1.0.0 Inital Release
Co-authored-by: tmddn3070 <tmddn3070@gmail.com>
This commit is contained in:
tmddn3070
parent
e06b2f795e
commit
74964b3195
22
.env
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22
.env
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@ -0,0 +1,22 @@
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##DATABASE
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#MongoDB
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MONGODB_URI=mongodb://tmddn3070:ss080826@localhost:27017,
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MONGODB_NAME=RUNABOT,
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#Redis
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REDIS_URI=redis://localhost:6379,
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##SERVER
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MASTER_API_KEY=masterkey,
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LISTEN_PORT=3000,
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LISTEN_HOST=localhost,
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API_KEYS=key1|key2|key3,
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##IMAGE GENERATE
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#1.5
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SD15_SERVERS="http://100.64.0.45:7000|http://100.64.0.24:7860",
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SDXL_SERVERS=http://100.64.0.45:7001,
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#PATH
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MODEL_PATH="BoraML/Modules/Analyze/Model/TaggingModel.onnx"
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6
.idea/developer-tools.xml
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6
.idea/developer-tools.xml
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="DeveloperToolsToolWindowSettingsV1" lastSelectedContentNodeId="ulid-generator">
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<developerToolsConfigurations />
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</component>
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</project>
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15
.idea/git_toolbox_prj.xml
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15
.idea/git_toolbox_prj.xml
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="GitToolBoxProjectSettings">
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<option name="commitMessageIssueKeyValidationOverride">
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<BoolValueOverride>
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<option name="enabled" value="true" />
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</BoolValueOverride>
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</option>
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<option name="commitMessageValidationEnabledOverride">
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<BoolValueOverride>
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<option name="enabled" value="true" />
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</BoolValueOverride>
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</option>
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</component>
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</project>
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32
BoraML/Controllers/Analyze.py
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32
BoraML/Controllers/Analyze.py
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import ujson
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import sentry_sdk
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from BoraML.Modules.Logging import Logging
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from BoraML.Models.Analyze import Analyze_OUTPUT, Analyze_INPUT
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from BoraML.Modules.Analyze import Analyze
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class Analyzer:
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def __init__(self):
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self.analyzer = Analyze()
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async def analyze(self, data: Analyze_INPUT) -> Analyze_OUTPUT:
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try:
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result = await self.analyzer.predict(data.image)
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file = open("BoraML/Modules/Analyze/Model/Translated.json", "r")
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json_file = ujson.load(file)
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for key in list(result.tag.keys()):
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if key in json_file:
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result.tag[json_file[key]] = result.tag.pop(key)
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for key in list(result.character.keys()):
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if key in json_file:
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result.character[json_file[key]] = result.character.pop(key)
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if result.rating in ["explicit", "questionable"]:
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result.nsfw = True
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return Analyze_OUTPUT(status=1, message="Success", tags=result.tag, rating=result.rating, character=result.character, nsfw=result.nsfw)
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except Exception as e:
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Logging().error(str(e))
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sentry_sdk.capture_exception(e)
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return Analyze_OUTPUT(status=-1, message=str(e), tags=None, rating=None, character=None, nsfw=None)
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52
BoraML/Controllers/ImageGenerate.py
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52
BoraML/Controllers/ImageGenerate.py
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from fastapi import APIRouter, HTTPException, status, Security, File, UploadFile
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from BoraML.Models.ImageGenerate import ImageGenerate_XL_INPUT, ImageGenerate_SD15_INPUT, ImageGenerate_OUTPUT
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from BoraML.Modules.ImageGenerate.SDXL import SDXL_Generation
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from BoraML.Modules.ImageGenerate.SD15 import SD15_Generation
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class ImageGenerate:
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def __init__(self):
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self.sdxl = SDXL_Generation()
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self.sd15 = SD15_Generation()
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async def generate_xl(self, data: ImageGenerate_XL_INPUT) -> ImageGenerate_OUTPUT:
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try:
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result = await self.sdxl.generate(
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data.positive,
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data.negative,
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data.sampler,
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data.resolution,
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data.afterprocess,
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data.style,
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data.seed,
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data.steps,
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data.cfg_scale,
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data.nsfw,
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)
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return result
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except Exception as e:
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raise HTTPException(
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status_code=status.HTTP_400_BAD_REQUEST,
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detail=str(e),
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)
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async def generate_sd15(self, data: ImageGenerate_SD15_INPUT) -> ImageGenerate_OUTPUT:
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try:
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result = await self.sd15.generate(
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data.positive,
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data.negative,
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data.res,
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data.afterprocess,
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data.style,
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data.seed,
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data.steps,
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data.cfg_scale,
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data.nsfw,
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)
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return result
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except Exception as e:
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raise HTTPException(
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status_code=status.HTTP_400_BAD_REQUEST,
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detail=str(e),
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)
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20
BoraML/Database/MongoDB/__init__.py
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20
BoraML/Database/MongoDB/__init__.py
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import os
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import dotenv
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from motor.motor_asyncio import AsyncIOMotorClient, AsyncIOMotorCollection
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dotenv.load_dotenv(dotenv.find_dotenv())
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class DBConnection:
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def __init__(self):
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self.client = AsyncIOMotorClient(os.getenv("MONGODB_URL"))
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self.db = self.client[os.getenv("MONGODB_NAME")]
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async def get(self, collection: str) -> AsyncIOMotorCollection:
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"""
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컬렉션을 입력받아 해당 컬렉션의 conn을 반환합니다.
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:param collection:
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:return: AsyncIOMotorCollection
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"""
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return self.db[collection]
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@ -3,22 +3,17 @@ import fastapi
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from fastapi import FastAPI
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from fastapi.middleware import Middleware
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from typing import Any, Dict, Callable, Coroutine
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from BoraML.Modules.Logging import Logging
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class LoggingMiddleware:
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def __init__(self, app: FastAPI):
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self.app = app
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self.logger = Logging()
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async def __call__(self, request: fastapi.Request, call_next):
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self.logger.info(f"Request({request.client.host}): {request.method} {request.url} Args: {request.query_params} Body: {await request.body()}")
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def LoggingMiddleware(app: FastAPI):
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@app.middleware("http")
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async def logging_middleware(request: fastapi.Request, call_next: Callable[..., Coroutine[Any, Any, Any]], *args, **kwargs):
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try:
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response = await call_next(request)
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self.logger.info(f"Response: {response.status_code}")
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logger = Logging()
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logger.info(f"Request: {request.url.path} {request.method} Request Body: {await request.body()}")
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logger.info(f"Response: {response}")
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return response
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def attach(self):
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self.app.add_middleware(LoggingMiddleware)
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except Exception as e:
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pass
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return app
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@ -1,24 +1,19 @@
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import fastapi
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import sentry_sdk
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from typing import Any, Dict, Callable, Coroutine
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from fastapi import FastAPI
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from BoraML.Modules.Logging import Logging
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class SentryMiddleware:
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def __init__(self, app: FastAPI):
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self.app = app
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self.logger = Logging()
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async def __call__(self, request: fastapi.Request, call_next):
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def SentryMiddleware(app: FastAPI):
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@app.middleware("http")
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async def sentry_middleware(request, call_next):
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try:
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response = await call_next(request)
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return response
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except Exception as e:
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sentry_sdk.capture_exception(e)
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self.logger.error(f"Exception: {e}")
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raise
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return response
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def attach(self):
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self.app.add_middleware(SentryMiddleware)
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raise e
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return app
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16
BoraML/Models/Analyze.py
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16
BoraML/Models/Analyze.py
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from typing import List, Tuple, Union, Optional
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from pydantic import BaseModel, Field
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from fastapi import UploadFile
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class Analyze_INPUT(BaseModel):
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image: UploadFile = Field(..., title="Image", description="Image to analyze.")
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tag_threshold: Optional[float] = Field(0.35, title="Tag Threshold", description="Tag Threshold for the AI to analyze the image.")
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character_threshold: Optional[float] = Field(0.85, title="Rating Threshold", description="Rating Threshold for the AI to analyze the image.")
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class Analyze_OUTPUT(BaseModel):
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status: int = Field(..., title="Status", description="Status of the image analysis.")
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message: str = Field(..., title="Message", description="Message of the image analysis.")
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tags: Union[List[str], None] = Field(None, title="Tags", description="Tags of the image analysis.")
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rating: Union[str, None] = Field(None, title="Rating", description="Rating of the image analysis.")
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character: Union[str, None] = Field(None, title="Character", description="Character of the image analysis.")
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nsfw: Union[bool, None] = Field(None, title="NSFW", description="NSFW of the image analysis.")
