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
Press 'n' to go to next uncovered line, 'b' for previous

98.82

/scope3ai/tracers/huggingface/chat.py

import time
from collections.abc import AsyncIterable, Iterable
from dataclasses import asdict, dataclass
from typing import Any, Callable, Optional, Union

import tiktoken
from huggingface_hub import AsyncInferenceClient, InferenceClient  # type: ignore[import-untyped]
from huggingface_hub import ChatCompletionOutput as _ChatCompletionOutput
from huggingface_hub import ChatCompletionStreamOutput as _ChatCompletionStreamOutput
from requests import Response

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

PROVIDER = PROVIDERS.HUGGINGFACE_HUB.value


@dataclass
class ChatCompletionOutput(_ChatCompletionOutput):
    scope3ai: Optional[Scope3AIContext] = None


@dataclass
class ChatCompletionStreamOutput(_ChatCompletionStreamOutput):
    scope3ai: Optional[Scope3AIContext] = None


def huggingface_chat_wrapper(
    wrapped: Callable, instance: InferenceClient, args: Any, kwargs: Any
) -> Union[ChatCompletionOutput, Iterable[ChatCompletionStreamOutput]]:
    if kwargs.get("stream", False):
        return huggingface_chat_wrapper_stream(wrapped, instance, args, kwargs)
    else:
        return huggingface_chat_wrapper_non_stream(wrapped, instance, args, kwargs)


def huggingface_chat_wrapper_non_stream(
    wrapped: Callable, instance: InferenceClient, args: Any, kwargs: Any
) -> ChatCompletionOutput:
    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[0]
    model = (
        instance.model or kwargs.get("model") or instance.get_recommended_model("chat")
    )
    if http_response:
        compute_time = http_response.headers.get("x-compute-time")
    else:
        compute_time = time.perf_counter() - timer_start
    scope3_row = ImpactRow(
        model=Model(id=model),
        input_tokens=response.usage.prompt_tokens,
        output_tokens=response.usage.completion_tokens,
        request_duration_ms=float(compute_time) * 1000,
        managed_service_id=PROVIDER,
    )
    scope3ai_ctx = Scope3AI.get_instance().submit_impact(scope3_row)
    chat = ChatCompletionOutput(**asdict(response))
    chat.scope3ai = scope3ai_ctx
    return chat


def huggingface_chat_wrapper_stream(
    wrapped: Callable, instance: InferenceClient, args: Any, kwargs: Any
) -> Iterable[ChatCompletionStreamOutput]:
    timer_start = time.perf_counter()
    stream = wrapped(*args, **kwargs)
    token_count = 0
    model = (
        instance.model or kwargs.get("model") or instance.get_recommended_model("chat")
    )
    for chunk in stream:
        token_count += 1
        request_latency = time.perf_counter() - timer_start
        scope3_row = ImpactRow(
            model=Model(id=model),
            output_tokens=token_count,
            request_duration_ms=request_latency * 1000,
            managed_service_id=PROVIDER,
        )
        chunk_data = ChatCompletionStreamOutput(**asdict(chunk))
        scope3_ctx = Scope3AI.get_instance().submit_impact(scope3_row)
        if scope3_ctx is not None:
            chunk_data.scope3ai = scope3_ctx
        yield chunk_data


async def huggingface_async_chat_wrapper(
    wrapped: Callable, instance: AsyncInferenceClient, args: Any, kwargs: Any
) -> Union[ChatCompletionOutput, AsyncIterable[ChatCompletionStreamOutput]]:
    if kwargs.get("stream", False):
        return huggingface_async_chat_wrapper_stream(wrapped, instance, args, kwargs)
    else:
        return await huggingface_async_chat_wrapper_non_stream(
            wrapped, instance, args, kwargs
        )


async def huggingface_async_chat_wrapper_non_stream(
    wrapped: Callable, instance: AsyncInferenceClient, args: Any, kwargs: Any
) -> ChatCompletionOutput:
    timer_start = time.perf_counter()

    response = await wrapped(*args, **kwargs)
    request_latency = time.perf_counter() - timer_start
    model = (
        instance.model or kwargs.get("model") or instance.get_recommended_model("chat")
    )
    encoder = tiktoken.get_encoding("cl100k_base")
    output_tokens = len(encoder.encode(response.choices[0].message.content))
    scope3_row = ImpactRow(
        model=Model(id=model),
        input_tokens=response.usage.prompt_tokens,
        output_tokens=output_tokens,
        request_duration_ms=request_latency * 1000,
        managed_service_id=PROVIDER,
    )

    scope3ai_ctx = Scope3AI.get_instance().submit_impact(scope3_row)
    chat = ChatCompletionOutput(**asdict(response))
    chat.scope3ai = scope3ai_ctx
    return chat


