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Refactor VideoTemplateEditor component: remove AI editing features and related state management, streamline template loading, and update styles for improved layout. Enhance CSS for responsive design and visual consistency across various components, including dropdowns, sync stats, and quiz elements. Remove deprecated premium badge styles and adjust layout for chat components.
66de9c8 | """One door for every generation call: runs the signature, meters tokens, writes a trace, and strips | |
| em dashes from the outputs. Tests replace `predict` to return canned outputs.""" | |
| import logging | |
| import uuid | |
| from concurrent.futures import ThreadPoolExecutor | |
| import dspy | |
| from sqlalchemy.orm import Session | |
| from app.config import settings | |
| from app.llm.postprocess import clean_output | |
| from app.llm.provider import main_lm, provider_name, track_usage | |
| from app.models import DspyTrace, Job, Workspace | |
| from app.services.jobs import record_usage | |
| log = logging.getLogger(__name__) | |
| def _dump(value): | |
| if hasattr(value, "model_dump"): | |
| return value.model_dump() | |
| if isinstance(value, list): | |
| return [_dump(v) for v in value] | |
| return value | |
| def predict( | |
| signature: type[dspy.Signature], | |
| *, | |
| db: Session, | |
| workspace_id: uuid.UUID, | |
| job: Job | None = None, | |
| lm: dspy.LM | None = None, | |
| **inputs, | |
| ) -> dict: | |
| """Run `signature` and return its outputs as plain dicts/lists (pydantic models dumped).""" | |
| lm = lm or main_lm() | |
| with track_usage(lm) as usage, dspy.context(lm=lm): | |
| pred = dspy.Predict(signature)(**inputs) | |
| outputs = {name: clean_output(_dump(pred[name])) for name in signature.output_fields} | |
| _record(db, workspace_id, job, signature, lm, usage, [(inputs, outputs)]) | |
| return outputs | |
| def predict_many( | |
| signature: type[dspy.Signature], | |
| inputs_list: list[dict], | |
| *, | |
| db: Session, | |
| workspace_id: uuid.UUID, | |
| lm: dspy.LM | None = None, | |
| workers: int = 5, | |
| ) -> list[dict | None]: | |
| """`predict` once per inputs, in parallel. Only the model calls run in threads: usage, traces and the commit | |
| happen here, on the caller's session. A call that fails is None in its place.""" | |
| if not inputs_list: | |
| return [] | |
| lm = lm or main_lm() | |
| def call(inputs: dict) -> dict | None: | |
| try: | |
| with dspy.context(lm=lm): | |
| pred = dspy.Predict(signature)(**inputs) | |
| return {name: clean_output(_dump(pred[name])) for name in signature.output_fields} | |
| except Exception: | |
| log.warning("%s failed", signature.__name__, exc_info=True) | |
| return None | |
| with track_usage(lm) as usage, ThreadPoolExecutor(max_workers=min(workers, len(inputs_list))) as pool: | |
| results = list(pool.map(call, inputs_list)) | |
| _record(db, workspace_id, None, signature, lm, usage, | |
| [(i, o) for i, o in zip(inputs_list, results, strict=True) if o is not None]) | |
| return results | |
| def _record(db: Session, workspace_id: uuid.UUID, job: Job | None, signature: type[dspy.Signature], lm: dspy.LM, | |
| usage, calls: list[tuple[dict, dict]]) -> None: | |
| if usage.total: | |
| model = getattr(lm, "model", None) or settings.llm_model # the model that answered, not always the main one | |
| record_usage(db, workspace_id=workspace_id, kind="llm", provider=provider_name(model), model=model, | |
| quantity=usage.total, unit="tokens", cost_usd=usage.cost_usd, job=job) | |
| workspace = db.get(Workspace, workspace_id) | |
| for inputs, outputs in calls: | |
| db.add(DspyTrace( | |
| workspace_id=workspace_id, module=signature.__name__, model=lm.model, | |
| inputs={k: (v if len(str(v)) < 4000 else str(v)[:4000]) for k, v in inputs.items()}, | |
| outputs=outputs, training_allowed=bool(workspace and workspace.training_opt_in), | |
| )) | |
| db.commit() | |