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37
BoraML/Models/ImageGenerate.py
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37
BoraML/Models/ImageGenerate.py
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from typing import List, Tuple, Union, Optional, Dict
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from pydantic import BaseModel, Field
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class ImageGenerate_XL_INPUT(BaseModel):
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positive: str = Field(..., title="Positive Prompt", description="Positive Prompt for the AI to generate the image.")
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negative: str = Field(..., title="Negative Prompt", description="Negative Prompt for the AI to generate the image.")
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sampler: str = Field(..., title="Sampler Index", description="Sampler Index for the AI to generate the image.")
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resolution: List[int] = Field(..., title="Resolution", description="Resolution for the AI to generate the image.")
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afterprocess: Optional[bool] = Field(False, title="Afterprocess",
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description="Afterprocess for the AI to generate the image.")
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style: Optional[dict] = Field(None, title="Style", description="Style for the AI to generate the image.")
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seed: Optional[int] = Field(None, title="Seed", description="Seed for the AI to generate the image.")
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steps: Optional[int] = Field(28, title="Steps", description="Steps for the AI to generate the image.")
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cfg_scale: Optional[float] = Field(7, title="CFG Scale", description="CFG Scale for the AI to generate the image.")
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nsfw: Optional[bool] = Field(False, title="NSFW", description="NSFW for the AI to generate the image.")
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class ImageGenerate_SD15_INPUT(BaseModel):
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positive: str = Field(..., title="Positive Prompt", description="Positive Prompt for the AI to generate the image.")
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negative: str = Field(..., title="Negative Prompt", description="Negative Prompt for the AI to generate the image.")
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res: List[int] = Field(..., title="Resolution", description="Resolution for the AI to generate the image.")
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afterprocess: Optional[bool] = Field(False, title="Afterprocess",
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description="Afterprocess for the AI to generate the image.")
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style: Optional[dict] = Field(None, title="Style", description="Style for the AI to generate the image.")
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seed: Optional[int] = Field(None, title="Seed", description="Seed for the AI to generate the image.")
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steps: Optional[int] = Field(28, title="Steps", description="Steps for the AI to generate the image.")
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cfg_scale: Optional[float] = Field(7, title="CFG Scale", description="CFG Scale for the AI to generate the image.")
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nsfw: Optional[bool] = Field(False, title="NSFW", description="NSFW for the AI to generate the image.")
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class ImageGenerate_OUTPUT(BaseModel):
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status: int = Field(..., title="Status", description="Status of the image generation.")
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message: str = Field(..., title="Message", description="Message of the image generation.")
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image: Union[str, None] = Field(None, title="Image", description="Image of the image generation.")
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rating: Union[str, None] = Field(None, title="Rating", description="Rating of the image generation.")
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tags: Union[List[str], None, Dict[str, float]] = Field(None, title="Tags", description="Tags of the image generation.")
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23
BoraML/Modules/APIKey.py
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23
BoraML/Modules/APIKey.py
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import os
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import ujson
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import aiohttp
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import asyncio
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import dotenv
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import random
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from fastapi import HTTPException, status, Security, FastAPI
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from fastapi.security import APIKeyHeader, APIKeyQuery
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dotenv.load_dotenv(dotenv.find_dotenv())
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api_key_header = APIKeyHeader(name="x-api-key", auto_error=False)
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API_KEYS = os.getenv("API_KEYS") # list
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print(API_KEYS)
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API_KEY_LIST = map(str, API_KEYS.split("|"))
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print(API_KEY_LIST)
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def get_api_key(
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api_key_header: str = Security(api_key_header)):
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if api_key_header in API_KEYS:
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return api_key_header
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else:
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raise HTTPException(status_code=status.HTTP_403_FORBIDDEN, detail="Could not validate credentials")
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@ -8,9 +8,9 @@ import dotenv
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import numpy as np
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import onnxruntime as rt
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import pandas as pd
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from PIL import Image
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from types import SimpleNamespace
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from concurrent.futures import ThreadPoolExecutor
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executor = ThreadPoolExecutor()
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@ -43,9 +43,7 @@ class Analyze:
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left, right = delta_w // 2, delta_w - (delta_w // 2)
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color = [255, 255, 255]
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new_im = cv2.copyMakeBorder(
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img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color
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)
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new_im = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)
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return new_im
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@staticmethod
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@ -94,7 +92,6 @@ class Analyze:
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labels = list(zip(tag_names, probs[0].astype(float)))
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# Extract ratings, general, and character labels
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ratings_names = [labels[i] for i in rating_indexes]
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general_names = [labels[i] for i in general_indexes]
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character_names = [labels[i] for i in character_indexes]
