pyr0ball
8e016d7fe6
- except clause in cancel_benchmark/cancel_finetune narrowed from Exception to _subprocess.TimeoutExpired (C1) - _cancelled_jobs.discard() called after registering new proc to prevent a stale flag from a prior run masking errors (I2) - local `import subprocess` removed from run_benchmark and run_finetune_endpoint; all Popen calls updated to _subprocess.Popen (I1) - test patch targets updated from subprocess.Popen to app.api._subprocess.Popen; cancelled-event tests updated to set flag in proc.wait() side-effect so the discard-on-new-run logic is exercised correctly
569 lines
20 KiB
Python
569 lines
20 KiB
Python
"""Avocet — FastAPI REST layer.
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JSONL read/write helpers and FastAPI app instance.
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Endpoints and static file serving are added in subsequent tasks.
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"""
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from __future__ import annotations
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import hashlib
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import json
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import os
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import subprocess as _subprocess
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import yaml
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from pathlib import Path
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from datetime import datetime, timezone
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from fastapi import FastAPI, HTTPException, Query
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from pydantic import BaseModel
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_ROOT = Path(__file__).parent.parent
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_DATA_DIR: Path = _ROOT / "data" # overridable in tests via set_data_dir()
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_MODELS_DIR: Path = _ROOT / "models" # overridable in tests via set_models_dir()
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_CONFIG_DIR: Path | None = None # None = use real path
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# Process registry for running jobs — used by cancel endpoints.
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# Keys: "benchmark" | "finetune". Values: the live Popen object.
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_running_procs: dict = {}
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_cancelled_jobs: set = set()
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def set_data_dir(path: Path) -> None:
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"""Override data directory — used by tests."""
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global _DATA_DIR
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_DATA_DIR = path
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def _best_cuda_device() -> str:
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"""Return the index of the GPU with the most free VRAM as a string.
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Uses nvidia-smi so it works in the job-seeker env (no torch). Returns ""
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if nvidia-smi is unavailable or no GPUs are found. Restricting the
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training subprocess to a single GPU via CUDA_VISIBLE_DEVICES prevents
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PyTorch DataParallel from replicating the model across all GPUs, which
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would OOM the GPU with less headroom.
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"""
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try:
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out = _subprocess.check_output(
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["nvidia-smi", "--query-gpu=index,memory.free",
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"--format=csv,noheader,nounits"],
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text=True,
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timeout=5,
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)
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best_idx, best_free = "", 0
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for line in out.strip().splitlines():
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parts = line.strip().split(", ")
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if len(parts) == 2:
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idx, free = parts[0].strip(), int(parts[1].strip())
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if free > best_free:
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best_free, best_idx = free, idx
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return best_idx
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except Exception:
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return ""
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def set_models_dir(path: Path) -> None:
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"""Override models directory — used by tests."""
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global _MODELS_DIR
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_MODELS_DIR = path
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def set_config_dir(path: Path | None) -> None:
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"""Override config directory — used by tests."""
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global _CONFIG_DIR
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_CONFIG_DIR = path
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def _config_file() -> Path:
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if _CONFIG_DIR is not None:
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return _CONFIG_DIR / "label_tool.yaml"
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return _ROOT / "config" / "label_tool.yaml"
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def reset_last_action() -> None:
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"""Reset undo state — used by tests."""
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global _last_action
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_last_action = None
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def _queue_file() -> Path:
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return _DATA_DIR / "email_label_queue.jsonl"
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def _score_file() -> Path:
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return _DATA_DIR / "email_score.jsonl"
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def _discarded_file() -> Path:
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return _DATA_DIR / "discarded.jsonl"
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def _read_jsonl(path: Path) -> list[dict]:
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if not path.exists():
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return []
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lines = path.read_text(encoding="utf-8").splitlines()
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return [json.loads(l) for l in lines if l.strip()]
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def _write_jsonl(path: Path, records: list[dict]) -> None:
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path.parent.mkdir(parents=True, exist_ok=True)
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text = "\n".join(json.dumps(r, ensure_ascii=False) for r in records)
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path.write_text(text + "\n" if records else "", encoding="utf-8")
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def _append_jsonl(path: Path, record: dict) -> None:
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path.parent.mkdir(parents=True, exist_ok=True)
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with path.open("a", encoding="utf-8") as f:
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f.write(json.dumps(record, ensure_ascii=False) + "\n")
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def _item_id(item: dict) -> str:
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"""Stable content-hash ID — matches label_tool.py _entry_key dedup logic."""
