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