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@ -106,18 +103,18 @@ class Analyze:
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general_res_sorted = dict(sorted(general_res.items(), key=lambda item: item[1], reverse=True))
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a = ", ".join(general_res_sorted.keys()).replace("_", " ").replace("(", "\(").replace(")", "\)")
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c = ", ".join(general_res_sorted.keys())
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#return a, c, rating, character_res, general_res_sorted
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# return a, c, rating, character_res, general_res_sorted
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return SimpleNamespace(
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rating=rating,
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tag=general_res_sorted,
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character=character_res,
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)
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async def predict(self, image, general_threshold: typing.Optional[float] = None,
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character_threshold: typing.Optional[float] = None) -> SimpleNamespace:
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async def predict(
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self, image, general_threshold: typing.Optional[float] = None, character_threshold: typing.Optional[float] = None
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) -> SimpleNamespace:
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if general_threshold is None:
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general_threshold = self.general_threshold
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if character_threshold is None:
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character_threshold = self.character_threshold
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return await asyncio.get_event_loop().run_in_executor(executor, self._predict_image, image, general_threshold,
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character_threshold)
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return await asyncio.get_event_loop().run_in_executor(executor, self._predict_image, image, general_threshold, character_threshold)
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@ -2,158 +2,101 @@ import base64
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import os
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import random
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import re
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from typing import List
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from typing import Dict, Any
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import aiohttp
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import dotenv
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import orjson
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import sentry_sdk
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from aiogoogletrans import Translator
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from better_profanity import profanity
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from IMAPI.Module.Logging import Logger
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from IMAPI.Module.Tagging import Tagging
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from BoraML.Modules.Logging import Logging
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from BoraML.Modules.ImageGenerate.utils import analyze_process
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from BoraML.Models.ImageGenerate import ImageGenerate_OUTPUT
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dotenv.load_dotenv(dotenv.find_dotenv())
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translator = Translator()
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async def _image_evulate(image: bytes) -> List[str]:
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tag = Tagging()
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predict = await tag.predict(image)
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print(predict)
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rating = max(predict[2], key=predict[2].get)
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json_file = open("IMAPI/Module/Tagging/Model/Translated.json", "r")
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orjson_file = orjson.loads(json_file.read())
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json_file.close()
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for key, value in list(predict[4].items()):
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if key in orjson_file:
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predict[4][orjson_file[key]] = predict[4].pop(key)
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if rating == "explicit" or rating == "questionable":
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return [True, rating, predict[4]]
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return [False, rating, predict[4]]
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BLOCKTAG = [
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"nsfw",
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"nude",
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"nipples",
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"nipple",
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"pussy",
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"public hair",
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"gay",
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"lesbian",
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"corpse",
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"no panties",
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"no panty",
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"no bra",
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"bra",
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"panty",
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"panties",
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"underwear",
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"undergarment",
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"underpants",
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"underpant",
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"blowjob",
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"sex",
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"sexy",
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"pennis",
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"realistic",
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"open breasts",
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"breasts",
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"bikini",
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"swimsuit",
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"give birth",
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"slave"
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]
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||||
class Image_Generation_Legacy:
|
||||
class SD15_Generation:
|
||||
def __init__(self):
|
||||
self.url = os.getenv("IMAGE_GENERATION_HOST")
|
||||
self.block_tag = BLOCKTAG
|
||||
self.url = os.getenv("SD15_SERVERS")
|
||||
self.aiohttp_session = aiohttp.ClientSession()
|
||||
|
||||
async def _before_process(self, positive: str, negative: str, res: list, afterprocess: bool = False, style: dict = None, seed : int = random.randint(0, 1000000), steps : int = 25, cfg_scale: float = 7) -> dict:
|
||||
try:
|
||||
if Image_Generation_Legacy._is_korean(positive):
|
||||
positive = await translator.translate(positive, src="ko", dest="en")
|
||||
positive = positive.text
|
||||
if Image_Generation_Legacy._is_korean(negative):
|
||||
negative = await translator.translate(negative, src="ko", dest="en")
|
||||
negative = negative.text
|
||||
except Exception as e:
|
||||
sentry_sdk.capture_exception(e)
|
||||
Logger().log(f"Error occurred while translating: {e}", "ERROR", exception=e)
|
||||
