12681022279

Committed 08 Jan 2025 11:43PM UTC coverage: 95.11% (+14.6%) from 80.557%

Build # 12681022279

Build Type

Pull #56

github

Specific Base da957e

Committed by

kevdevg

Commit Message

fix: add try catch to pillow images

Pull Request Pull Request #56: feat(huggingface): add support for image-to-image/ text-to-speech

Run Details

155 of 169 new or added lines in 7 files covered. (91.72%)

26 existing lines in 4 files now uncovered.

1770 of 1861 relevant lines covered (95.11%)

3.8 hits per line

Source File
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94.44

/scope3ai/tracers/huggingface/translation.py

import time
from dataclasses import dataclass, asdict
from typing import Any, Callable, Optional, Union

import tiktoken
from aiohttp import ClientResponse
from huggingface_hub import InferenceClient, AsyncInferenceClient  # type: ignore[import-untyped]
from huggingface_hub import TranslationOutput as _TranslationOutput
from requests import Response

from scope3ai.api.types import Scope3AIContext, Model, ImpactRow
from scope3ai.api.typesgen import Task
from scope3ai.constants import PROVIDERS
from scope3ai.lib import Scope3AI
from scope3ai.response_interceptor.aiohttp_interceptor import aiohttp_response_capture
from scope3ai.response_interceptor.requests_interceptor import requests_response_capture

PROVIDER = PROVIDERS.HUGGINGFACE_HUB.value


@dataclass
class TranslationOutput(_TranslationOutput):
    scope3ai: Optional[Scope3AIContext] = None


def _hugging_face_translation_wrapper(
    timer_start: Any,
    model: Any,
    response: Any,
    http_response: Union[ClientResponse, Response],
    args: Any,
    kwargs: Any,
) -> TranslationOutput:
    encoder = tiktoken.get_encoding("cl100k_base")
    input_tokens = None
    if http_response:
        compute_time = http_response.headers.get("x-compute-time")
        input_tokens = http_response.headers.get("x-compute-characters")
    else:
        compute_time = time.perf_counter() - timer_start
    if not input_tokens:
        prompt = args[0] if len(args) > 0 else kwargs.get("text", "")
        input_tokens = len(encoder.encode(prompt)) if prompt != "" else 0
    output_tokens = len(encoder.encode(response.translation_text))
    scope3_row = ImpactRow(
        model=Model(id=model),
        task=Task.translation,
        input_tokens=input_tokens,
        output_tokens=output_tokens,
        request_duration_ms=float(compute_time) * 1000,
        managed_service_id=PROVIDER,
    )

    scope3_ctx = Scope3AI.get_instance().submit_impact(scope3_row)
    result = TranslationOutput(**asdict(response))
    result.scope3ai = scope3_ctx
    return result


async def huggingface_translation_wrapper_async_non_stream(
    wrapped: Callable, instance: AsyncInferenceClient, args: Any, kwargs: Any
) -> TranslationOutput:
    timer_start = time.perf_counter()
    http_response: ClientResponse | None = None
    with aiohttp_response_capture() as responses:
        response = await wrapped(*args, **kwargs)
        http_responses = responses.get()
        if len(http_responses) > 0:
            http_response = http_responses[-1]
    model = kwargs.get("model") or instance.get_recommended_model("translation")
    return _hugging_face_translation_wrapper(
        timer_start, model, response, http_response, args, kwargs
    )


def huggingface_translation_wrapper_non_stream(
    wrapped: Callable, instance: InferenceClient, args: Any, kwargs: Any
) -> TranslationOutput:
    timer_start = time.perf_counter()
    http_response: Response | None = None
    with requests_response_capture() as responses:
        response = wrapped(*args, **kwargs)
        http_responses = responses.get()
        if len(http_responses) > 0:
            http_response = http_responses[-1]
    model = kwargs.get("model") or instance.get_recommended_model("translation")
    return _hugging_face_translation_wrapper(
        timer_start, model, response, http_response, args, kwargs
    )

