gpt4free

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import ast
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import logging
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import time
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import json
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import random
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import string
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import uvicorn
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import nest_asyncio
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from fastapi           import FastAPI, Response, Request
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from fastapi.responses import StreamingResponse
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from typing            import List, Union, Any, Dict, AnyStr
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#from ._tokenizer       import tokenize
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import g4f
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from .. import debug
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debug.logging = True
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class Api:
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    def __init__(self, engine: g4f, debug: bool = True, sentry: bool = False,
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                 list_ignored_providers: List[str] = 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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        self.app = FastAPI()
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        nest_asyncio.apply()
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        JSONObject = Dict[AnyStr, Any]
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        JSONArray = List[Any]
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        JSONStructure = Union[JSONArray, JSONObject]
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        @self.app.get("/")
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        async def read_root():
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            return Response(content=json.dumps({"info": "g4f API"}, indent=4), media_type="application/json")
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        @self.app.get("/v1")
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        async def read_root_v1():
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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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        @self.app.get("/v1/models")
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        async def models():
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            model_list = []
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            for model in g4f.Model.__all__():
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                model_info = (g4f.ModelUtils.convert[model])
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                model_list.append({
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                'id': model,
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                'object': 'model',
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                'created': 0,
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                'owned_by': model_info.base_provider}
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                )
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            return Response(content=json.dumps({
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                'object': 'list',
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                'data': model_list}, indent=4), media_type="application/json")
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        @self.app.get("/v1/models/{model_name}")
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        async def model_info(model_name: str):
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            try:
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                model_info = (g4f.ModelUtils.convert[model_name])
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                return Response(content=json.dumps({
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                    'id': model_name,
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                    'object': 'model',
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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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        @self.app.post("/v1/chat/completions")
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        async def chat_completions(request: Request, item: JSONStructure = None):
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            item_data = {
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                'model': 'gpt-3.5-turbo',
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                'stream': False,
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            }
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            # item contains byte keys, and dict.get suppresses error
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            item_data.update({
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                key.decode('utf-8') if isinstance(key, bytes) else key: str(value)
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                for key, value in (item or {}).items()
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            })
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            # messages is str, need dict
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            if isinstance(item_data.get('messages'), str):
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                item_data['messages'] = ast.literal_eval(item_data.get('messages'))
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            model = item_data.get('model')
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            stream = True if item_data.get("stream") == "True" else False
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            messages = item_data.get('messages')
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            provider = item_data.get('provider', '').replace('g4f.Provider.', '')
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            provider = provider if provider and provider != "Auto" else None
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            try:
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                response = g4f.ChatCompletion.create(
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                    model=model,
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                    stream=stream,
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                    messages=messages,
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                    provider = provider,
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                    ignored=self.list_ignored_providers
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                )
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            except Exception as e:
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                logging.exception(e)
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                content = json.dumps({
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                    "error": {"message": f"An error occurred while generating the response:\n{e}"},
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                    "model": model,
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                    "provider": g4f.get_last_provider(True)
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                })
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                return Response(content=content, status_code=500, media_type="application/json")
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            completion_id = ''.join(random.choices(string.ascii_letters + string.digits, k=28))
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            completion_timestamp = int(time.time())
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            if not stream:
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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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                    'provider': g4f.get_last_provider(True),
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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': 0, #prompt_tokens,
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                        'completion_tokens': 0, #completion_tokens,
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                        'total_tokens': 0, #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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                            'object': 'chat.completion.chunk',
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                            'created': completion_timestamp,
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                            'model': model,
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                            'provider': g4f.get_last_provider(True),
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                            'choices': [
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                                {
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                                    'index': 0,
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                                    'delta': {
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                                        'role': 'assistant',
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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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                        yield f'data: {json.dumps(completion_data)}\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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                        'provider': g4f.get_last_provider(True),
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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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                    yield f'data: {json.dumps(end_completion_data)}\n\n'
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                except GeneratorExit:
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                    pass
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                except Exception as e:
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                    logging.exception(e)
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                    content = json.dumps({
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                        "error": {"message": f"An error occurred while generating the response:\n{e}"},
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                        "model": model,
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                        "provider": g4f.get_last_provider(True),
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                    })
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                    yield f'data: {content}'
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            return StreamingResponse(streaming(), media_type="text/event-stream")
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        @self.app.post("/v1/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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    def run(self, ip):
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        split_ip = ip.split(":")
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        uvicorn.run(app=self.app, host=split_ip[0], port=int(split_ip[1]), use_colors=False)
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