12753874046

Committed 13 Jan 2025 06:40PM UTC coverage: 95.076% (+14.5%) from 80.557%

Build # 12753874046

Build Type

Pull #61

github

Specific Base 3a8d3f

Committed by

kevdevg

Commit Message

fix: vision pillow read bytes

Pull Request Pull Request #61: feat(Hugging face): Vision methods - image classification / image segmentation / object detection

Run Details

179 of 189 new or added lines in 5 files covered. (94.71%)

34 existing lines in 9 files now uncovered.

2008 of 2112 relevant lines covered (95.08%)

3.8 hits per line

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98.15

/scope3ai/tracers/huggingface/text_to_speech.py

import time
from dataclasses import dataclass
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 TextToSpeechOutput as _TextToSpeechOutput
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
HUGGING_FACE_TEXT_TO_SPEECH_TASK = "text-to-speech"


@dataclass
class TextToSpeechOutput(_TextToSpeechOutput):
    scope3ai: Optional[Scope3AIContext] = None


def _hugging_face_text_to_speech_wrapper(
    timer_start: Any,
    model: Any,
    response: Any,
    http_response: Union[ClientResponse, Response],
    args: Any,
    kwargs: Any,
) -> TextToSpeechOutput:
    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:
        encoder = tiktoken.get_encoding("cl100k_base")
        prompt = args[0] if len(args) > 0 else kwargs.get("text", "")
        input_tokens = len(encoder.encode(prompt)) if prompt != "" else 0

    scope3_row = ImpactRow(
        model=Model(id=model),
        input_tokens=input_tokens,
        task=Task.text_to_speech,
        request_duration_ms=float(compute_time) * 1000,
        managed_service_id=PROVIDER,
    )

    scope3_ctx = Scope3AI.get_instance().submit_impact(scope3_row)
    result = TextToSpeechOutput(audio=response, sampling_rate=16000)
    result.scope3ai = scope3_ctx
    return result


def huggingface_text_to_speech_wrapper(
    wrapped: Callable, instance: InferenceClient, args: Any, kwargs: Any
) -> TextToSpeechOutput:
    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(
        HUGGING_FACE_TEXT_TO_SPEECH_TASK
    )
    return _hugging_face_text_to_speech_wrapper(
        timer_start, model, response, http_response, args, kwargs
    )


async def huggingface_text_to_speech_wrapper_async(
    wrapped: Callable, instance: AsyncInferenceClient, args: Any, kwargs: Any
) -> TextToSpeechOutput:
    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(
        HUGGING_FACE_TEXT_TO_SPEECH_TASK
    )
    return _hugging_face_text_to_speech_wrapper(
        timer_start, model, response, http_response, args, kwargs
    )

1	import time	4✔
2	from dataclasses import dataclass	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 TextToSpeechOutput as _TextToSpeechOutput	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	HUGGING_FACE_TEXT_TO_SPEECH_TASK = "text-to-speech"	4✔
20
21
22	@dataclass	4✔
23	class TextToSpeechOutput(_TextToSpeechOutput):	4✔
24	scope3ai: Optional[Scope3AIContext] = None	4✔
25
26
27	def _hugging_face_text_to_speech_wrapper(	4✔
28	timer_start: Any,
29	model: Any,
30	response: Any,
31	http_response: Union[ClientResponse, Response],
32	args: Any,
33	kwargs: Any,
34	) -> TextToSpeechOutput:
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:
UNCOV 40	compute_time = time.perf_counter() - timer_start	×
41	if not input_tokens:	4✔
42	encoder = tiktoken.get_encoding("cl100k_base")	4✔
43	prompt = args[0] if len(args) > 0 else kwargs.get("text", "")	4✔
44	input_tokens = len(encoder.encode(prompt)) if prompt != "" else 0	4✔
45
46	scope3_row = ImpactRow(	4✔
47	model=Model(id=model),
48	input_tokens=input_tokens,
49	task=Task.text_to_speech,
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 = TextToSpeechOutput(audio=response, sampling_rate=16000)	4✔
56	result.scope3ai = scope3_ctx	4✔
57	return result	4✔
58
59
60	def huggingface_text_to_speech_wrapper(	4✔
61	wrapped: Callable, instance: InferenceClient, args: Any, kwargs: Any
62	) -> TextToSpeechOutput:
63	timer_start = time.perf_counter()	4✔
64	http_response: Response \| None = None	4✔
65	with requests_response_capture() as responses:	4✔
66	response = 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(	4✔
71	HUGGING_FACE_TEXT_TO_SPEECH_TASK
72	)
73	return _hugging_face_text_to_speech_wrapper(	4✔
74	timer_start, model, response, http_response, args, kwargs
75	)
76
77
78	async def huggingface_text_to_speech_wrapper_async(	4✔
79	wrapped: Callable, instance: AsyncInferenceClient, args: Any, kwargs: Any
80	) -> TextToSpeechOutput:
81	timer_start = time.perf_counter()	4✔
82	http_response: ClientResponse \| None = None	4✔
83	with aiohttp_response_capture() as responses:	4✔
84	response = await wrapped(args, *kwargs)	4✔
85	http_responses = responses.get()	4✔
86	if len(http_responses) > 0:	4✔
87	http_response = http_responses[-1]	4✔
88	model = kwargs.get("model") or instance.get_recommended_model(	4✔
89	HUGGING_FACE_TEXT_TO_SPEECH_TASK
90	)
91	return _hugging_face_text_to_speech_wrapper(	4✔
92	timer_start, model, response, http_response, args, kwargs
93	)

scope3data / scope3ai-py / 12753874046

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