negative = "(KHFB, AuroraNegative, easynegative, negative_hand-neg, verybadimagenegative_v1.3:0.8), (Worst Quality, Low Quality:1.4), border, skimpy, grayscale, multiple_girls, 3d, realistic, string, multiple hands, chinese, thick abs, chubby abs, lowres, bad anatomy, asymmetric wings, elf, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, large areolae, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, Multiple legs, multiple feet, tie, (necktie:1.5), several hands, three feet, four legs, three legs,animal ears, nsfw, exposure, aissistxlneg" + negative
|
||||
width = res[0]
|
||||
height = res[1]
|
||||
cfg_scale = round(cfg_scale, 1)
|
||||
if style is not None:
|
||||
for key, value in style.items():
|
||||
positive = f"""{positive} <lora:{key}:{value}>"""
|
||||
@staticmethod
|
||||
def _is_korean(string):
|
||||
return bool(re.search(r"[ㄱ-ㅎㅏ-ㅣ가-힣]", string))
|
||||
|
||||
async def _translate(self, text):
|
||||
return (await translator.translate(text, src="ko", dest="en")).text if self._is_korean(text) else text
|
||||
|
||||
async def _before_process(
|
||||
self,
|
||||
positive: str,
|
||||
negative: str,
|
||||
res: list,
|
||||
afterprocess: bool = False,
|
||||
style: dict = None,
|
||||
seed: int = random.randint(0, 1000000),
|
||||
steps: int = 28,
|
||||
cfg_scale: float = 7,
|
||||
) -> dict:
|
||||
positive = await self._translate(positive)
|
||||
negative = await self._translate(negative)
|
||||
negative = (
|
||||
"(KHFB, AuroraNegative, easynegative, negative_hand-neg, verybadimagenegative_v1.3:0.8), (Worst Quality, Low Quality:1.4), border, skimpy, grayscale, multiple_girls, 3d, realistic, string, multiple hands, chinese, thick abs, chubby abs, lowres, bad anatomy, asymmetric wings, elf, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, large areolae, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, Multiple legs, multiple feet, tie, (necktie:1.5), several hands, three feet, four legs, three legs,animal ears, nsfw, exposure,"
|
||||
+ negative
|
||||
)
|
||||
if style is not None:
|
||||
positive += "".join([f" <lora:{key}:{value}>" for key, value in style.items()])
|
||||
payload = {
|
||||
"prompt" : f"""{positive}""",
|
||||
"negative_prompt" : f"""{negative}""",
|
||||
"seed" : seed,
|
||||
"width" : width,
|
||||
"height" : height,
|
||||
"cfg_scale" : cfg_scale,
|
||||
"steps" : steps,
|
||||
"prompt": positive,
|
||||
"negative_prompt": negative,
|
||||
"seed": seed,
|
||||
"width": res[0],
|
||||
"height": res[1],
|
||||
"cfg_scale": round(cfg_scale, 1),
|
||||
"steps": steps,
|
||||
"sampler_index": "DPM++ 2M Karras",
|
||||
"refiner_checkpoint": "smooREFINERV2R10_half",
|
||||
"refiner_switch_at": 0.45,
|
||||
"alwayson_scripts": {
|
||||
"ADetailer": {
|
||||
'args': [
|
||||
{
|
||||
'ad_model': 'face_yolov8n.pt',
|
||||
'ad_inpaint_only_masked': True
|
||||
}]
|
||||
"ADetailer": {"args": [{"ad_model": "face_yolov8n.pt", "ad_inpaint_only_masked": True}]},
|
||||
"sonar": {"args": [{"sampler": "Euler a", "momentum": 0.95, "momentum_hist": 0.75}]},
|
||||
},
|
||||
"sonar" : {
|
||||
'args' : [
|
||||
{
|
||||
'sampler' : 'Euler a',
|
||||
'momentum' : 0.95,
|
||||
'momentum_hist' : 0.75
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
if afterprocess:
|
||||
additional = {
|
||||
"enable_hr" : True,
|
||||
"hr_scale" : 1.3,
|
||||
"hr_upscaler": "R-ESRGAN 4x+ Anime6B",
|
||||
"denoising_strength": 0.6
|
||||
}
|
||||
payload.update(additional)
|
||||
payload.update({"enable_hr": True, "hr_scale": 1.3, "hr_upscaler": "R-ESRGAN 4x+ Anime6B", "denoising_strength": 0.6})
|
||||
return payload
|
||||
|
||||
@staticmethod
|
||||
def _is_korean(string):
|
||||
pattern = re.compile(r"[ㄱ-ㅎㅏ-ㅣ가-힣]")
|
||||
|
||||
match = pattern.search(string)
|
||||
return bool(match)
|
||||
|
||||
async def generate(self, positive: str, negative: str, res: list, is_afterprocess: bool = False, style: dict = None, seed : int = random.randint(0, 1000000), steps : int = 25, cfg_scale: float = 7, nsfw : bool = False) -> dict[str, str | int]:
|
||||
async def generate(
|
||||
self,
|
||||
positive: str,
|
||||
negative: str,
|
||||
res: list,
|
||||
is_afterprocess: bool = False,
|
||||
style: dict = None,
|
||||
seed: int = random.randint(0, 1000000),
|
||||
steps: int = 28,
|
||||
cfg_scale: float = 7,
|
||||
nsfw: bool = False,
|
||||
) -> ImageGenerate_OUTPUT | dict[str, None | str | int | Any] | dict[str, str | int | Any]:
|
||||
if not nsfw:
|
||||
for tag in self.block_tag:
|
||||
if tag in positive:
|
||||
positive = positive.replace(tag, "")
|
||||
positive = profanity.censor(positive, " ")
|
||||
payload = await self._before_process(positive, negative, res, is_afterprocess, style, seed, steps, cfg_scale)
|
||||
response = await self.aiohttp_session.post(self.url + "/sdapi/v1/txt2img", json=payload, timeout=600)
|
||||
url = random.choice(map(str, self.url.split("|")))
|
||||
response = await self.aiohttp_session.post(url + "/sdapi/v1/txt2img", json=payload, timeout=300)
|
||||
if response.status != 200:
|
||||
sentry_sdk.capture_message(f"Image generation API ERROR returned {response.status} text: {await response.text()}")
|
||||
Logger().log(f"Image generation API ERROR returned {response.status} text: {await response.text()}", "ERROR")
|
||||
return {"status": -1, "message": await response.text(), "image": None, "rating": None, "tags": None, "nsfw": None}
|
||||
Logging.error(f"Image generation API ERROR returned {response.status} text: {await response.text()}")
|
||||
#return {"status": -1, "message": await response.text(), "image": None, "rating": None, "tags": None, "nsfw": None}
|
||||
return ImageGenerate_OUTPUT(status=-1, message=await response.text(), image=None, rating=None, tags=None, nsfw=None)
|
||||
else:
|
||||
data = await response.json()
|
||||
evulate = await _image_evulate(base64.b64decode(data["images"][0]))
|
||||
if not nsfw:
|
||||
if evulate[0] is True:
|
||||
return {"status": 2, "message": "NSFW", "rating": evulate[1], "tags": evulate[2], "image": None}
|
||||
return {"status": 1, "image": data["images"][0], "rating": evulate[1], "tags": evulate[2], "message" : "success"}
|
||||
evulate = await analyze_process(base64.b64decode(data["images"][0]), "BoraML/Modules/Analyze/Model/Translated.json")
|
||||
if not nsfw and evulate.nsfw is True:
|
||||
#return {"status": 2, "message": "NSFW", "rating": evulate[1], "tags": evulate[2], "image": None}
|
||||
return ImageGenerate_OUTPUT(status=2, message="NSFW", rating=evulate.rating, tags=evulate.tag, image=None)
|
||||
#return {"status": 1, "image": data["images"][0], "rating": evulate[1], "tags": evulate[2], "message": "success"}
|
||||
return ImageGenerate_OUTPUT(status=1, image=data["images"][0], rating=evulate.rating, tags=evulate.tag, message="success")
|
||||
@ -2,164 +2,116 @@ import base64
|
||||
import os
|
||||
import random
|
||||
import re
|
||||
from typing import List
|
||||
|
||||
import aiohttp
|
||||
import dotenv
|
||||
import orjson
|
||||
|
||||
import sentry_sdk
|
||||
from aiogoogletrans import Translator
|
||||
from better_profanity import profanity
|
||||
|
||||
from BoraML.Modules.Logging import Logging
|
||||
|
||||
from BoraML.Modules.Analyze import Analyze
|
||||
|
||||
from BoraML.Modules.ImageGenerate.utils import analyze_process
|
||||
from BoraML.Models.ImageGenerate import ImageGenerate_OUTPUT
|
||||
dotenv.load_dotenv(dotenv.find_dotenv())
|
||||
|
||||
translator = Translator()
|
||||
|
||||
|
||||
async def _image_evulate(image: bytes) -> List[str]:
|
||||
tag = Tagging()
|
||||
predict = await tag.predict(image)
|
||||
print(predict)
|
||||
rating = max(predict[2], key=predict[2].get)
|
||||
json_file = open("IMAPI/Module/Tagging/Model/Translated.json", "r")
|
||||
orjson_file = orjson.loads(json_file.read())
|
||||
json_file.close()
|
||||
for key, value in list(predict[4].items()):
|
||||
if key in orjson_file:
|
||||
predict[4][orjson_file[key]] = predict[4].pop(key)
|
||||
if rating == "explicit" or rating == "questionable":
|
||||
return [True, rating, predict[4]]
|
||||
return [False, rating, predict[4]]
|
||||
|
||||
|
||||
BLOCKTAG = [
|
||||
"nsfw",
|
||||
"nude",
|
||||
"nipples",
|
||||
"nipple",
|
||||
"pussy",
|
||||
"public hair",
|
||||
"gay",
|
||||
"lesbian",
|
||||
"corpse",
|
||||
"no panties",
|
||||
"no panty",
|
||||
"no bra",
|
||||
"bra",
|
||||
"panty",
|
||||
"panties",
|
||||
"underwear",
|
||||
"undergarment",
|
||||
"underpants",
|
||||
"underpant",
|
||||
"blowjob",
|
||||
"sex",
|
||||
"sexy",
|
||||
"pennis",
|
||||
"realistic",
|
||||
"open breasts",
|
||||
"breasts",
|
||||
"bikini",
|
||||