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key = (item.get("subject", "") + (item.get("body", "") or "")[:100])
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return hashlib.md5(key.encode("utf-8", errors="replace")).hexdigest()
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def _normalize(item: dict) -> dict:
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"""Normalize JSONL item to the Vue frontend schema.
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label_tool.py stores: subject, body, from_addr, date, account (no id).
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The Vue app expects: id, subject, body, from, date, source.
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Both old (from_addr/account) and new (from/source) field names are handled.
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"""
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return {
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"id": item.get("id") or _item_id(item),
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"subject": item.get("subject", ""),
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"body": item.get("body", ""),
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"from": item.get("from") or item.get("from_addr", ""),
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"date": item.get("date", ""),
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"source": item.get("source") or item.get("account", ""),
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}
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app = FastAPI(title="Avocet API")
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# In-memory last-action store (single user, local tool — in-memory is fine)
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_last_action: dict | None = None
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@app.get("/api/queue")
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def get_queue(limit: int = Query(default=10, ge=1, le=50)):
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items = _read_jsonl(_queue_file())
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return {"items": [_normalize(x) for x in items[:limit]], "total": len(items)}
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class LabelRequest(BaseModel):
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id: str
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label: str
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@app.post("/api/label")
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def post_label(req: LabelRequest):
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global _last_action
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items = _read_jsonl(_queue_file())
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match = next((x for x in items if _normalize(x)["id"] == req.id), None)
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if not match:
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raise HTTPException(404, f"Item {req.id!r} not found in queue")
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record = {**match, "label": req.label,
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"labeled_at": datetime.now(timezone.utc).isoformat()}
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_append_jsonl(_score_file(), record)
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_write_jsonl(_queue_file(), [x for x in items if _normalize(x)["id"] != req.id])
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_last_action = {"type": "label", "item": match, "label": req.label}
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return {"ok": True}
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class SkipRequest(BaseModel):
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id: str
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@app.post("/api/skip")
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def post_skip(req: SkipRequest):
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global _last_action
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items = _read_jsonl(_queue_file())
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match = next((x for x in items if _normalize(x)["id"] == req.id), None)
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if not match:
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raise HTTPException(404, f"Item {req.id!r} not found in queue")
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reordered = [x for x in items if _normalize(x)["id"] != req.id] + [match]
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_write_jsonl(_queue_file(), reordered)
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_last_action = {"type": "skip", "item": match}
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return {"ok": True}
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class DiscardRequest(BaseModel):
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id: str
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@app.post("/api/discard")
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def post_discard(req: DiscardRequest):
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global _last_action
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items = _read_jsonl(_queue_file())
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match = next((x for x in items if _normalize(x)["id"] == req.id), None)
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if not match:
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raise HTTPException(404, f"Item {req.id!r} not found in queue")
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record = {**match, "label": "__discarded__",
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"discarded_at": datetime.now(timezone.utc).isoformat()}
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_append_jsonl(_discarded_file(), record)
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_write_jsonl(_queue_file(), [x for x in items if _normalize(x)["id"] != req.id])
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_last_action = {"type": "discard", "item": match}
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return {"ok": True}
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@app.delete("/api/label/undo")
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def delete_undo():
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global _last_action
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if not _last_action:
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raise HTTPException(404, "No action to undo")
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action = _last_action
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item = action["item"] # always the original clean queue item
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# Perform file operations FIRST — only clear _last_action on success
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if action["type"] == "label":
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records = _read_jsonl(_score_file())
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if not records:
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raise HTTPException(409, "Score file is empty — cannot undo label")
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_write_jsonl(_score_file(), records[:-1])
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items = _read_jsonl(_queue_file())
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_write_jsonl(_queue_file(), [item] + items)
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elif action["type"] == "discard":
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records = _read_jsonl(_discarded_file())
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if not records:
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raise HTTPException(409, "Discarded file is empty — cannot undo discard")
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_write_jsonl(_discarded_file(), records[:-1])
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items = _read_jsonl(_queue_file())
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_write_jsonl(_queue_file(), [item] + items)