# Todo: How headers works for stream
async def huggingface_async_chat_wrapper_stream(
    wrapped: Callable, instance: AsyncInferenceClient, args: Any, kwargs: Any
) -> AsyncIterable[ChatCompletionStreamOutput]:
    timer_start = time.perf_counter()
    stream = await wrapped(*args, **kwargs)
    token_count = 0
    model_used = instance.model or kwargs["model"]
    async for chunk in stream:
        token_count += 1
        request_latency = time.perf_counter() - timer_start
        scope3_row = ImpactRow(
            model=Model(id=model_used),
            output_tokens=token_count,
            request_duration_ms=request_latency
            * 1000,  # TODO: can we get the header that has the processing time
            managed_service_id=PROVIDER,
        )
        scope3_ctx = Scope3AI.get_instance().submit_impact(scope3_row)
        chunk_data = ChatCompletionStreamOutput(**asdict(chunk))
        if scope3_ctx is not None:
            chunk_data.scope3ai = scope3_ctx
        yield chunk_data

1	import time	4✔
2	from collections.abc import AsyncIterable, Iterable	4✔
3	from dataclasses import asdict, dataclass	4✔
4	from typing import Any, Callable, Optional, Union	4✔
5
6	import tiktoken	4✔
7	from huggingface_hub import AsyncInferenceClient, InferenceClient # type: ignore[import-untyped]	4✔
8	from huggingface_hub import ChatCompletionOutput as _ChatCompletionOutput	4✔
9	from huggingface_hub import ChatCompletionStreamOutput as _ChatCompletionStreamOutput	4✔
10	from requests import Response	4✔
11
12	from scope3ai.api.types import Scope3AIContext, Model, ImpactRow	4✔
13	from scope3ai.constants import PROVIDERS	4✔
14	from scope3ai.lib import Scope3AI	4✔
15	from scope3ai.response_interceptor.requests_interceptor import requests_response_capture	4✔
16
17	PROVIDER = PROVIDERS.HUGGINGFACE_HUB.value	4✔
18
19
20	@dataclass	4✔
21	class ChatCompletionOutput(_ChatCompletionOutput):	4✔
22	scope3ai: Optional[Scope3AIContext] = None	4✔
23
24
25	@dataclass	4✔
26	class ChatCompletionStreamOutput(_ChatCompletionStreamOutput):	4✔
27	scope3ai: Optional[Scope3AIContext] = None	4✔
28
29
30	def huggingface_chat_wrapper(	4✔
31	wrapped: Callable, instance: InferenceClient, args: Any, kwargs: Any
32	) -> Union[ChatCompletionOutput, Iterable[ChatCompletionStreamOutput]]:
33	if kwargs.get("stream", False):	4✔
34	return huggingface_chat_wrapper_stream(wrapped, instance, args, kwargs)	4✔
35	else:
36	return huggingface_chat_wrapper_non_stream(wrapped, instance, args, kwargs)	4✔
37
38
39	def huggingface_chat_wrapper_non_stream(	4✔
40	wrapped: Callable, instance: InferenceClient, args: Any, kwargs: Any
41	) -> ChatCompletionOutput:
42	timer_start = time.perf_counter()	4✔
43	http_response: Response \| None = None	4✔
44	with requests_response_capture() as responses:	4✔
45	response = wrapped(args, *kwargs)	4✔
46	http_responses = responses.get()	4✔
47	if len(http_responses) > 0:	4✔
48	http_response = http_responses[0]	4✔
49	model = (	4✔
50	instance.model or kwargs.get("model") or instance.get_recommended_model("chat")
51	)
52	if http_response:	4✔
53	compute_time = http_response.headers.get("x-compute-time")	4✔
54	else:
NEW 55	compute_time = time.perf_counter() - timer_start	×
56	scope3_row = ImpactRow(	4✔
57	model=Model(id=model),
58	input_tokens=response.usage.prompt_tokens,
59	output_tokens=response.usage.completion_tokens,
60	request_duration_ms=float(compute_time) * 1000,
61	managed_service_id=PROVIDER,
62	)
63	scope3ai_ctx = Scope3AI.get_instance().submit_impact(scope3_row)	4✔
64	chat = ChatCompletionOutput(**asdict(response))	4✔