"swimsuit",
|
||||
"give birth",
|
||||
"slave"
|
||||
]
|
||||
|
||||
class Image_Generation:
|
||||
class SDXL_Generation:
|
||||
def __init__(self):
|
||||
self.url = "http://100.64.0.45:7001"
|
||||
self.block_tag = BLOCKTAG
|
||||
self.url = os.getenv("SDXL_SERVERS")
|
||||
print(self.url)
|
||||
self.aiohttp_session = aiohttp.ClientSession()
|
||||
|
||||
async def _before_process(self, positive: str, negative: str, res: list, afterprocess: bool = False, style: dict = None, seed : int = random.randint(0, 1000000), steps : int = 28, cfg_scale: float = 7) -> dict:
|
||||
try:
|
||||
if Image_Generation._is_korean(positive):
|
||||
positive = await translator.translate(positive, src="ko", dest="en")
|
||||
positive = positive.text
|
||||
if Image_Generation._is_korean(negative):
|
||||
negative = await translator.translate(negative, src="ko", dest="en")
|
||||
negative = negative.text
|
||||
except Exception as e:
|
||||
sentry_sdk.capture_exception(e)
|
||||
Logger().log(f"Error occurred while translating: {e}", "ERROR", exception=e)
|
||||
negative = "(worst quality:1.3), low quality, lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract], nsfw, explicit, nude" + "," + negative
|
||||
width = res[0]
|
||||
height = res[1]
|
||||
cfg_scale = round(cfg_scale, 1)
|
||||
@staticmethod
|
||||
def _is_korean(string):
|
||||
return bool(re.search(r"[ㄱ-ㅎㅏ-ㅣ가-힣]", string))
|
||||
|
||||
async def _translate(self, text):
|
||||
return (await translator.translate(text, src="ko", dest="en")).text if self._is_korean(text) else text
|
||||
|
||||
async def _before_process(
|
||||
self,
|
||||
positive: str,
|
||||
negative: str,
|
||||
sampler: str,
|
||||
res: list,
|
||||
afterprocess: bool = False,
|
||||
style: dict = None,
|
||||
seed: int = random.randint(0, 1000000),
|
||||
steps: int = 28,
|
||||
cfg_scale: float = 7,
|
||||
) -> dict:
|
||||
positive = await self._translate(positive)
|
||||
negative = await self._translate(negative)
|
||||
negative = (
|
||||
"(worst quality:1.3), low quality, lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract], nsfw, explicit, nude, aissistxlneg"
|
||||
+ negative
|
||||
)
|
||||
if style is not None:
|
||||
for key, value in style.items():
|
||||
positive = f"""{positive} <lora:{key}:{value}>"""
|
||||
positive = f"""{positive}""" + " , " + "masterpiece, face, best quality, safe"
|
||||
positive += "".join([f" <lora:{key}:{value}>" for key, value in style.items()])
|
||||
positive += ",masterpiece, face, best quality, safe"
|
||||
payload = {
|
||||
"prompt" : f"""{positive}""",
|
||||
"negative_prompt" : f"""{negative}""",
|
||||
"seed" : seed,
|
||||
"width" : width,
|
||||
"height" : height,
|
||||
"cfg_scale" : cfg_scale,
|
||||
"steps" : steps,
|
||||
"sampler_index": "Euler a",
|
||||
"prompt": positive,
|
||||
"negative_prompt": negative,
|
||||
"seed": seed,
|
||||
"width": res[0],
|
||||
"height": res[1],
|
||||
"cfg_scale": round(cfg_scale, 1),
|
||||
"steps": steps,
|
||||
"sampler_index": sampler,
|
||||
"refiner_checkpoint": "animagine-xl-3.1",
|
||||
"refiner_switch_at": 0.4,
|
||||
"alwayson_scripts": {
|
||||
"ADetailer": {
|
||||
'args': [
|
||||
"args": [
|
||||
{
|
||||
'ad_model': 'face_yolov8n.pt',
|
||||
'ad_inpaint_only_masked': True,
|
||||
'ad_use_cfg_scale': True,
|
||||
'ad_cfg_scale': 7,
|
||||
'ad_use_checkpoint': True,
|
||||
'ad_checkpoint': "waifuart"
|
||||
}]
|
||||
},
|
||||
"sonar" : {
|
||||
'args' : [
|
||||
{
|
||||
'sampler' : 'Euler a',
|
||||
'momentum' : 0.95,
|
||||
'momentum_hist' : 0.75
|
||||
"ad_model": "face_yolov8n.pt",
|
||||
"ad_inpaint_only_masked": True,
|
||||
"ad_use_cfg_scale": True,
|
||||
"ad_cfg_scale": 7,
|
||||
"ad_use_checkpoint": True,
|
||||
"ad_checkpoint": "waifuart",
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
},
|
||||
"sonar": {"args": [{"sampler": "Euler a", "momentum": 0.95, "momentum_hist": 0.75}]},
|
||||
},
|
||||
}
|
||||
if afterprocess:
|
||||
additional = {
|
||||
"enable_hr" : True,
|
||||
"hr_scale" : 1.5,
|
||||
"hr_upscaler": "R-ESRGAN 4x+ Anime6B",
|
||||
"denoising_strength": 0.6
|
||||
}
|
||||
payload.update(additional)
|
||||
payload.update({"enable_hr": True, "hr_scale": 1.9, "hr_upscaler": "R-ESRGAN 4x+ Anime6B", "denoising_strength": 0.6})
|
||||
return payload
|
||||
|
||||
@staticmethod
|
||||
def _is_korean(string):
|
||||
pattern = re.compile(r"[ㄱ-ㅎㅏ-ㅣ가-힣]")
|
||||
|
||||
match = pattern.search(string)
|
||||
return bool(match)
|
||||
|
||||
async def generate(self, positive: str, negative: str, res: list, is_afterprocess: bool = False, style: dict = None, seed : int = random.randint(0, 1000000), steps : int = 28, cfg_scale: float = 7, nsfw : bool = False) -> dict[str, str | int]:
|
||||
async def generate(
|
||||
self,
|
||||
positive: str,
|
||||
negative: str,
|
||||
sampler: str,
|
||||
res: list,
|
||||
is_afterprocess: bool = False,
|
||||
style: dict = None,
|
||||
seed: int = random.randint(0, 1000000),
|
||||
steps: int = 28,
|
||||
cfg_scale: float = 7,
|
||||
nsfw: bool = False,
|
||||
) -> ImageGenerate_OUTPUT:
|
||||
if not nsfw:
|
||||
for tag in self.block_tag:
|
||||
if tag in positive:
|
||||
positive = positive.replace(tag, "")
|
||||
payload = await self._before_process(positive, negative, res, is_afterprocess, style, seed, steps, cfg_scale)
|
||||
print(payload)
|
||||
response = await self.aiohttp_session.post(self.url + "/sdapi/v1/txt2img", json=payload, timeout=300)
|
||||
positive = profanity.censor(positive, " ")
|
||||
payload = await self._before_process(positive, negative, sampler, res, is_afterprocess, style, seed, steps, cfg_scale)
|
||||
if self.url.find("|"):
|
||||
url = self.url.replace(",", "")
|
||||
else:
|
||||
url = random.choice(self.url.split("|")).replace(",", "")
|
||||
response = await self.aiohttp_session.post(url + "/sdapi/v1/txt2img", json=payload, timeout=300)
|
||||
if response.status != 200:
|
||||
sentry_sdk.capture_message(f"Image generation API ERROR returned {response.status} text: {await response.text()}")
|
||||
Logger().log(f"Image generation API ERROR returned {response.status} text: {await response.text()}", "ERROR")
|
||||
return {"status": -1, "message": await response.text(), "image": None, "rating": None, "tags": None, "nsfw": None}
|
||||
Logging.error(f"Image generation API ERROR returned {response.status} text: {await response.text()}")
|
||||
#return {"status": -1, "message": await response.text(), "image": None, "rating": None, "tags": None}
|
||||
return ImageGenerate_OUTPUT(status=-1, message=await response.text(), image=None, rating=None, tags=None)
|
||||
else:
|
||||
data = await response.json()
|
||||
evulate = await _image_evulate(base64.b64decode(data["images"][0]))
|
||||
if not nsfw:
|
||||
if evulate[0] is True:
|
||||
return {"status": 2, "message": "NSFW", "rating": evulate[1], "tags": evulate[2], "image": None}
|
||||
return {"status": 1, "image": data["images"][0], "rating": evulate[1], "tags": evulate[2], "message" : "success"}
|
||||
evulate = await analyze_process(base64.b64decode(data["images"][0]), "BoraML/Modules/Analyze/Model/Translated.json")
|
||||
if not nsfw and evulate.nsfw is True:
|
||||
#return {"status": 2, "message": "NSFW", "rating": evulate[1], "tags": evulate[2], "image": None}
|
||||
return ImageGenerate_OUTPUT(status=2, message="NSFW", rating=evulate.rating, tags=evulate.tag, image=None)
|
||||
#return {"status": 1, "image": data["images"][0], "rating": evulate[1], "tags": evulate[2], "message": "success"}
|
||||
return ImageGenerate_OUTPUT(status=1, image=data["images"][0], rating=evulate.rating, tags=evulate.tag, message="success")
|
||||
|
||||
@ -1,7 +1,6 @@
|
||||
import sentry_sdk
|
||||
import ujson
|
||||
|
||||
from types import SimpleNamespace
|
||||
|
||||
from BoraML.Modules.Analyze import Analyze
|
||||
from BoraML.Modules.Logging import Logging
|
||||
|
||||
@ -11,3 +10,13 @@ async def analyze_process(images: bytes, translate_json_path: str):
|
||||
try:
|
||||
result = await analyzer.predict(images)
|
||||
rating = max(result.rating, key=result.rating.get)
|
||||
with open(translate_json_path, "r") as file:
|
||||
json_file = ujson.load(file)
|
||||
for key in list(result.tag.keys()):
|
||||
if key in json_file:
|
||||
result.tag[json_file[key]] = result.tag.pop(key)
|
||||
nsfw = rating in ["explicit", "questionable"]
|
||||
return SimpleNamespace(nsfw=nsfw, rating=rating, tag=result.tag)
|
||||
except Exception as e:
|
||||
Logging().error(str(e))
|
||||
sentry_sdk.capture_exception(e)
|
||||
