact format:
{{"relation": "LABEL", "confidence": "high|low"}}

If the excerpt does not clearly support a relationship, respond with {{"relation": "NONE", "confidence": "low"}}.
"""

VALID_LLM_RELATIONS = {
    "SUBSIDIARY_OF", "COMPETITOR_OF", "SUPPLIER_TO",
    "PARTNERED_WITH", "ACQUIRED", "BOARD_OVERLAP_WITH"
}


def _ollama_available() -> bool:
    try:
        r = requests.get(f"{_OLLAMA_BASE}/api/tags", timeout=2)
        return r.status_code == 200
    except requests.RequestException:
        return False


def extract_llm(chunk_text: str, org_a: str, org_b: str) -> dict | None:
    """Single LLM call for one ambiguous pair. Returns
    {"relation": ..., "confidence": ...} or None on any failure
    (Ollama down, bad JSON, invalid label, timeout)."""
    prompt = LLM_PROMPT_TEMPLATE.format(
        chunk_text=chunk_text[:1000],  # keep prompt short, local model
        org_a=org_a,
        org_b=org_b
    )

    payload = {
        "model": OLLAMA_MODEL,
        "prompt": prompt,
        "stream": False,
        "options": {
            "temperature": 0,
            "num_ctx": int(os.getenv("OLLAMA_NUM_CTX", "4096"))
        }
    }
    # suppress chain-of-thought for thinking models (mirrors backend/llm.py)
    if OLLAMA_MODEL.split(":")[0] in ("qwen3", "deepseek-r1"):
        payload["think"] = False

    try:
        resp = requests.post(
            OLLAMA_URL,
            json=payload,
            timeout=OLLAMA_TIMEOUT
        )
        resp.raise_for_status()
        raw = resp.json().get("response", "").strip()

        # strip thinking blocks and markdown fences if the model adds them anyway
        raw = re.sub(r"<think>.*?</think>", "", raw, flags=re.DOTALL).strip()
        raw = re.sub(r"^```(?:json)?|```$", "", raw, flags=re.MULTILINE).strip()

        parsed = json.loads(raw)
        relation = parsed.get("relation", "NONE")

        if relation not in VALID_LLM_RELATIONS:
            return None

        return {
            "relation": relation,
            "confidence": parsed.get("confidence", "low")
        }

    except (requests.RequestException, json.JSONDecodeError, ValueError, KeyError):
        return None


# ===== Main entry point =====
def extract_relations(
    chunk_text: str,
    org_texts: list,
    use_llm_fallback: bool = True
) -> list:
    """
    Extract typed relationships between co-occurring ORG entities in a chunk.

    Args:
        chunk_text: full text of the chunk
        org_texts: list of ORG entity mention strings found in this chunk
        use_llm_fallback: if True, calls local Ollama for pairs the
            regex patterns miss. Silently skipped if Ollama isn't running.

    Returns:
        list of dicts: {"org_a": str, "org_b": str, "relation": str, "source": "pattern"|"llm"}
    """
    if len(org_texts) < 2:
        return []

    results = []
    pattern_hits = extract_patterns(chunk_text, org_texts)
    matched_pairs = set()

    for org_a, org_b, rel_type in pattern_hits:
        results.append({
            "org_a": org_a, "org_b": org_b,
            "relation": rel_type, "source": "pattern"
        })
        matched_pairs.add(frozenset([org_a.lower(), org_b.lower()]))

    if not use_llm_fallback:
        return results

    spans = _find_mentions(chunk_text, org_texts)
    unique_orgs = list({s[2] for s in spans})

    if len(unique_orgs) < 2:
        return results

    if not _ollama_available():
        print("relation_extractor: Ollama not reachable, skipping LLM fallback")
        return results

    for i in range(len(unique_orgs)):
        for j in range(i + 1, len(unique_orgs)):
            org_a, org_b = unique_orgs[i], unique_orgs[j]

            if org_a.lower() == org_b.lower():
                continue

            pair_key = frozenset([org_a.lower(), org_b.lower()])
            if pair_key in matched_pairs:
                continue  # pattern already found a relation for this pair

            llm_result = extract_llm(chunk_text, org_a, org_b)

            if llm_result and llm_result["relation"] != "NONE":
                results.append({
                    "org_a": org_a, "org_b": org_b,
                    "relation": llm_result["relation"],
                    "source": "llm",
                    "confidence": llm_result["confidence"]
                })

    return results


if __name__ == "__main__":
    test_chunk = (
        "Foxconn is a major supplier to Apple Inc. for iPhone assembly. "
        "In contrast, Apple competes with Samsung in the smartphone market. "
        "Beats Electronics, a subsidiary of Apple, also contributed to revenue."
    )
    test_orgs = ["Foxconn", "Apple Inc.", "Samsung", "Beats Electronics", "Apple"]

    rels = extract_relations(test_chunk, test_orgs, use_llm_fallback=True)
    for r in rels:
        print(r)