| |
| import os |
| from fastapi import FastAPI, HTTPException, BackgroundTasks |
| from fastapi.responses import FileResponse, JSONResponse |
| from fastapi.staticfiles import StaticFiles |
| from pydantic import BaseModel |
|
|
| from src.hitl.db import QuarantineDatabase |
| from src.hitl.schemas import ReviewAction, JobTrigger |
|
|
| app = FastAPI(title="MiniSEAL HITL Dashboard") |
|
|
| db_path = os.environ.get("HITL_DB_PATH", "data/quarantine_store.db") |
| db = QuarantineDatabase(db_path) |
|
|
| |
| os.makedirs("ui", exist_ok=True) |
| app.mount("/ui", StaticFiles(directory="ui"), name="ui") |
|
|
| @app.get("/") |
| def serve_index(): |
| if not os.path.exists("ui/index.html"): |
| return JSONResponse({"error": "UI not built yet"}) |
| return FileResponse("ui/index.html") |
|
|
|
|
| @app.get("/health") |
| def health(): |
| """Lightweight liveness probe used by the gateway.""" |
| return {"status": "ok", "service": "hitl"} |
|
|
| @app.get("/api/proposals") |
| def get_proposals(status: str = "PENDING"): |
| """Returns proposals based on status (PENDING, APPROVED, REJECTED).""" |
| rows = db.list_proposals(status) |
| return rows |
|
|
| @app.get("/api/metrics") |
| def get_metrics(): |
| """Returns placeholder metrics.""" |
| return { |
| "dpo_final_loss": 4.19, |
| "dpo_chosen_acc": "74%", |
| "proposals_approved": len(db.list_proposals("APPROVED")), |
| "proposals_rejected": len(db.list_proposals("REJECTED")) |
| } |
|
|
| @app.post("/api/review") |
| def review_proposal(action: ReviewAction): |
| """Approves or rejects a proposal.""" |
| db.update_status(action.proposal_id, action.action) |
| return {"message": f"Proposal {action.proposal_id} updated to {action.action}"} |
|
|
| def run_lora_training(epochs: int, lr: float): |
| """Run LoRA training on approved proposals in-process (no fragile subprocess).""" |
| from src.hitl.approved_lora_train import execute_approved_training |
|
|
| base_model = os.environ.get("HITL_BASE_MODEL", "Qwen/Qwen2.5-1.5B-Instruct") |
| output_dir = os.environ.get("HITL_ADAPTER_DIR", "ckpts/adapters") |
|
|
| print(f"Triggering LoRA training on approved proposals (epochs={epochs}, lr={lr})...") |
| path = execute_approved_training( |
| db_path=db_path, |
| base_model_path=base_model, |
| output_dir=output_dir, |
| epochs=epochs, |
| lr=lr, |
| ) |
| print(f"LoRA training complete! Adapter: {path}") |
|
|
| @app.post("/api/train_lora") |
| def trigger_training(trigger: JobTrigger, background_tasks: BackgroundTasks): |
| """Triggers LoRA training on approved proposals in the background.""" |
| approved_count = len(db.list_proposals("APPROVED")) |
| if approved_count == 0: |
| raise HTTPException(status_code=400, detail="No approved proposals available for training.") |
|
|
| epochs = trigger.epochs if trigger.epochs is not None else 5 |
| lr = trigger.lr if trigger.lr is not None else 1e-4 |
| background_tasks.add_task(run_lora_training, epochs, lr) |
| return {"message": f"Started LoRA training on {approved_count} approved proposals!"} |
|
|