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elif action["type"] == "skip":
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items = _read_jsonl(_queue_file())
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item_id = _normalize(item)["id"]
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items = [item] + [x for x in items if _normalize(x)["id"] != item_id]
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_write_jsonl(_queue_file(), items)
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# Clear AFTER all file operations succeed
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_last_action = None
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return {"undone": {"type": action["type"], "item": _normalize(item)}}
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# Label metadata — 10 labels matching label_tool.py
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_LABEL_META = [
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{"name": "interview_scheduled", "emoji": "\U0001f4c5", "color": "#4CAF50", "key": "1"},
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{"name": "offer_received", "emoji": "\U0001f389", "color": "#2196F3", "key": "2"},
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{"name": "rejected", "emoji": "\u274c", "color": "#F44336", "key": "3"},
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{"name": "positive_response", "emoji": "\U0001f44d", "color": "#FF9800", "key": "4"},
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{"name": "survey_received", "emoji": "\U0001f4cb", "color": "#9C27B0", "key": "5"},
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{"name": "neutral", "emoji": "\u2b1c", "color": "#607D8B", "key": "6"},
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{"name": "event_rescheduled", "emoji": "\U0001f504", "color": "#FF5722", "key": "7"},
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{"name": "digest", "emoji": "\U0001f4f0", "color": "#00BCD4", "key": "8"},
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{"name": "new_lead", "emoji": "\U0001f91d", "color": "#009688", "key": "9"},
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{"name": "hired", "emoji": "\U0001f38a", "color": "#FFC107", "key": "h"},
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]
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@app.get("/api/config/labels")
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def get_labels():
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return _LABEL_META
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@app.get("/api/config")
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def get_config():
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f = _config_file()
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if not f.exists():
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return {"accounts": [], "max_per_account": 500}
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raw = yaml.safe_load(f.read_text(encoding="utf-8")) or {}
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return {"accounts": raw.get("accounts", []), "max_per_account": raw.get("max_per_account", 500)}
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class ConfigPayload(BaseModel):
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accounts: list[dict]
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max_per_account: int = 500
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@app.post("/api/config")
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def post_config(payload: ConfigPayload):
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f = _config_file()
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f.parent.mkdir(parents=True, exist_ok=True)
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tmp = f.with_suffix(".tmp")
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tmp.write_text(yaml.dump(payload.model_dump(), allow_unicode=True, sort_keys=False),
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encoding="utf-8")
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tmp.rename(f)
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return {"ok": True}
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@app.get("/api/stats")
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def get_stats():
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records = _read_jsonl(_score_file())
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counts: dict[str, int] = {}
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for r in records:
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lbl = r.get("label", "")
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if lbl:
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counts[lbl] = counts.get(lbl, 0) + 1
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return {
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"total": len(records),
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"counts": counts,
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"score_file_bytes": _score_file().stat().st_size if _score_file().exists() else 0,
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}
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@app.get("/api/stats/download")
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def download_stats():
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from fastapi.responses import FileResponse
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if not _score_file().exists():
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raise HTTPException(404, "No score file")
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return FileResponse(
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str(_score_file()),
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filename="email_score.jsonl",
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media_type="application/jsonlines",
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headers={"Content-Disposition": 'attachment; filename="email_score.jsonl"'},
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)
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class AccountTestRequest(BaseModel):
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account: dict
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@app.post("/api/accounts/test")
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def test_account(req: AccountTestRequest):
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from app.imap_fetch import test_connection
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ok, message, count = test_connection(req.account)
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return {"ok": ok, "message": message, "count": count}
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from fastapi.responses import StreamingResponse
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# ---------------------------------------------------------------------------
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# Benchmark endpoints
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# ---------------------------------------------------------------------------
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@app.get("/api/benchmark/results")
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def get_benchmark_results():
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"""Return the most recently saved benchmark results, or an empty envelope."""
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path = _DATA_DIR / "benchmark_results.json"
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if not path.exists():
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return {"models": {}, "sample_count": 0, "timestamp": None}
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return json.loads(path.read_text())
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@app.get("/api/benchmark/run")
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def run_benchmark(include_slow: bool = False):
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"""Spawn the benchmark script and stream stdout as SSE progress events."""