|
||||
@ -1,6 +1,5 @@
|
||||
import sentry_sdk
|
||||
|
||||
from sentry_sdk.integrations.starlette import StarletteIntegration
|
||||
from sentry_sdk.integrations.fastapi import FastApiIntegration
|
||||
from sentry_sdk.integrations.redis import RedisIntegration
|
||||
from sentry_sdk.integrations.loguru import LoguruIntegration
|
||||
@ -11,13 +10,7 @@ class Sentry:
|
||||
def __init__(self, dsn: str):
|
||||
self.sentry = sentry_sdk.init(
|
||||
dsn=dsn,
|
||||
integrations=[
|
||||
StarletteIntegration(),
|
||||
FastApiIntegration(),
|
||||
RedisIntegration(),
|
||||
LoguruIntegration(),
|
||||
AsyncioIntegration()
|
||||
],
|
||||
integrations=[FastApiIntegration(), RedisIntegration(), LoguruIntegration(), AsyncioIntegration()],
|
||||
traces_sample_rate=1.0,
|
||||
enable_tracing=True
|
||||
enable_tracing=True,
|
||||
)
|
||||
|
||||
0
BoraML/Modules/json.py
Normal file
0
BoraML/Modules/json.py
Normal file
25
BoraML/Routes/Analyze.py
Normal file
25
BoraML/Routes/Analyze.py
Normal file
@ -0,0 +1,25 @@
|
||||
import sentry_sdk
|
||||
from fastapi import APIRouter, HTTPException, status, Security, File, UploadFile
|
||||
|
||||
from BoraML.Modules.Logging import Logging
|
||||
from BoraML.Modules.APIKey import get_api_key
|
||||
from BoraML.Controllers.Analyze import Analyzer
|
||||
from BoraML.Models.Analyze import Analyze_INPUT, Analyze_OUTPUT
|
||||
|
||||
router = APIRouter()
|
||||
|
||||
analyzer = Analyzer()
|
||||
|
||||
|
||||
@router.post("/", response_model=Analyze_OUTPUT)
|
||||
async def analyze(data: Analyze_INPUT, api_key: str = Security(get_api_key)):
|
||||
try:
|
||||
result = await analyzer.analyze(data)
|
||||
return result
|
||||
except Exception as e:
|
||||
Logging().error(str(e))
|
||||
sentry_sdk.capture_exception(e)
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_400_BAD_REQUEST,
|
||||
detail=str(e),
|
||||
)
|
||||
39
BoraML/Routes/ImageGenerate.py
Normal file
39
BoraML/Routes/ImageGenerate.py
Normal file
@ -0,0 +1,39 @@
|
||||
import sentry_sdk
|
||||
from fastapi import APIRouter, HTTPException, status, Security, File, UploadFile
|
||||
|
||||
from BoraML.Modules.Logging import Logging
|
||||
from BoraML.Modules.APIKey import get_api_key
|
||||
from BoraML.Controllers.ImageGenerate import ImageGenerate
|
||||
from BoraML.Models.ImageGenerate import ImageGenerate_XL_INPUT, ImageGenerate_SD15_INPUT, ImageGenerate_OUTPUT
|
||||
|
||||
router = APIRouter()
|
||||
|
||||
image_generate = ImageGenerate()
|
||||
|
||||
|
||||
@router.post("/xl", response_model=ImageGenerate_OUTPUT)
|
||||
async def generate_xl(data: ImageGenerate_XL_INPUT, api_key: str = Security(get_api_key)):
|
||||
try:
|
||||
result = await image_generate.generate_xl(data)
|
||||
return result
|
||||
except Exception as e:
|
||||
Logging().error(str(e))
|
||||
sentry_sdk.capture_exception(e)
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_400_BAD_REQUEST,
|
||||
detail=str(e),
|
||||
)
|
||||
|
||||
|
||||
@router.post("/sd15", response_model=ImageGenerate_OUTPUT)
|
||||
async def generate_sd15(data: ImageGenerate_SD15_INPUT, api_key: str = Security(get_api_key)):
|
||||
try:
|
||||
result = await image_generate.generate_sd15(data)
|
||||
return result
|
||||
except Exception as e:
|
||||
Logging().error(str(e))
|
||||
sentry_sdk.capture_exception(e)
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_400_BAD_REQUEST,
|
||||
detail=str(e),
|
||||
)
|
||||
9
BoraML/Routes/__init__.py
Normal file
9
BoraML/Routes/__init__.py
Normal file
@ -0,0 +1,9 @@
|
||||
from fastapi import APIRouter
|
||||
|
||||
from BoraML.Routes.ImageGenerate import router as image_generate_router
|
||||
from BoraML.Routes.Analyze import router as analyze_router
|
||||
|
||||
router = APIRouter()
|
||||
|
||||
router.include_router(image_generate_router, tags=["Image Generation"], prefix="/generate")
|
||||
router.include_router(analyze_router, tags=["Image Analysis"], prefix="/analyze")
|
||||
@ -1,2 +1,38 @@
|
||||
from fastapi import FastAPI, Request, Response, HTTPException, status, Depends, APIRouter
|
||||
from fastapi.responses import JSONResponse
|
||||
from fastapi.openapi.docs import get_swagger_ui_html
|
||||
from functools import partial
|
||||
|
||||
from BoraML.Modules.Logging import Logging
|
||||
from BoraML.Middlewares.LoggingMiddleware import LoggingMiddleware
|
||||
from BoraML.Middlewares.SentryMiddleware import SentryMiddleware
|
||||
|
||||
from BoraML.Routes import router as routes
|
||||
app = FastAPI(
|
||||
title="BoraML",
|
||||
description="Machine Learning API",
|
||||
version="0.1.0",
|
||||
)
|
||||
|
||||
#app = SentryMiddleware(app)
|
||||
#app = LoggingMiddleware(app)
|
||||
|
||||
|
||||
@app.get("/")
|
||||
async def root():
|
||||
return {"message": "Hello World"}
|
||||
|
||||
@app.get("/docs")
|
||||
async def get_documentation():
|
||||
return get_swagger_ui_html(openapi_url="/openapi.json", title="docs")
|
||||
|
||||
@app.exception_handler(HTTPException)
|
||||
async def http_exception_handler(request: Request, exc: HTTPException):
|
||||
return JSONResponse(
|
||||
status_code=exc.status_code,
|
||||
content={"message": exc.detail},
|
||||
)
|
||||
|
||||
app.include_router(routes)
|
||||
|
||||
|
||||
|
||||
190
poetry.lock
generated
190
poetry.lock
generated
@ -1016,6 +1016,17 @@ MarkupSafe = ">=2.0"
|
||||
[package.extras]
|
||||
i18n = ["Babel (>=2.7)"]
|
||||
|
||||
[[package]]
|
||||
name = "joblib"
|
||||
version = "1.4.2"
|
||||
description = "Lightweight pipelining with Python functions"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "joblib-1.4.2-py3-none-any.whl", hash = "sha256:06d478d5674cbc267e7496a410ee875abd68e4340feff4490bcb7afb88060ae6"},
|
||||
{file = "joblib-1.4.2.tar.gz", hash = "sha256:2382c5816b2636fbd20a09e0f4e9dad4736765fdfb7dca582943b9c1366b3f0e"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "korcen"
|
||||
version = "0.3.8"
|
||||
@ -1211,6 +1222,30 @@ files = [
|
||||
intel-openmp = "==2021.*"
|
||||
tbb = "==2021.*"
|
||||
|
||||
[[package]]
|
||||
name = "motor"
|
||||
version = "3.4.0"
|
||||
description = "Non-blocking MongoDB driver for Tornado or asyncio"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "motor-3.4.0-py3-none-any.whl", hash = "sha256:4b1e1a0cc5116ff73be2c080a72da078f2bb719b53bc7a6bb9e9a2f7dcd421ed"},
|
||||
{file = "motor-3.4.0.tar.gz", hash = "sha256:c89b4e4eb2e711345e91c7c9b122cb68cce0e5e869ed0387dd0acb10775e3131"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
pymongo = ">=4.5,<5"
|
||||
|
||||
[package.extras]
|
||||
aws = ["pymongo[aws] (>=4.5,<5)"]
|
||||
encryption = ["pymongo[encryption] (>=4.5,<5)"]
|
||||
gssapi = ["pymongo[gssapi] (>=4.5,<5)"]
|
||||
ocsp = ["pymongo[ocsp] (>=4.5,<5)"]
|
||||
snappy = ["pymongo[snappy] (>=4.5,<5)"]
|
||||
srv = ["pymongo[srv] (>=4.5,<5)"]
|
||||
test = ["aiohttp (!=3.8.6)", "mockupdb", "motor[encryption]", "pytest (>=7)", "tornado (>=5)"]
|
||||
zstd = ["pymongo[zstd] (>=4.5,<5)"]
|
||||
|
||||
[[package]]
|
||||
name = "mpmath"
|
||||
version = "1.3.0"
|
||||
@ -2161,6 +2196,87 @@ files = [
|
||||
[package.extras]
|
||||
windows-terminal = ["colorama (>=0.4.6)"]
|
||||
|
||||
[[package]]
|
||||
name = "pymongo"
|
||||
version = "4.7.2"
|
||||
description = "Python driver for MongoDB <http://www.mongodb.org>"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "pymongo-4.7.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:268d8578c0500012140c5460755ea405cbfe541ef47c81efa9d6744f0f99aeca"},
|
||||
{file = "pymongo-4.7.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:827611beb6c483260d520cfa6a49662d980dfa5368a04296f65fa39e78fccea7"},
|
||||
{file = "pymongo-4.7.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a754e366c404d19ff3f077ddeed64be31e0bb515e04f502bf11987f1baa55a16"},
|
||||
{file = "pymongo-4.7.2-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:c44efab10d9a3db920530f7bcb26af8f408b7273d2f0214081d3891979726328"},
|
||||
{file = "pymongo-4.7.2-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:35b3f0c7d49724859d4df5f0445818d525824a6cd55074c42573d9b50764df67"},
|
||||
{file = "pymongo-4.7.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1e37faf298a37ffb3e0809e77fbbb0a32b6a2d18a83c59cfc2a7b794ea1136b0"},
|
||||
{file = "pymongo-4.7.2-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d1bcd58669e56c08f1e72c5758868b5df169fe267501c949ee83c418e9df9155"},
|
||||
{file = "pymongo-4.7.2-cp310-cp310-win32.whl", hash = "sha256:c72d16fede22efe7cdd1f422e8da15760e9498024040429362886f946c10fe95"},
|
||||