65	chat.scope3ai = scope3ai_ctx	4✔
66	return chat	4✔
67
68
69	def huggingface_chat_wrapper_stream(	4✔
70	wrapped: Callable, instance: InferenceClient, args: Any, kwargs: Any
71	) -> Iterable[ChatCompletionStreamOutput]:
72	timer_start = time.perf_counter()	4✔
73	stream = wrapped(args, *kwargs)	4✔
74	token_count = 0	4✔
75	model = (	4✔
76	instance.model or kwargs.get("model") or instance.get_recommended_model("chat")
77	)
78	for chunk in stream:	4✔
79	token_count += 1	4✔
80	request_latency = time.perf_counter() - timer_start	4✔
81	scope3_row = ImpactRow(	4✔
82	model=Model(id=model),
83	output_tokens=token_count,
84	request_duration_ms=request_latency * 1000,
85	managed_service_id=PROVIDER,
86	)
87	chunk_data = ChatCompletionStreamOutput(**asdict(chunk))	4✔
88	scope3_ctx = Scope3AI.get_instance().submit_impact(scope3_row)	4✔
89	if scope3_ctx is not None:	4✔
90	chunk_data.scope3ai = scope3_ctx	4✔
91	yield chunk_data	4✔
92
93
94	async def huggingface_async_chat_wrapper(	4✔
95	wrapped: Callable, instance: AsyncInferenceClient, args: Any, kwargs: Any
96	) -> Union[ChatCompletionOutput, AsyncIterable[ChatCompletionStreamOutput]]:
97	if kwargs.get("stream", False):	4✔
98	return huggingface_async_chat_wrapper_stream(wrapped, instance, args, kwargs)	4✔
99	else:
100	return await huggingface_async_chat_wrapper_non_stream(	4✔
101	wrapped, instance, args, kwargs
102	)
103
104
105	async def huggingface_async_chat_wrapper_non_stream(	4✔
106	wrapped: Callable, instance: AsyncInferenceClient, args: Any, kwargs: Any
107	) -> ChatCompletionOutput:
108	timer_start = time.perf_counter()	4✔
109
110	response = await wrapped(args, *kwargs)	4✔
111	request_latency = time.perf_counter() - timer_start	4✔
112	model = (	4✔
113	instance.model or kwargs.get("model") or instance.get_recommended_model("chat")
114	)
115	encoder = tiktoken.get_encoding("cl100k_base")	4✔
116	output_tokens = len(encoder.encode(response.choices[0].message.content))	4✔
117	scope3_row = ImpactRow(	4✔
118	model=Model(id=model),
119	input_tokens=response.usage.prompt_tokens,
120	output_tokens=output_tokens,
121	request_duration_ms=request_latency * 1000,
122	managed_service_id=PROVIDER,
123	)
124
125	scope3ai_ctx = Scope3AI.get_instance().submit_impact(scope3_row)	4✔
126	chat = ChatCompletionOutput(**asdict(response))	4✔
127	chat.scope3ai = scope3ai_ctx	4✔
128	return chat	4✔
129
130
131	# Todo: How headers works for stream
132	async def huggingface_async_chat_wrapper_stream(	4✔
133	wrapped: Callable, instance: AsyncInferenceClient, args: Any, kwargs: Any
134	) -> AsyncIterable[ChatCompletionStreamOutput]:
135	timer_start = time.perf_counter()	4✔
136	stream = await wrapped(args, *kwargs)	4✔
137	token_count = 0	4✔
138	model_used = instance.model or kwargs["model"]	4✔
139	async for chunk in stream:	4✔
140	token_count += 1	4✔
141	request_latency = time.perf_counter() - timer_start	4✔
142	scope3_row = ImpactRow(	4✔
143	model=Model(id=model_used),
144	output_tokens=token_count,
145	request_duration_ms=request_latency
146	* 1000, # TODO: can we get the header that has the processing time
147	managed_service_id=PROVIDER,
148	)
149	scope3_ctx = Scope3AI.get_instance().submit_impact(scope3_row)	4✔
150	chunk_data = ChatCompletionStreamOutput(**asdict(chunk))	4✔
151	if scope3_ctx is not None:	4✔
152	chunk_data.scope3ai = scope3_ctx	4✔
153	yield chunk_data	4✔

scope3data / scope3ai-py / 12681022279

Source File Press 'n' to go to next uncovered line, 'b' for previous

Source File
Press 'n' to go to next uncovered line, 'b' for previous