import json import os import traceback from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer from threading import Thread import extensions.openai.completions as OAIcompletions import extensions.openai.edits as OAIedits import extensions.openai.embeddings as OAIembeddings import extensions.openai.images as OAIimages import extensions.openai.models as OAImodels import extensions.openai.moderations as OAImoderations from extensions.openai.defaults import clamp, default, get_default_req_params from extensions.openai.errors import ( InvalidRequestError, OpenAIError, ServiceUnavailableError ) from extensions.openai.tokens import token_count, token_decode, token_encode from extensions.openai.utils import debug_msg from modules import shared import cgi import speech_recognition as sr from pydub import AudioSegment params = { 'port': int(os.environ.get('OPENEDAI_PORT')) if 'OPENEDAI_PORT' in os.environ else 5001, } class Handler(BaseHTTPRequestHandler): def send_access_control_headers(self): self.send_header("Access-Control-Allow-Origin", "*") self.send_header("Access-Control-Allow-Credentials", "true") self.send_header( "Access-Control-Allow-Methods", "GET,HEAD,OPTIONS,POST,PUT" ) self.send_header( "Access-Control-Allow-Headers", "Origin, Accept, X-Requested-With, Content-Type, " "Access-Control-Request-Method, Access-Control-Request-Headers, " "Authorization" ) def do_OPTIONS(self): self.send_response(200) self.send_access_control_headers() self.send_header('Content-Type', 'application/json') self.end_headers() self.wfile.write("OK".encode('utf-8')) def start_sse(self): self.send_response(200) self.send_access_control_headers() self.send_header('Content-Type', 'text/event-stream') self.send_header('Cache-Control', 'no-cache') # self.send_header('Connection', 'keep-alive') self.end_headers() def send_sse(self, chunk: dict): response = 'data: ' + json.dumps(chunk) + '\r\n\r\n' debug_msg(response[:-4]) self.wfile.write(response.encode('utf-8')) def end_sse(self): response = 'data: [DONE]\r\n\r\n' debug_msg(response[:-4]) self.wfile.write(response.encode('utf-8')) def return_json(self, ret: dict, code: int = 200, no_debug=False): self.send_response(code) self.send_access_control_headers() self.send_header('Content-Type', 'application/json') response = json.dumps(ret) r_utf8 = response.encode('utf-8') self.send_header('Content-Length', str(len(r_utf8))) self.end_headers() self.wfile.write(r_utf8) if not no_debug: debug_msg(r_utf8) def openai_error(self, message, code=500, error_type='APIError', param='', internal_message=''): error_resp = { 'error': { 'message': message, 'code': code, 'type': error_type, 'param': param, } } if internal_message: print(error_type, message) print(internal_message) # error_resp['internal_message'] = internal_message self.return_json(error_resp, code) def openai_error_handler(func): def wrapper(self): try: func(self) except InvalidRequestError as e: self.openai_error(e.message, e.code, e.__class__.__name__, e.param, internal_message=e.internal_message) except OpenAIError as e: self.openai_error(e.message, e.code, e.__class__.__name__, internal_message=e.internal_message) except Exception as e: self.openai_error(repr(e), 500, 'OpenAIError', internal_message=traceback.format_exc()) return wrapper @openai_error_handler def do_GET(self): debug_msg(self.requestline) debug_msg(self.headers) if self.path.startswith('/v1/engines') or self.path.startswith('/v1/models'): is_legacy = 'engines' in self.path is_list = self.path in ['/v1/engines', '/v1/models'] if is_legacy and not is_list: model_name = self.path[self.path.find('/v1/engines/') + len('/v1/engines/'):] resp = OAImodels.load_model(model_name) elif is_list: resp = OAImodels.list_models(is_legacy) else: model_name = self.path[len('/v1/models/'):] resp = OAImodels.model_info(model_name) self.return_json(resp) elif '/billing/usage' in self.path: # Ex. /v1/dashboard/billing/usage?start_date=2023-05-01&end_date=2023-05-31 self.return_json({"total_usage": 0}, no_debug=True) else: self.send_error(404) @openai_error_handler def do_POST(self): if '/v1/audio/transcriptions' in self.path: r = sr.Recognizer() # Parse the form data form = cgi.FieldStorage( fp=self.rfile, headers=self.headers, environ={'REQUEST_METHOD': 'POST', 'CONTENT_TYPE': self.headers['Content-Type']} ) audio_file = form['file'].file audio_data = AudioSegment.from_file(audio_file) # Convert AudioSegment to raw data raw_data = audio_data.raw_data # Create AudioData object audio_data = sr.AudioData(raw_data, audio_data.frame_rate, audio_data.sample_width) whipser_language = form.getvalue('language', None) whipser_model = form.getvalue('model', 'tiny') # Use the model from the form data if it exists, otherwise default to tiny transcription = {"text": ""} try: transcription["text"] = r.recognize_whisper(audio_data, language=whipser_language, model=whipser_model) except