{file = "pymongo-4.7.2-cp310-cp310-win_amd64.whl", hash = "sha256:12d1fef77d25640cb78893d07ff7d2fac4c4461d8eec45bd3b9ad491a1115d6e"},
|
||||
{file = "pymongo-4.7.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:fc5af24fcf5fc6f7f40d65446400d45dd12bea933d0299dc9e90c5b22197f1e9"},
|
||||
{file = "pymongo-4.7.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:730778b6f0964b164c187289f906bbc84cb0524df285b7a85aa355bbec43eb21"},
|
||||
{file = "pymongo-4.7.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:47a1a4832ef2f4346dcd1a10a36ade7367ad6905929ddb476459abb4fd1b98cb"},
|
||||
{file = "pymongo-4.7.2-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:e6eab12c6385526d386543d6823b07187fefba028f0da216506e00f0e1855119"},
|
||||
{file = "pymongo-4.7.2-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:37e9ea81fa59ee9274457ed7d59b6c27f6f2a5fe8e26f184ecf58ea52a019cb8"},
|
||||
{file = "pymongo-4.7.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7e9d9d2c0aae73aa4369bd373ac2ac59f02c46d4e56c4b6d6e250cfe85f76802"},
|
||||
{file = "pymongo-4.7.2-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:cb6e00a79dff22c9a72212ad82021b54bdb3b85f38a85f4fc466bde581d7d17a"},
|
||||
{file = "pymongo-4.7.2-cp311-cp311-win32.whl", hash = "sha256:02efd1bb3397e24ef2af45923888b41a378ce00cb3a4259c5f4fc3c70497a22f"},
|
||||
{file = "pymongo-4.7.2-cp311-cp311-win_amd64.whl", hash = "sha256:87bb453ac3eb44db95cb6d5a616fbc906c1c00661eec7f55696253a6245beb8a"},
|
||||
{file = "pymongo-4.7.2-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:12c466e02133b7f8f4ff1045c6b5916215c5f7923bc83fd6e28e290cba18f9f6"},
|
||||
{file = "pymongo-4.7.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:f91073049c43d14e66696970dd708d319b86ee57ef9af359294eee072abaac79"},
|
||||
{file = "pymongo-4.7.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:87032f818bf5052ab742812c715eff896621385c43f8f97cdd37d15b5d394e95"},
|
||||
{file = "pymongo-4.7.2-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:6a87eef394039765679f75c6a47455a4030870341cb76eafc349c5944408c882"},
|
||||
{file = "pymongo-4.7.2-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:d275596f840018858757561840767b39272ac96436fcb54f5cac6d245393fd97"},
|
||||
{file = "pymongo-4.7.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:82102e353be13f1a6769660dd88115b1da382447672ba1c2662a0fbe3df1d861"},
|
||||
{file = "pymongo-4.7.2-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:194065c9d445017b3c82fb85f89aa2055464a080bde604010dc8eb932a6b3c95"},
|
||||
{file = "pymongo-4.7.2-cp312-cp312-win32.whl", hash = "sha256:db4380d1e69fdad1044a4b8f3bb105200542c49a0dde93452d938ff9db1d6d29"},
|
||||
{file = "pymongo-4.7.2-cp312-cp312-win_amd64.whl", hash = "sha256:fadc6e8db7707c861ebe25b13ad6aca19ea4d2c56bf04a26691f46c23dadf6e4"},
|
||||
{file = "pymongo-4.7.2-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:2cb77d09bd012cb4b30636e7e38d00b5f9be5eb521c364bde66490c45ee6c4b4"},
|
||||
{file = "pymongo-4.7.2-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:56bf8b706946952acdea0fe478f8e44f1ed101c4b87f046859e6c3abe6c0a9f4"},
|
||||
{file = "pymongo-4.7.2-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:bcf337d1b252405779d9c79978d6ca15eab3cdaa2f44c100a79221bddad97c8a"},
|
||||
{file = "pymongo-4.7.2-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:4ffd1519edbe311df73c74ec338de7d294af535b2748191c866ea3a7c484cd15"},
|
||||
{file = "pymongo-4.7.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d4d59776f435564159196d971aa89422ead878174aff8fe18e06d9a0bc6d648c"},
|
||||
{file = "pymongo-4.7.2-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:347c49cf7f0ba49ea87c1a5a1984187ecc5516b7c753f31938bf7b37462824fd"},
|
||||
{file = "pymongo-4.7.2-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:84bc00200c3cbb6c98a2bb964c9e8284b641e4a33cf10c802390552575ee21de"},
|
||||
{file = "pymongo-4.7.2-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:fcaf8c911cb29316a02356f89dbc0e0dfcc6a712ace217b6b543805690d2aefd"},
|
||||
{file = "pymongo-4.7.2-cp37-cp37m-win32.whl", hash = "sha256:b48a5650ee5320d59f6d570bd99a8d5c58ac6f297a4e9090535f6561469ac32e"},
|
||||
{file = "pymongo-4.7.2-cp37-cp37m-win_amd64.whl", hash = "sha256:5239ef7e749f1326ea7564428bf861d5250aa39d7f26d612741b1b1273227062"},
|
||||
{file = "pymongo-4.7.2-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:d2dcf608d35644e8d276d61bf40a93339d8d66a0e5f3e3f75b2c155a421a1b71"},
|
||||
{file = "pymongo-4.7.2-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:25eeb2c18ede63891cbd617943dd9e6b9cbccc54f276e0b2e693a0cc40f243c5"},
|
||||
{file = "pymongo-4.7.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9349f0bb17a31371d4cacb64b306e4ca90413a3ad1fffe73ac7cd495570d94b5"},
|
||||
{file = "pymongo-4.7.2-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:ffd4d7cb2e6c6e100e2b39606d38a9ffc934e18593dc9bb326196afc7d93ce3d"},
|
||||
{file = "pymongo-4.7.2-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:9a8bd37f5dabc86efceb8d8cbff5969256523d42d08088f098753dba15f3b37a"},
|
||||
{file = "pymongo-4.7.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1c78f156edc59b905c80c9003e022e1a764c54fd40ac4fea05b0764f829790e2"},
|
||||
{file = "pymongo-4.7.2-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:9d892fb91e81cccb83f507cdb2ea0aa026ec3ced7f12a1d60f6a5bf0f20f9c1f"},
|
||||
{file = "pymongo-4.7.2-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:87832d6076c2c82f42870157414fd876facbb6554d2faf271ffe7f8f30ce7bed"},
|
||||
{file = "pymongo-4.7.2-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:ce1a374ea0e49808e0380ffc64284c0ce0f12bd21042b4bef1af3eb7bdf49054"},
|
||||
{file = "pymongo-4.7.2-cp38-cp38-win32.whl", hash = "sha256:eb0642e5f0dd7e86bb358749cc278e70b911e617f519989d346f742dc9520dfb"},
|
||||
{file = "pymongo-4.7.2-cp38-cp38-win_amd64.whl", hash = "sha256:4bdb5ffe1cd3728c9479671a067ef44dacafc3743741d4dc700c377c4231356f"},
|
||||
{file = "pymongo-4.7.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:743552033c63f0afdb56b9189ab04b5c1dbffd7310cf7156ab98eebcecf24621"},
|
||||
{file = "pymongo-4.7.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:5239776633f7578b81207e5646245415a5a95f6ae5ef5dff8e7c2357e6264bfc"},
|
||||
{file = "pymongo-4.7.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:727ad07952c155cd20045f2ce91143c7dc4fb01a5b4e8012905a89a7da554b0c"},
|
||||
{file = "pymongo-4.7.2-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9385654f01a90f73827af4db90c290a1519f7d9102ba43286e187b373e9a78e9"},
|
||||
{file = "pymongo-4.7.2-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:0d833651f1ba938bb7501f13e326b96cfbb7d98867b2d545ca6d69c7664903e0"},
|
||||
{file = "pymongo-4.7.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cf17ea9cea14d59b0527403dd7106362917ced7c4ec936c4ba22bd36c912c8e0"},
|
||||
{file = "pymongo-4.7.2-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:cecd2df037249d1c74f0af86fb5b766104a5012becac6ff63d85d1de53ba8b98"},
|
||||
{file = "pymongo-4.7.2-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:65b4c00dedbd333698b83cd2095a639a6f0d7c4e2a617988f6c65fb46711f028"},
|
||||
{file = "pymongo-4.7.2-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:d9b6cbc037108ff1a0a867e7670d8513c37f9bcd9ee3d2464411bfabf70ca002"},
|
||||
{file = "pymongo-4.7.2-cp39-cp39-win32.whl", hash = "sha256:cf28430ec1924af1bffed37b69a812339084697fd3f3e781074a0148e6475803"},
|
||||
{file = "pymongo-4.7.2-cp39-cp39-win_amd64.whl", hash = "sha256:e004527ea42a6b99a8b8d5b42b42762c3bdf80f88fbdb5c3a9d47f3808495b86"},
|
||||
{file = "pymongo-4.7.2.tar.gz", hash = "sha256:9024e1661c6e40acf468177bf90ce924d1bc681d2b244adda3ed7b2f4c4d17d7"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
dnspython = ">=1.16.0,<3.0.0"
|
||||
|
||||
[package.extras]
|
||||
aws = ["pymongo-auth-aws (>=1.1.0,<2.0.0)"]
|
||||
encryption = ["certifi", "pymongo-auth-aws (>=1.1.0,<2.0.0)", "pymongocrypt (>=1.6.0,<2.0.0)"]
|
||||
gssapi = ["pykerberos", "winkerberos (>=0.5.0)"]
|
||||
ocsp = ["certifi", "cryptography (>=2.5)", "pyopenssl (>=17.2.0)", "requests (<3.0.0)", "service-identity (>=18.1.0)"]
|
||||
snappy = ["python-snappy"]
|
||||
test = ["pytest (>=7)"]
|
||||
zstd = ["zstandard"]
|
||||
|
||||
[[package]]
|
||||
name = "pyreadline3"
|
||||
version = "3.4.1"
|
||||
@ -2623,8 +2739,11 @@ files = [
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
aiohttp = {version = ">=3.5", optional = true, markers = "extra == \"aiohttp\""}
|
||||
certifi = "*"
|
||||
