app/agents/verifier.py

# app/agents/verifier.py
from __future__ import annotations
import os, json, requests
from typing import List, Dict

from app.schemas.claim import Claim
from app.schemas.evidence import Evidence
from app.schemas.verdict import Verdict
from app.core.config import WATSONX_BASE_URL, WATSONX_PROJECT, IBM_VERIFIER_MODEL_ID, IBM_API_VERSION
from app.core.auth import get_ibm_iam_token
from app.core.parse_json import parse_json_anywhere 

PROMPT = """You are a precise fact verifier.
Return STRICT JSON ONLY. Your first character MUST be '{' and your last character MUST be '}'.
Schema:
{
  "verdicts": [
    {"claim_id": "string", "label": "supported|refuted|insufficient", "confidence": 0.0, "citation_ids": ["doc_id", "..."], "rationale": "string"}
  ]
}
# No extra text, no markdown, no backticks.

Rules:
- "supported" if at least one evidence snippet directly supports the claim.
- "refuted" if any evidence directly contradicts the claim.
- "insufficient" if evidence is not enough to decide.
- Cite relevant evidence doc_ids in "citation_ids".
- Keep "rationale" ≤ 2 sentences.

Claims (JSON):
{CLAIMS_JSON}

Evidence catalog (doc_id -> snippet) as JSON:
{EVIDENCE_JSON}

Output JSON:
"""


def _gen(url: str, body: dict, timeout: int = 120) -> str:
	"""Low-level call to watsonx text/generation; returns raw model text."""
	tok = get_ibm_iam_token()
	headers = {
		"Authorization": f"Bearer {tok}",
		"Accept": "application/json",
		"Content-Type": "application/json",
	}
	r = requests.post(url, headers=headers, json=body, timeout=timeout)
	r.raise_for_status()
	j = r.json()
	res = j.get("results") or []
	return (res[0].get("generated_text") if res else "") or ""


def _post_generation(prompt: str) -> dict:
	"""Call model → parse with parse_json_anywhere(root='verdicts') → repair once if needed."""
	url = f"{WATSONX_BASE_URL.rstrip('/')}/ml/v1/text/generation?version={IBM_API_VERSION}"
	body = {
		"input": prompt,
		"model_id": IBM_VERIFIER_MODEL_ID,
		"project_id": WATSONX_PROJECT,
		"parameters": {
			"decoding_method": "greedy",
			"max_new_tokens": 600,
			"min_new_tokens": 0,
			"repetition_penalty": 1.0,
			"temperature": 0.0,
		},
	}

	text = _gen(url, body)
	parsed = parse_json_anywhere(text, root_key="verdicts")
	if parsed and parsed.get("verdicts"):
		return parsed

	# One-shot repair: coerce to strict JSON with 'verdicts' root
	repair_body = {
		"input": (
			"Return ONLY valid JSON object with root key 'verdicts' "
			"(no prose, no markdown). If invalid, fix and output JSON:\n\n" + text
		),
		"model_id": IBM_VERIFIER_MODEL_ID,
		"project_id": WATSONX_PROJECT,
		"parameters": {
			"decoding_method": "greedy",
			"max_new_tokens": 400,
			"temperature": 0.0,
		},
	}
	repaired = _gen(url, repair_body)
	reparsed = parse_json_anywhere(repaired, root_key="verdicts")
	if reparsed and reparsed.get("verdicts"):
		return reparsed

	# Debug preview if still not parsable
	print("[verifier] RAW OUTPUT >>>", (text or repaired)[:1000])
	return {"verdicts": []}


def verify(claims: List[Claim], evidence_map: Dict[str, List[Evidence]]) -> List[Verdict]:
	"""
	claims: list of Claim (must have .id and .text)
	evidence_map: claim_id -> List[Evidence] (must have .doc_id, .snippet)
	returns: List[Verdict]
	"""
	# 1) Flatten evidence to a doc_id -> snippet catalog
	doc_catalog: Dict[str, str] = {}
	for lst in evidence_map.values():
		for e in lst:
			doc_catalog.setdefault(e.doc_id, e.snippet)

	# 2) Minimal claims JSON for the LLM
	claims_json = [{"id": c.id, "text": c.text} for c in claims]

	# 3) Build prompt
	prompt = (
		PROMPT
		.replace("{CLAIMS_JSON}", json.dumps(claims_json, ensure_ascii=False))
		.replace("{EVIDENCE_JSON}", json.dumps(doc_catalog, ensure_ascii=False))
	)

	# 4) Call model + robust parse
	try:
		parsed = _post_generation(prompt)
	except Exception as e:
		# Fail-safe: mark all as insufficient
		print(f"[verifier] generation failed: {e}")
		return [
			Verdict(
				claim_id=c.id,
				label="insufficient",
				confidence=0.4,
				best_evidence_id="",
				rationale="Verifier offline; defaulting to insufficient.",
				citation_ids=[],
			)
			for c in claims
		]

	# 5) Convert to Verdict[]
	allowed = {"supported", "refuted", "insufficient"}
	items = parsed.get("verdicts", []) or []

	# Tiebreaker: top retrieved evidence per claim
	top_ev: Dict[str, str] = {}
	for c in claims:
		evs = evidence_map.get(c.id, [])
		best = max(evs, key=lambda e: e.score, default=None)
		top_ev[c.id] = best.doc_id if best else ""

	out: List[Verdict] = []
	for it in items:
		cid = it.get("claim_id", "")
		label = (it.get("label") or "").lower()
		conf = float(it.get("confidence", 0.5))
		cites = it.get("citation_ids") or []
		rationale = (it.get("rationale") or "")[:300]
		if label not in allowed:
			label = "insufficient"

		# choose best_evidence_id from cited doc_ids or fallback to top_ev
		best_id = next((d for d in cites if d in doc_catalog), "") or top_ev.get(cid, "")

		out.append(Verdict(
			claim_id=cid,
			label=label,
			confidence=conf,
			best_evidence_id=best_id,
			rationale=rationale,
			citation_ids=cites,
		))

	# Ensure every claim has a verdict
	have = {v.claim_id for v in out}
	for c in claims:
		if c.id not in have:
			out.append(Verdict(
				claim_id=c.id,
				label="insufficient",
				confidence=0.4,
				best_evidence_id=top_ev.get(c.id, ""),
				rationale="No explicit verdict returned; marking as insufficient.",
				citation_ids=[],
			))
	return out