mirror of https://github.com/xtekky/gpt4free.git
Update __init__.py
This commit is contained in:
ThatLukinhasGuy
GitHub
parent
318112c8b9
commit
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from fastapi import FastAPI, Response, Request
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from fastapi import FastAPI, Response, Request
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from fastapi.middleware.cors import CORSMiddleware
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from typing import List, Union, Any, Dict, AnyStr
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from typing import List, Union, Any, Dict, AnyStr
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from ._tokenizer import tokenize
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from ._tokenizer import tokenize
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from .. import BaseProvider
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import g4f
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import time
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import time
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import json
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import json
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import random
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import random
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import string
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import string
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import uvicorn
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import uvicorn
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import nest_asyncio
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import nest_asyncio
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import g4f
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app = FastAPI()
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class Api:
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nest_asyncio.apply()
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def __init__(self, engine: g4f, debug: bool = True, sentry: bool = False,
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list_ignored_providers: List[Union[str, BaseProvider]] = None) -> None:
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self.engine = engine
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self.debug = debug
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self.sentry = sentry
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self.list_ignored_providers = list_ignored_providers
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origins = [
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self.app = FastAPI()
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"http://localhost",
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nest_asyncio.apply()
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"http://localhost:1337",
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]
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app.add_middleware(
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JSONObject = Dict[AnyStr, Any]
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CORSMiddleware,
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JSONArray = List[Any]
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allow_origins=origins,
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JSONStructure = Union[JSONArray, JSONObject]
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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JSONObject = Dict[AnyStr, Any]
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@self.app.get("/")
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JSONArray = List[Any]
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async def read_root():
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JSONStructure = Union[JSONArray, JSONObject]
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return Response(content=json.dumps({"info": "g4f API"}, indent=4), media_type="application/json")
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@app.get("/")
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@self.app.get("/v1")
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async def read_root():
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async def read_root_v1():
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return Response(content=json.dumps({"info": "G4F API"}, indent=4), media_type="application/json")
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return Response(content=json.dumps({"info": "Go to /v1/chat/completions or /v1/models."}, indent=4), media_type="application/json")
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@app.get("/v1")
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@self.app.get("/v1/models")
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async def read_root_v1():
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async def models():
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return Response(content=json.dumps({"info": "Go to /v1/chat/completions or /v1/models."}, indent=4), media_type="application/json")
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model_list = [{
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'id': model,
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'object': 'model',
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'created': 0,
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'owned_by': 'g4f'} for model in g4f.Model.__all__()]
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@app.get("/v1/models")
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return Response(content=json.dumps({
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async def models():
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'object': 'list',
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model_list = [{
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'data': model_list}, indent=4), media_type="application/json")
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'id': model,
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'object': 'model',
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'created': 0,
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'owned_by': 'g4f'} for model in g4f.Model.__all__()]
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return Response(content=json.dumps({
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@self.app.get("/v1/models/{model_name}")
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'object': 'list',
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async def model_info(model_name: str):
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'data': model_list}, indent=4), media_type="application/json")
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try:
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model_info = (g4f.ModelUtils.convert[model_name])
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@app.get("/v1/models/{model_name}")
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return Response(content=json.dumps({
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async def model_info(model_name: str):
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'id': model_name,
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try:
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'object': 'model',
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model_info = (g4f.ModelUtils.convert[model_name])
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'created': 0,
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'owned_by': model_info.base_provider
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}, indent=4), media_type="application/json")
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except:
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return Response(content=json.dumps({"error": "The model does not exist."}, indent=4), media_type="application/json")
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return Response(content=json.dumps({
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@self.app.post("/v1/chat/completions")
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'id': model_name,
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async def chat_completions(request: Request, item: JSONStructure = None):
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'object': 'model',
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item_data = {
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'created': 0,
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'model': 'gpt-3.5-turbo',
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'owned_by': model_info.base_provider
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'stream': False,
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}, indent=4), media_type="application/json")
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}
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except:
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return Response(content=json.dumps({"error": "The model does not exist."}, indent=4), media_type="application/json")
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@app.post("/v1/chat/completions")
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item_data.update(item or {})
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async def chat_completions(request: Request, item: JSONStructure = None):
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model = item_data.get('model')
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stream = item_data.get('stream')
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messages = item_data.get('messages')
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item_data = {
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try:
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'model': 'gpt-3.5-turbo',
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response = g4f.ChatCompletion.create(model=model, stream=stream, messages=messages)
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'stream': False,
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except:
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}
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return Response(content=json.dumps({"error": "An error occurred while generating the response."}, indent=4), media_type="application/json")
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item_data.update(item or {})
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model = item_data.get('model')
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stream = item_data.get('stream')
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messages = item_data.get('messages')
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try:
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completion_id = ''.join(random.choices(string.ascii_letters + string.digits, k=28))
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response = g4f.ChatCompletion.create(model=model, stream=stream, messages=messages)
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completion_timestamp = int(time.time())
