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import re
import time
from dataclasses import dataclass
from typing import TYPE_CHECKING, Any, Dict, Iterator, List, Literal, Optional, Union
from typing_extensions import NotRequired, TypedDict, Unpack
from replicate.exceptions import ModelError, ReplicateError
from replicate.files import upload_file
from replicate.json import encode_json
from replicate.pagination import Page
from replicate.resource import Namespace, Resource
from replicate.stream import EventSource
from replicate.version import Version
try:
from pydantic import v1 as pydantic # type: ignore
except ImportError:
import pydantic # type: ignore
if TYPE_CHECKING:
from replicate.client import Client
from replicate.stream import ServerSentEvent
class Prediction(Resource):
"""
A prediction made by a model hosted on Replicate.
"""
_client: "Client" = pydantic.PrivateAttr()
id: str
"""The unique ID of the prediction."""
model: str
"""An identifier for the model used to create the prediction, in the form `owner/name`."""
version: str
"""An identifier for the version of the model used to create the prediction."""
status: Literal["starting", "processing", "succeeded", "failed", "canceled"]
"""The status of the prediction."""
input: Optional[Dict[str, Any]]
"""The input to the prediction."""
output: Optional[Any]
"""The output of the prediction."""
logs: Optional[str]
"""The logs of the prediction."""
error: Optional[str]
"""The error encountered during the prediction, if any."""
metrics: Optional[Dict[str, Any]]
"""Metrics for the prediction."""
created_at: Optional[str]
"""When the prediction was created."""
started_at: Optional[str]
"""When the prediction was started."""
completed_at: Optional[str]
"""When the prediction was completed, if finished."""
urls: Optional[Dict[str, str]]
"""
URLs associated with the prediction.
The following keys are available:
- `get`: A URL to fetch the prediction.
- `cancel`: A URL to cancel the prediction.
"""
@dataclass
class Progress:
"""
The progress of a prediction.
"""
percentage: float
"""The percentage of the prediction that has completed."""
current: int
"""The number of items that have been processed."""
total: int
"""The total number of items to process."""
_pattern = re.compile(
r"^\s*(?P<percentage>\d+)%\s*\|.+?\|\s*(?P<current>\d+)\/(?P<total>\d+)"
)
@classmethod
def parse(cls, logs: str) -> Optional["Prediction.Progress"]:
"""Parse the progress from the logs of a prediction."""
lines = logs.split("\n")
for idx in reversed(range(len(lines))):
line = lines[idx].strip()
if cls._pattern.match(line):
matches = cls._pattern.findall(line)
if len(matches) == 1:
percentage, current, total = map(int, matches[0])
return cls(percentage / 100.0, current, total)
return None
@property
def progress(self) -> Optional[Progress]:
"""
The progress of the prediction, if available.
"""
if self.logs is None or self.logs == "":
return None
return Prediction.Progress.parse(self.logs)
def wait(self) -> None:
"""
Wait for prediction to finish.
"""
while self.status not in ["succeeded", "failed", "canceled"]:
time.sleep(self._client.poll_interval)
self.reload()
def stream(self) -> Optional[Iterator["ServerSentEvent"]]:
"""
Stream the prediction output.
Raises:
ReplicateError: If the model does not support streaming.
"""
url = self.urls and self.urls.get("stream", None)
if not url or not isinstance(url, str):
raise ReplicateError("Model does not support streaming")
headers = {}
headers["Accept"] = "text/event-stream"
headers["Cache-Control"] = "no-store"
with self._client._client.stream("GET", url, headers=headers) as response:
yield from EventSource(response)
def cancel(self) -> None:
"""
Cancels a running prediction.
"""
canceled = self._client.predictions.cancel(self.id)
for name, value in canceled.dict().items():
setattr(self, name, value)
def reload(self) -> None:
"""
Load this prediction from the server.
"""
updated = self._client.predictions.get(self.id)
for name, value in updated.dict().items():
setattr(self, name, value)
def output_iterator(self) -> Iterator[Any]:
"""
Return an iterator of the prediction output.
"""
# TODO: check output is list
previous_output = self.output or []
while self.status not in ["succeeded", "failed", "canceled"]:
output = self.output or []
new_output = output[len(previous_output) :]
yield from new_output
previous_output = output
time.sleep(self._client.poll_interval) # pylint: disable=no-member
self.reload()
if self.status == "failed":
raise ModelError(self.error)
output = self.output or []
new_output = output[len(previous_output) :]
for output in new_output:
yield output
class Predictions(Namespace):
"""
Namespace for operations related to predictions.
"""
def list(self, cursor: Union[str, "ellipsis", None] = ...) -> Page[Prediction]: # noqa: F821
"""
List your predictions.