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python_bin = "/devl/miniconda3/envs/job-seeker-classifiers/bin/python"
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script = str(_ROOT / "scripts" / "benchmark_classifier.py")
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cmd = [python_bin, script, "--score", "--save"]
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if include_slow:
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cmd.append("--include-slow")
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def generate():
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try:
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proc = _subprocess.Popen(
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cmd,
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stdout=_subprocess.PIPE,
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stderr=_subprocess.STDOUT,
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text=True,
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bufsize=1,
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cwd=str(_ROOT),
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)
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_running_procs["benchmark"] = proc
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_cancelled_jobs.discard("benchmark") # clear any stale flag from a prior run
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try:
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for line in proc.stdout:
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line = line.rstrip()
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if line:
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yield f"data: {json.dumps({'type': 'progress', 'message': line})}\n\n"
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proc.wait()
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if proc.returncode == 0:
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yield f"data: {json.dumps({'type': 'complete'})}\n\n"
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elif "benchmark" in _cancelled_jobs:
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_cancelled_jobs.discard("benchmark")
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yield f"data: {json.dumps({'type': 'cancelled'})}\n\n"
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else:
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yield f"data: {json.dumps({'type': 'error', 'message': f'Process exited with code {proc.returncode}'})}\n\n"
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finally:
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_running_procs.pop("benchmark", None)
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except Exception as exc:
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yield f"data: {json.dumps({'type': 'error', 'message': str(exc)})}\n\n"
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return StreamingResponse(
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generate(),
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media_type="text/event-stream",
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headers={"Cache-Control": "no-cache", "X-Accel-Buffering": "no"},
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)
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# ---------------------------------------------------------------------------
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# Finetune endpoints
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# ---------------------------------------------------------------------------
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@app.get("/api/finetune/status")
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def get_finetune_status():
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"""Scan models/ for training_info.json files. Returns [] if none exist."""
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models_dir = _MODELS_DIR
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if not models_dir.exists():
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return []
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results = []
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for sub in models_dir.iterdir():
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if not sub.is_dir():
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continue
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info_path = sub / "training_info.json"
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if not info_path.exists():
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continue
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try:
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info = json.loads(info_path.read_text(encoding="utf-8"))
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results.append(info)
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except Exception:
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pass
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return results
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@app.get("/api/finetune/run")
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def run_finetune_endpoint(
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model: str = "deberta-small",
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epochs: int = 5,
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score: list[str] = Query(default=[]),
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):
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"""Spawn finetune_classifier.py and stream stdout as SSE progress events."""
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python_bin = "/devl/miniconda3/envs/job-seeker-classifiers/bin/python"
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script = str(_ROOT / "scripts" / "finetune_classifier.py")
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cmd = [python_bin, script, "--model", model, "--epochs", str(epochs)]
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data_root = _DATA_DIR.resolve()
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for score_file in score:
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resolved = (_DATA_DIR / score_file).resolve()
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if not str(resolved).startswith(str(data_root)):
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raise HTTPException(400, f"Invalid score path: {score_file!r}")
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cmd.extend(["--score", str(resolved)])
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# Pick the GPU with the most free VRAM. Setting CUDA_VISIBLE_DEVICES to a
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# single device prevents DataParallel from replicating the model across all
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# GPUs, which would force a full copy onto the more memory-constrained device.
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proc_env = {**os.environ, "PYTORCH_ALLOC_CONF": "expandable_segments:True"}
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best_gpu = _best_cuda_device()
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if best_gpu:
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proc_env["CUDA_VISIBLE_DEVICES"] = best_gpu
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gpu_note = f"GPU {best_gpu}" if best_gpu else "CPU (no GPU found)"
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def generate():
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yield f"data: {json.dumps({'type': 'progress', 'message': f'[api] Using {gpu_note} (most free VRAM)'})}\n\n"
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try:
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proc = _subprocess.Popen(
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cmd,
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stdout=_subprocess.PIPE,
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stderr=_subprocess.STDOUT,
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text=True,
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bufsize=1,
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cwd=str(_ROOT),
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env=proc_env,
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)
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_running_procs["finetune"] = proc
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_cancelled_jobs.discard("finetune") # clear any stale flag from a prior run
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try:
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for line in proc.stdout:
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line = line.rstrip()
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if line:
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yield f"data: {json.dumps({'type': 'progress', 'message': line})}\n\n"
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proc.wait()
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if proc.returncode == 0:
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yield f"data: {json.dumps({'type': 'complete'})}\n\n"
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elif "finetune" in _cancelled_jobs:
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_cancelled_jobs.discard("finetune")
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|
yield f"data: {json.dumps({'type': 'cancelled'})}\n\n"
|
|
else:
|
|
yield f"data: {json.dumps({'type': 'error', 'message': f'Process exited with code {proc.returncode}'})}\n\n"
|
|
finally:
|
|
_running_procs.pop("finetune", None)
|
|
except Exception as exc:
|
|
yield f"data: {json.dumps({'type': 'error', 'message': str(exc)})}\n\n"
|
|
|
|
return StreamingResponse(
|
|
generate(),
|
|
media_type="text/event-stream",
|
|
headers={"Cache-Control": "no-cache", "X-Accel-Buffering": "no"},
|
|
)
|
|
|
|
|
|
@app.post("/api/benchmark/cancel")
|
|
def cancel_benchmark():
|
|
"""Kill the running benchmark subprocess. 404 if none is running."""