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except:
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return Response(content=json.dumps({"error": "An error occurred while generating the response."}, indent=4), media_type="application/json")
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completion_id = ''.join(random.choices(string.ascii_letters + string.digits, k=28))
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if not stream:
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completion_timestamp = int(time.time())
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prompt_tokens, _ = tokenize(''.join([message['content'] for message in messages]))
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completion_tokens, _ = tokenize(response)
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if not stream:
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json_data = {
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prompt_tokens, _ = tokenize(''.join([message['content'] for message in messages]))
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completion_tokens, _ = tokenize(response)
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json_data = {
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'id': f'chatcmpl-{completion_id}',
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'object': 'chat.completion',
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'created': completion_timestamp,
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'model': model,
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'choices': [
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{
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'index': 0,
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'message': {
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'role': 'assistant',
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'content': response,
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},
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'finish_reason': 'stop',
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}
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],
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'usage': {
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'prompt_tokens': prompt_tokens,
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'completion_tokens': completion_tokens,
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'total_tokens': prompt_tokens + completion_tokens,
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},
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}
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return Response(content=json.dumps(json_data, indent=4), media_type="application/json")
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def streaming():
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try:
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for chunk in response:
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completion_data = {
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'id': f'chatcmpl-{completion_id}',
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'id': f'chatcmpl-{completion_id}',
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'object': 'chat.completion.chunk',
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'object': 'chat.completion',
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'created': completion_timestamp,
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'created': completion_timestamp,
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'model': model,
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'model': model,
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'choices': [
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'choices': [
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{
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{
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'index': 0,
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'index': 0,
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'delta': {
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'message': {
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'content': chunk,
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'role': 'assistant',
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'content': response,
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},
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},
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'finish_reason': None,
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'finish_reason': 'stop',
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}
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}
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],
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],
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'usage': {
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'prompt_tokens': prompt_tokens,
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'completion_tokens': completion_tokens,
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'total_tokens': prompt_tokens + completion_tokens,
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},
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}
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}
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content = json.dumps(completion_data, separators=(',', ':'))
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return Response(content=json.dumps(json_data, indent=4), media_type="application/json")
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yield f'data: {content}\n\n'
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time.sleep(0.03)
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end_completion_data = {
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def streaming():
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'id': f'chatcmpl-{completion_id}',
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try:
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'object': 'chat.completion.chunk',
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for chunk in response:
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'created': completion_timestamp,
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completion_data = {
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'model': model,
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'id': f'chatcmpl-{completion_id}',
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'choices': [
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'object': 'chat.completion.chunk',
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{
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'created': completion_timestamp,
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'index': 0,
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'model': model,
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'delta': {},
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'choices': [
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'finish_reason': 'stop',
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{
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'index': 0,
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'delta': {
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'content': chunk,
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},
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'finish_reason': None,
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}
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],
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}
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content = json.dumps(completion_data, separators=(',', ':'))
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yield f'data: {content}\n\n'
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time.sleep(0.03)
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end_completion_data = {
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'id': f'chatcmpl-{completion_id}',
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'object': 'chat.completion.chunk',
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'created': completion_timestamp,
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'model': model,
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'choices': [
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{
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'index': 0,
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'delta': {},
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'finish_reason': 'stop',
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}
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],
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}
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}
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],
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}
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content = json.dumps(end_completion_data, separators=(',', ':'))
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content = json.dumps(end_completion_data, separators=(',', ':'))
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yield f'data: {content}\n\n'
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yield f'data: {content}\n\n'
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except GeneratorExit:
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except GeneratorExit:
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pass
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pass
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return Response(content=json.dumps(streaming(), indent=4), media_type="application/json")
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return Response(content=json.dumps(streaming(), indent=4), media_type="application/json")
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@app.post("/v1/completions")
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@self.app.post("/v1/completions")
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async def completions():
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async def completions():
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return Response(content=json.dumps({'info': 'Not working yet.'}, indent=4), media_type="application/json")
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return Response(content=json.dumps({'info': 'Not working yet.'}, indent=4), media_type="application/json")
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def run(ip, thread_quantity):
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def run(self, ip, thread_quantity):
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split_ip = ip.split(":")
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split_ip = ip.split(":")
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uvicorn.run(app, host=split_ip[0], port=int(split_ip[1]), use_colors=False, workers=thread_quantity)
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uvicorn.run(self.app, host=split_ip[0], port=int(split_ip[1]), use_colors=False, workers=thread_quantity)
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