from __future__ import annotations

import datetime
import json
import logging
import threading
import time
from typing import List, Optional, TYPE_CHECKING

from models import AgentTraceEntry

if TYPE_CHECKING:
    from storage import StorageManager

logger = logging.getLogger(__name__)

_HF_DATASET_REPO = "build-small-hackathon/kirana-detective-traces"

# Dataset card pushed once on first publish (ensures format:agent-traces tag is set)
_DATASET_README = """\
---
license: mit
language:
  - en
tags:
  - agent-traces
  - kirana-detective
  - invoice-audit
  - indian-fmcg
format: agent-traces
---

# Kirana Detective — Runtime Audit Traces

Per-audit agent execution traces from the Kirana Detective AI app.
Each file records one full audit run: all six agents, their inputs,
outputs, and timings.

Trace format: one JSON object per file under `traces/`.

- **Build traces** (Claude Code sessions): [build-small-hackathon/kirana-detective-build-traces](https://huggingface.co/datasets/build-small-hackathon/kirana-detective-build-traces)
- **Space**: [build-small-hackathon/kirana-detective](https://huggingface.co/spaces/build-small-hackathon/kirana-detective)
"""
MAX_RETRIES = 3
BACKOFF_BASE_SECONDS = 2  # sleeps 2s, 4s, 8s on successive failures


def _make_trace_entry(
    agent_name: str,
    agent_version: str,
    audit_run_id: str,
    t_start: float,         # time.monotonic() value
    t_end: float,
    input_summary: str,
    output_summary: str,
) -> AgentTraceEntry:
    now_utc = datetime.datetime.now(datetime.timezone.utc)
    duration_ms = int((t_end - t_start) * 1000)
    # Approximate start timestamp from end - duration
    ts_end = now_utc.isoformat()
    ts_start = (now_utc - datetime.timedelta(milliseconds=duration_ms)).isoformat()
    return AgentTraceEntry(
        agent_name=agent_name,
        agent_version=agent_version,
        audit_run_id=audit_run_id,
        timestamp_start=ts_start,
        timestamp_end=ts_end,
        duration_ms=duration_ms,
        input_summary=input_summary,
        output_summary=output_summary,
    )


class AgentTracer:
    def __init__(self, hf_token: Optional[str] = None) -> None:
        self._hf_token = hf_token
        self._buffer: List[AgentTraceEntry] = []

    # ── Collection ────────────────────────────────────────────────────────────

    def collect(self, entry: AgentTraceEntry) -> None:
        self._buffer.append(entry)

    def finalise(self, audit_run_id: str) -> List[AgentTraceEntry]:
        entries = list(self._buffer)
        self._buffer.clear()
        return entries

    # ── Publishing ────────────────────────────────────────────────────────────

    def publish_async(
        self,
        audit_run_id: str,
        entries: List[AgentTraceEntry],
        storage: "StorageManager",
    ) -> None:
        t = threading.Thread(
            target=self._publish_with_retry,
            args=(audit_run_id, entries, storage),
            daemon=True,
        )
        t.start()

    def _publish_with_retry(
        self,
        audit_run_id: str,
        entries: List[AgentTraceEntry],
        storage: "StorageManager",
    ) -> None:
        trace_json = json.dumps(
            [e.__dict__ for e in entries], ensure_ascii=False, default=str
        )

        # Persist to local SQLite first (fast, synchronous)
        storage.save_audit_run(audit_run_id, trace_json)

        # HF Hub publishing disabled — runtime trace dataset was not completed
        logger.info("Trace %s stored locally (HF Hub publishing disabled)", audit_run_id)

    def _publish_to_hf_hub(self, audit_run_id: str, entries: List[AgentTraceEntry]) -> None:
        from huggingface_hub import HfApi, CommitOperationAdd

        if not self._hf_token:
            raise ValueError("HF_TOKEN not set — cannot publish trace")

        api = HfApi(token=self._hf_token)

        # Ensure the dataset repo exists with correct tags on first publish
        api.create_repo(repo_id=_HF_DATASET_REPO, repo_type="dataset", exist_ok=True, private=False)

        now = datetime.datetime.now(datetime.timezone.utc).isoformat()
        row = {
            "audit_run_id": audit_run_id,
            "timestamp": now,
            "agents": [e.__dict__ for e in entries],
        }
        trace_content = (json.dumps(row, ensure_ascii=False) + "\n").encode("utf-8")

        operations = [
            CommitOperationAdd(
                path_in_repo=f"traces/{audit_run_id}.jsonl",
                path_or_fileobj=trace_content,
            ),
        ]

        # Push dataset README on every call (idempotent — ensures tag is always set)
        operations.append(
            CommitOperationAdd(
                path_in_repo="README.md",
                path_or_fileobj=_DATASET_README.encode("utf-8"),
            )
        )

        api.create_commit(
            repo_id=_HF_DATASET_REPO,
            repo_type="dataset",
            operations=operations,
            commit_message=f"Add audit trace {audit_run_id[:8]}",
        )


# ── Module-level helper (used by agents) ─────────────────────────────────────

def make_trace_entry(
    agent_name: str,
    agent_version: str,
    audit_run_id: str,
    t_start: float,
    t_end: float,
    input_summary: str,
    output_summary: str,
) -> AgentTraceEntry:
    return _make_trace_entry(
        agent_name, agent_version, audit_run_id, t_start, t_end, input_summary, output_summary
    )