fastapi = {version = ">=0.79.0", optional = true, markers = "extra == \"fastapi\""}
|
||||
loguru = {version = ">=0.5", optional = true, markers = "extra == \"loguru\""}
|
||||
pymongo = {version = ">=3.1", optional = true, markers = "extra == \"pymongo\""}
|
||||
urllib3 = ">=1.26.11"
|
||||
|
||||
[package.extras]
|
||||
@ -2927,75 +3046,6 @@ typing-extensions = ">=4.8.0"
|
||||
opt-einsum = ["opt-einsum (>=3.3)"]
|
||||
optree = ["optree (>=0.9.1)"]
|
||||
|
||||
[[package]]
|
||||
name = "torchaudio"
|
||||
version = "2.3.0"
|
||||
description = "An audio package for PyTorch"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "torchaudio-2.3.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:342108da83aa19a457c9a128b1206fadb603753b51cca022b9f585aac2f4754c"},
|
||||
{file = "torchaudio-2.3.0-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:73fedb2c631e01fa10feaac308540b836aefe758e55ca3ee026335e5d01e8e30"},
|
||||
{file = "torchaudio-2.3.0-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:e5bb50b7a4874ed97086c9e516dd90b103d954edcb5ed4b36f4fc22c4000a5a7"},
|
||||
{file = "torchaudio-2.3.0-cp310-cp310-win_amd64.whl", hash = "sha256:b4cc9cef5c98ed37e9405c4e0b0e6413bc101f3f49d45dc4f1d4e927757fe41e"},
|
||||
{file = "torchaudio-2.3.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:341ca3048ce6edcc731519b30187f0b13acb245c4efe16f925f69f9d533546e1"},
|
||||
{file = "torchaudio-2.3.0-cp311-cp311-manylinux1_x86_64.whl", hash = "sha256:8f2e0a28740bb0ee66369f92c811f33c0a47e6fcfc2de9cee89746472d713906"},
|
||||
{file = "torchaudio-2.3.0-cp311-cp311-manylinux2014_aarch64.whl", hash = "sha256:61edb02ae9c0efea4399f9c1f899601136b24f35d430548284ea8eaf6ccbe3be"},
|
||||
{file = "torchaudio-2.3.0-cp311-cp311-win_amd64.whl", hash = "sha256:04bc960cf1aef3b469b095a432a25496bc28197850fc2d90b7b52d6b5255487b"},
|
||||
{file = "torchaudio-2.3.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:535144a2fbba95fbb3b883224ffcf44788e4cecbabbe49c4a1ae3e7a74f71485"},
|
||||
{file = "torchaudio-2.3.0-cp312-cp312-manylinux1_x86_64.whl", hash = "sha256:fb3f52ed1d63b272c240d9bf051705312cb172212051b8a6a2f64d42e3cc1633"},
|
||||
{file = "torchaudio-2.3.0-cp312-cp312-manylinux2014_aarch64.whl", hash = "sha256:668a8b694e5522cff28cd5e02d01aa1b75ce940aa9fb40480892bdc623b1735d"},
|
||||
{file = "torchaudio-2.3.0-cp312-cp312-win_amd64.whl", hash = "sha256:6c1f538018b85d7766835d042e555de2f096f7a69bba6b16031bf42a914dd9e1"},
|
||||
{file = "torchaudio-2.3.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:7ba93265455dc363385e98c0cfcaeb586b7401af8a2c824811ee1466134a4f30"},
|
||||
{file = "torchaudio-2.3.0-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:21bb6d1b384fc8895133f01489133d575d4a715cd81734b89651fb0264bd8b80"},
|
||||
{file = "torchaudio-2.3.0-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:ed1866f508dc689c4f682d330b2ed4c83108d35865e4fb89431819364d8ad9ed"},
|
||||
{file = "torchaudio-2.3.0-cp38-cp38-win_amd64.whl", hash = "sha256:a3cbb230e2bb38ad1a1dd74aea242a154a9f76ab819d9c058b2c5074a9f5d7d2"},
|
||||
{file = "torchaudio-2.3.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:f4b933776f20a36af5ddc57968fcb3da34dd03881db8d6760f3e1176803b9cf8"},
|
||||
{file = "torchaudio-2.3.0-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:c5e63cc2dbf179088b6cdfd21ecdbb943aa003c780075aa440162f231ee72db2"},
|
||||
{file = "torchaudio-2.3.0-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:d243bb8a1ee263c2cdafb9feed1569c3742d8135731e8f7818de12f4e0c83e28"},
|
||||
{file = "torchaudio-2.3.0-cp39-cp39-win_amd64.whl", hash = "sha256:6cd6d45cf8a45c89953e35434d9a461feb418e51e760adafc606a903dcbb9bd5"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
torch = "2.3.0"
|
||||
|
||||
[[package]]
|
||||
name = "torchvision"
|
||||
version = "0.18.0"
|
||||
description = "image and video datasets and models for torch deep learning"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "torchvision-0.18.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:dd61628a3d189c6852a12dc5ed4cd2eece66d2d67f35a866cb16f1dcb06c8c62"},
|
||||
{file = "torchvision-0.18.0-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:493c45f9937dad37aa1b64b14da17c7a589c72b91adc4837d431009cfe29bd53"},
|
||||
{file = "torchvision-0.18.0-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:5337f6acfa1fe959d5cb340d01a00614d6b31ce7a4824ccb95435a85c5273b95"},
|
||||
{file = "torchvision-0.18.0-cp310-cp310-win_amd64.whl", hash = "sha256:bd8e6f3b5beb49965f15c461302488edfa3d8c2d01d3bb79b150d6fb62711e3a"},
|
||||
{file = "torchvision-0.18.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:6896a52168befe1105fb3c9335287390ed227e71d1e4ec4d68b62e8a3099fc09"},
|
||||
{file = "torchvision-0.18.0-cp311-cp311-manylinux1_x86_64.whl", hash = "sha256:3d7955398d4ceaad77c487c2c44f6f7813112402c9bab8cd906d346005891048"},
|
||||
{file = "torchvision-0.18.0-cp311-cp311-manylinux2014_aarch64.whl", hash = "sha256:e5a24d620cea14a4bb89f24aa2b506230c0a16a3ada57fc53ad80cfd256a2128"},
|
||||
{file = "torchvision-0.18.0-cp311-cp311-win_amd64.whl", hash = "sha256:6ad70ddfa879bda5ed886b2518fe562640e0059787cbd65cb2bffa7674541410"},
|
||||
{file = "torchvision-0.18.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:eb9d83c0e1dbb54ecb0fb04c87f786333e3a6fb8b9c400aca7c31081f9aa5707"},
|
||||
{file = "torchvision-0.18.0-cp312-cp312-manylinux1_x86_64.whl", hash = "sha256:b657d052d146f24cb3b2a78219bfc82ae70a9706671c50f632528907d10cccec"},
|
||||
{file = "torchvision-0.18.0-cp312-cp312-manylinux2014_aarch64.whl", hash = "sha256:a964afbc7ddf50a46b941477f6c35729b416deedd139756befd488245e2e226d"},
|
||||
{file = "torchvision-0.18.0-cp312-cp312-win_amd64.whl", hash = "sha256:7c770f0f748e0b17f57c0297508d7254f686cdf03fc2e2949f422b20574f4c0f"},
|
||||
{file = "torchvision-0.18.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:2115a1906c015f5da9ceedc40a983313b0fd6e2c8a17108a92991706f51f6987"},
|
||||
{file = "torchvision-0.18.0-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:6323f7e5423ff2594d5891863b919deb9d0de95f01c36bf26fbd879036b6ed08"},
|
||||
{file = "torchvision-0.18.0-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:925d0a82cccf6f986c18b29b4392a942db65cbdb73c13a129c8493822eb9e36f"},
|
||||
{file = "torchvision-0.18.0-cp38-cp38-win_amd64.whl", hash = "sha256:95b42d0dc599b47a01530c7439a5751e67e45b85e3a67113989cf7c7c70f2039"},
|
||||
{file = "torchvision-0.18.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:75e22ecf44a13b8f95b8ad421c0261282d859c61816badaca1959e073ccdd691"},
|
||||
{file = "torchvision-0.18.0-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:4c334b3e719ba0a9ba6e15d4aff1178f5e6d029174f346163fed525f0ccfffd3"},
|
||||
{file = "torchvision-0.18.0-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:36efd87001c6bee2383e043e46a025affb03179747c8f4777b9918527ffce756"},
|
||||
{file = "torchvision-0.18.0-cp39-cp39-win_amd64.whl", hash = "sha256:ccc292e093771d5baacf5535ac4416306b6b5f15676341cd4d010d8542eace25"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
numpy = "*"
|
||||
pillow = ">=5.3.0,<8.3.dev0 || >=8.4.dev0"
|
||||
torch = "2.3.0"
|
||||
|
||||
[package.extras]
|
||||
scipy = ["scipy"]
|
||||
|
||||
[[package]]
|
||||
name = "tqdm"
|
||||
version = "4.66.4"
|
||||
@ -3684,4 +3734,4 @@ multidict = ">=4.0"
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = "^3.11"
|
||||
content-hash = "e42ed69102f662e3a6bd7b1949594f4277bea3460f38427b331ddbc439e455d7"
|
||||
content-hash = "45dc66654b5fa9ba530a5371278397b5a07449b0b1e23a84f736250411e06686"
|
||||
|
||||
@ -11,7 +11,7 @@ uvicorn = {extras = ["standard"], version = "^0.29.0"}
|
||||
pendulum = "^3.0.0"
|
||||
onnxruntime = "^1.18.0"
|
||||
aiohttp = "^3.9.5"
|
||||
sentry-sdk = {extras = ["loguru"], version = "^2.2.0"}
|
||||
sentry-sdk = {extras = ["pymongo", "fastapi", "loguru", "aiohttp"], version = "^2.2.0"}
|
||||
redis = {extras = ["hiredis"], version = "^5.0.4"}
|
||||
rocketry = "^2.5.1"
|
||||
fastapi = "^0.111.0"
|
||||
@ -22,12 +22,14 @@ korcen = "^0.3.8"
|
||||
opencv-python = "^4.9.0.80"
|
||||
psutil = "^5.9.8"
|
||||
transformers = "^4.41.0"
|
||||
torch = "^2.3.0"
|
||||
torchvision = "^0.18.0"
|
||||
torchaudio = "^2.3.0"
|
||||
loguru = "^0.7.2"
|
||||
slowapi = "^0.1.9"
|
||||
aiogoogletrans = "^3.3.3"
|
||||
motor = "^3.4.0"
|
||||
python-dotenv = "^1.0.1"
|
||||
better-profanity = "^0.7.0"
|
||||
joblib = "^1.4.2"
|
||||
torch = "^2.3.0"
|
||||
|
||||
|
||||
[tool.poetry.group.dev.dependencies]
|
||||
|
||||
Loading…
Reference in New Issue
Block a user