sr.UnknownValueError: print("Whisper could not understand audio") transcription["text"] = "Whisper could not understand audio UnknownValueError" except sr.RequestError as e: print("Could not request results from Whisper", e) transcription["text"] = "Whisper could not understand audio RequestError" self.return_json(transcription, no_debug=True) return debug_msg(self.requestline) debug_msg(self.headers) content_length = self.headers.get('Content-Length') transfer_encoding = self.headers.get('Transfer-Encoding') if content_length: body = json.loads(self.rfile.read(int(content_length)).decode('utf-8')) elif transfer_encoding == 'chunked': chunks = [] while True: chunk_size = int(self.rfile.readline(), 16) # Read the chunk size if chunk_size == 0: break # End of chunks chunks.append(self.rfile.read(chunk_size)) self.rfile.readline() # Consume the trailing newline after each chunk body = json.loads(b''.join(chunks).decode('utf-8')) else: self.send_response(400, "Bad Request: Either Content-Length or Transfer-Encoding header expected.") self.end_headers() return debug_msg(body) if '/completions' in self.path or '/generate' in self.path: if not shared.model: raise ServiceUnavailableError("No model loaded.") is_legacy = '/generate' in self.path is_streaming = body.get('stream', False) if is_streaming: self.start_sse() response = [] if 'chat' in self.path: response = OAIcompletions.stream_chat_completions(body, is_legacy=is_legacy) else: response = OAIcompletions.stream_completions(body, is_legacy=is_legacy) for resp in response: self.send_sse(resp) self.end_sse() else: response = '' if 'chat' in self.path: response = OAIcompletions.chat_completions(body, is_legacy=is_legacy) else: response = OAIcompletions.completions(body, is_legacy=is_legacy) self.return_json(response) elif '/edits' in self.path: # deprecated if not shared.model: raise ServiceUnavailableError("No model loaded.") req_params = get_default_req_params() instruction = body['instruction'] input = body.get('input', '') temperature = clamp(default(body, 'temperature', req_params['temperature']), 0.001, 1.999) # fixup absolute 0.0 top_p = clamp(default(body, 'top_p', req_params['top_p']), 0.001, 1.0) response = OAIedits.edits(instruction, input, temperature, top_p) self.return_json(response) elif '/images/generations' in self.path: if 'SD_WEBUI_URL' not in os.environ: raise ServiceUnavailableError("Stable Diffusion not available. SD_WEBUI_URL not set.") prompt = body['prompt'] size = default(body, 'size', '1024x1024') response_format = default(body, 'response_format', 'url') # or b64_json n = default(body, 'n', 1) # ignore the batch limits of max 10 response = OAIimages.generations(prompt=prompt, size=size, response_format=response_format, n=n) self.return_json(response, no_debug=True) elif '/embeddings' in self.path: encoding_format = body.get('encoding_format', '') input = body.get('input', body.get('text', '')) if not input: raise InvalidRequestError("Missing required argument input", params='input') if type(input) is str: input = [input] response = OAIembeddings.embeddings(input, encoding_format) self.return_json(response, no_debug=True) elif '/moderations' in self.path: input = body['input'] if not input: raise InvalidRequestError("Missing required argument input", params='input') response = OAImoderations.moderations(input) self.return_json(response, no_debug=True) elif self.path == '/api/v1/token-count': # NOT STANDARD. lifted from the api extension, but it's still very useful to calculate tokenized length client side. response = token_count(body['prompt']) self.return_json(response, no_debug=True) elif self.path == '/api/v1/token/encode': # NOT STANDARD. needed to support logit_bias, logprobs and token arrays for native models encoding_format = body.get('encoding_format', '') response = token_encode(body['input'], encoding_format) self.return_json(response, no_debug=True) elif self.path == '/api/v1/token/decode': # NOT STANDARD. needed to support logit_bias, logprobs and token arrays for native models encoding_format = body.get('encoding_format', '') response = token_decode(body['input'], encoding_format) self.return_json(response, no_debug=True) else: self.send_error(404) def run_server(): server_addr = ('0.0.0.0' if shared.args.listen else '127.0.0.1', params['port']) server = ThreadingHTTPServer(server_addr, Handler) if shared.args.share: try: from flask_cloudflared import _run_cloudflared public_url = _run_cloudflared(params['port'], params['port'] + 1) print(f'OpenAI compatible API ready at: OPENAI_API_BASE={public_url}/v1') except ImportError: print('You should install flask_cloudflared manually') else: print(f'OpenAI compatible API ready at: OPENAI_API_BASE=http://{server_addr[0]}:{server_addr[1]}/v1') server.serve_forever() def setup(): Thread(target=run_server, daemon=True).start()