|
|
proc = _running_procs.get("benchmark")
|
|
if proc is None:
|
|
raise HTTPException(404, "No benchmark is running")
|
|
_cancelled_jobs.add("benchmark")
|
|
proc.terminate()
|
|
try:
|
|
proc.wait(timeout=3)
|
|
except _subprocess.TimeoutExpired:
|
|
proc.kill()
|
|
return {"status": "cancelled"}
|
|
|
|
|
|
@app.post("/api/finetune/cancel")
|
|
def cancel_finetune():
|
|
"""Kill the running fine-tune subprocess. 404 if none is running."""
|
|
proc = _running_procs.get("finetune")
|
|
if proc is None:
|
|
raise HTTPException(404, "No finetune is running")
|
|
_cancelled_jobs.add("finetune")
|
|
proc.terminate()
|
|
try:
|
|
proc.wait(timeout=3)
|
|
except _subprocess.TimeoutExpired:
|
|
proc.kill()
|
|
return {"status": "cancelled"}
|
|
|
|
|
|
@app.get("/api/fetch/stream")
|
|
def fetch_stream(
|
|
accounts: str = Query(default=""),
|
|
days_back: int = Query(default=90, ge=1, le=365),
|
|
limit: int = Query(default=150, ge=1, le=1000),
|
|
mode: str = Query(default="wide"),
|
|
):
|
|
from app.imap_fetch import fetch_account_stream
|
|
|
|
selected_names = {n.strip() for n in accounts.split(",") if n.strip()}
|
|
config = get_config() # reuse existing endpoint logic
|
|
selected = [a for a in config["accounts"] if a.get("name") in selected_names]
|
|
|
|
def generate():
|
|
known_keys = {_item_id(x) for x in _read_jsonl(_queue_file())}
|
|
total_added = 0
|
|
|
|
for acc in selected:
|
|
try:
|
|
batch_emails: list[dict] = []
|
|
for event in fetch_account_stream(acc, days_back, limit, known_keys):
|
|
if event["type"] == "done":
|
|
batch_emails = event.pop("emails", [])
|
|
total_added += event["added"]
|
|
yield f"data: {json.dumps(event)}\n\n"
|
|
# Write new emails to queue after each account
|
|
if batch_emails:
|
|
existing = _read_jsonl(_queue_file())
|
|
_write_jsonl(_queue_file(), existing + batch_emails)
|
|
except Exception as exc:
|
|
error_event = {"type": "error", "account": acc.get("name", "?"),
|
|
"message": str(exc)}
|
|
yield f"data: {json.dumps(error_event)}\n\n"
|
|
|
|
queue_size = len(_read_jsonl(_queue_file()))
|
|
complete = {"type": "complete", "total_added": total_added, "queue_size": queue_size}
|
|
yield f"data: {json.dumps(complete)}\n\n"
|
|
|
|
return StreamingResponse(generate(), media_type="text/event-stream",
|
|
headers={"Cache-Control": "no-cache", "X-Accel-Buffering": "no"})
|
|
|
|
|
|
# Static SPA — MUST be last (catches all unmatched paths)
|
|
_DIST = _ROOT / "web" / "dist"
|
|
if _DIST.exists():
|
|
from fastapi.responses import FileResponse
|
|
from fastapi.staticfiles import StaticFiles
|
|
|
|
# Serve index.html with no-cache so browsers always fetch fresh HTML after rebuilds.
|
|
# Hashed assets (/assets/index-abc123.js) can be cached forever — they change names
|
|
# when content changes (standard Vite cache-busting strategy).
|
|
_NO_CACHE = {"Cache-Control": "no-cache, no-store, must-revalidate", "Pragma": "no-cache"}
|
|
|
|
@app.get("/")
|
|
def get_spa_root():
|
|
return FileResponse(str(_DIST / "index.html"), headers=_NO_CACHE)
|
|
|
|
app.mount("/", StaticFiles(directory=str(_DIST), html=True), name="spa")
|