Parameters:
cursor: The cursor to use for pagination. Use the value of `Page.next` or `Page.previous`.
Returns:
Page[Prediction]: A page of of predictions.
Raises:
ValueError: If `cursor` is `None`.
"""
if cursor is None:
raise ValueError("cursor cannot be None")
resp = self._client._request(
"GET", "/v1/predictions" if cursor is ... else cursor
)
obj = resp.json()
obj["results"] = [
_json_to_prediction(self._client, result) for result in obj["results"]
]
return Page[Prediction](**obj)
async def async_list(
self,
cursor: Union[str, "ellipsis", None] = ..., # noqa: F821
) -> Page[Prediction]:
"""
List your predictions.
Parameters:
cursor: The cursor to use for pagination. Use the value of `Page.next` or `Page.previous`.
Returns:
Page[Prediction]: A page of of predictions.
Raises:
ValueError: If `cursor` is `None`.
"""
if cursor is None:
raise ValueError("cursor cannot be None")
resp = await self._client._async_request(
"GET", "/v1/predictions" if cursor is ... else cursor
)
obj = resp.json()
obj["results"] = [
_json_to_prediction(self._client, result) for result in obj["results"]
]
return Page[Prediction](**obj)
def get(self, id: str) -> Prediction:
"""
Get a prediction by ID.
Args:
id: The ID of the prediction.
Returns:
Prediction: The prediction object.
"""
resp = self._client._request("GET", f"/v1/predictions/{id}")
return _json_to_prediction(self._client, resp.json())
async def async_get(self, id: str) -> Prediction:
"""
Get a prediction by ID.
Args:
id: The ID of the prediction.
Returns:
Prediction: The prediction object.
"""
resp = await self._client._async_request("GET", f"/v1/predictions/{id}")
return _json_to_prediction(self._client, resp.json())
class CreatePredictionParams(TypedDict):
"""Parameters for creating a prediction."""
webhook: NotRequired[str]
"""The URL to receive a POST request with prediction updates."""
webhook_completed: NotRequired[str]
"""The URL to receive a POST request when the prediction is completed."""
webhook_events_filter: NotRequired[List[str]]
"""List of events to trigger webhooks."""
stream: NotRequired[bool]
"""Enable streaming of prediction output."""
def create(
self,
version: Union[Version, str],
input: Optional[Dict[str, Any]],
**params: Unpack["Predictions.CreatePredictionParams"],
) -> Prediction:
"""
Create a new prediction for the specified model version.
"""
body = _create_prediction_body(
version,
input,
**params,
)
resp = self._client._request(
"POST",
"/v1/predictions",
json=body,
)
return _json_to_prediction(self._client, resp.json())
async def async_create(
self,
version: Union[Version, str],
input: Optional[Dict[str, Any]],
**params: Unpack["Predictions.CreatePredictionParams"],
) -> Prediction:
"""
Create a new prediction for the specified model version.
"""
body = _create_prediction_body(
version,
input,
**params,
)
resp = await self._client._async_request(
"POST",
"/v1/predictions",
json=body,
)
return _json_to_prediction(self._client, resp.json())
def cancel(self, id: str) -> Prediction:
"""
Cancel a prediction.
Args:
id: The ID of the prediction to cancel.
Returns:
Prediction: The canceled prediction object.
"""
resp = self._client._request(
"POST",
f"/v1/predictions/{id}/cancel",
)
return _json_to_prediction(self._client, resp.json())
async def async_cancel(self, id: str) -> Prediction:
"""
Cancel a prediction.
Args:
id: The ID of the prediction to cancel.
Returns:
Prediction: The canceled prediction object.
"""
resp = await self._client._async_request(
"POST",
f"/v1/predictions/{id}/cancel",
)
return _json_to_prediction(self._client, resp.json())
def _create_prediction_body( # pylint: disable=too-many-arguments
version: Optional[Union[Version, str]],
input: Optional[Dict[str, Any]],
webhook: Optional[str] = None,
webhook_completed: Optional[str] = None,
webhook_events_filter: Optional[List[str]] = None,
stream: Optional[bool] = None,
) -> Dict[str, Any]:
body = {}
if input is not None:
body["input"] = encode_json(input, upload_file=upload_file)
if version is not None:
body["version"] = version.id if isinstance(version, Version) else version
if webhook is not None:
body["webhook"] = webhook
if webhook_completed is not None:
body["webhook_completed"] = webhook_completed
if webhook_events_filter is not None:
body["webhook_events_filter"] = webhook_events_filter
if stream is not None:
body["stream"] = stream
return body
def _json_to_prediction(client: "Client", json: Dict[str, Any]) -> Prediction:
prediction = Prediction(**json)
prediction._client = client
return prediction