1	import time	4✔
2	from dataclasses import dataclass, asdict	4✔
3	from typing import Any, Callable, Optional, Union	4✔
4
5	import tiktoken	4✔
6	from aiohttp import ClientResponse	4✔
7	from huggingface_hub import InferenceClient, AsyncInferenceClient # type: ignore[import-untyped]	4✔
8	from huggingface_hub import TranslationOutput as _TranslationOutput	4✔
9	from requests import Response	4✔
10
11	from scope3ai.api.types import Scope3AIContext, Model, ImpactRow	4✔
12	from scope3ai.api.typesgen import Task	4✔
13	from scope3ai.constants import PROVIDERS	4✔
14	from scope3ai.lib import Scope3AI	4✔
15	from scope3ai.response_interceptor.aiohttp_interceptor import aiohttp_response_capture	4✔
16	from scope3ai.response_interceptor.requests_interceptor import requests_response_capture	4✔
17
18	PROVIDER = PROVIDERS.HUGGINGFACE_HUB.value	4✔
19
20
21	@dataclass	4✔
22	class TranslationOutput(_TranslationOutput):	4✔
23	scope3ai: Optional[Scope3AIContext] = None	4✔
24
25
26	def _hugging_face_translation_wrapper(	4✔
27	timer_start: Any,
28	model: Any,
29	response: Any,
30	http_response: Union[ClientResponse, Response],
31	args: Any,
32	kwargs: Any,
33	) -> TranslationOutput:
34	encoder = tiktoken.get_encoding("cl100k_base")	4✔
35	input_tokens = None	4✔
36	if http_response:	4✔
37	compute_time = http_response.headers.get("x-compute-time")	4✔
38	input_tokens = http_response.headers.get("x-compute-characters")	4✔
39	else:
NEW 40	compute_time = time.perf_counter() - timer_start	×
41	if not input_tokens:	4✔
NEW 42	prompt = args[0] if len(args) > 0 else kwargs.get("text", "")	×
NEW 43	input_tokens = len(encoder.encode(prompt)) if prompt != "" else 0	×
44	output_tokens = len(encoder.encode(response.translation_text))	4✔
45	scope3_row = ImpactRow(	4✔
46	model=Model(id=model),
47	task=Task.translation,
48	input_tokens=input_tokens,
49	output_tokens=output_tokens,
50	request_duration_ms=float(compute_time) * 1000,
51	managed_service_id=PROVIDER,
52	)
53
54	scope3_ctx = Scope3AI.get_instance().submit_impact(scope3_row)	4✔
55	result = TranslationOutput(**asdict(response))	4✔
56	result.scope3ai = scope3_ctx	4✔
57	return result	4✔
58
59
60	async def huggingface_translation_wrapper_async_non_stream(	4✔
61	wrapped: Callable, instance: AsyncInferenceClient, args: Any, kwargs: Any
62	) -> TranslationOutput:
63	timer_start = time.perf_counter()	4✔
64	http_response: ClientResponse \| None = None	4✔
65	with aiohttp_response_capture() as responses:	4✔
66	response = await wrapped(args, *kwargs)	4✔
67	http_responses = responses.get()	4✔
68	if len(http_responses) > 0:	4✔
69	http_response = http_responses[-1]	4✔
70	model = kwargs.get("model") or instance.get_recommended_model("translation")	4✔
71	return _hugging_face_translation_wrapper(	4✔
72	timer_start, model, response, http_response, args, kwargs
73	)
74
75
76	def huggingface_translation_wrapper_non_stream(	4✔
77	wrapped: Callable, instance: InferenceClient, args: Any, kwargs: Any
78	) -> TranslationOutput:
79	timer_start = time.perf_counter()	4✔
80	http_response: Response \| None = None	4✔
81	with requests_response_capture() as responses:	4✔
82	response = wrapped(args, *kwargs)	4✔
83	http_responses = responses.get()	4✔
84	if len(http_responses) > 0:	4✔
85	http_response = http_responses[-1]	4✔
86	model = kwargs.get("model") or instance.get_recommended_model("translation")	4✔
87	return _hugging_face_translation_wrapper(	4✔
88	timer_start, model, response, http_response, args, kwargs
89	)

scope3data / scope3ai-py / 12681022279

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