import base64
import json
import os
import time
import requests
import yaml
import numpy as np
from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
from threading import Thread
from modules.utils import get_available_models
from modules.models import load_model, unload_model
from modules.models_settings import (get_model_settings_from_yamls,
update_model_parameters)
from modules import shared
from modules.text_generation import encode, generate_reply
params = {
'port': int(os.environ.get('OPENEDAI_PORT')) if 'OPENEDAI_PORT' in os.environ else 5001,
}
debug = True if 'OPENEDAI_DEBUG' in os.environ else False
# Slightly different defaults for OpenAI's API
# Data type is important, Ex. use 0.0 for a float 0
default_req_params = {
'max_new_tokens': 200,
'temperature': 1.0,
'top_p': 1.0,
'top_k': 1,
'repetition_penalty': 1.18,
'encoder_repetition_penalty': 1.0,
'suffix': None,
'stream': False,
'echo': False,
'seed': -1,
# 'n' : default(body, 'n', 1), # 'n' doesn't have a direct map
'truncation_length': 2048,
'add_bos_token': True,
'do_sample': True,
'typical_p': 1.0,
'epsilon_cutoff': 0.0, # In units of 1e-4
'eta_cutoff': 0.0, # In units of 1e-4
'tfs': 1.0,
'top_a': 0.0,
'min_length': 0,
'no_repeat_ngram_size': 0,
'num_beams': 1,
'penalty_alpha': 0.0,
'length_penalty': 1.0,
'early_stopping': False,
'mirostat_mode': 0,
'mirostat_tau': 5.0,
'mirostat_eta': 0.1,
'ban_eos_token': False,
'skip_special_tokens': True,
'custom_stopping_strings': '',
}
# Optional, install the module and download the model to enable
# v1/embeddings
try:
from sentence_transformers import SentenceTransformer
except ImportError:
pass
st_model = os.environ["OPENEDAI_EMBEDDING_MODEL"] if "OPENEDAI_EMBEDDING_MODEL" in os.environ else "all-mpnet-base-v2"
embedding_model = None
# little helper to get defaults if arg is present but None and should be the same type as default.
def default(dic, key, default):
val = dic.get(key, default)
if type(val) != type(default):
# maybe it's just something like 1 instead of 1.0
try:
v = type(default)(val)
if type(val)(v) == val: # if it's the same value passed in, it's ok.
return v
except:
pass
val = default
return val
def clamp(value, minvalue, maxvalue):
return max(minvalue, min(value, maxvalue))
def float_list_to_base64(float_list):
# Convert the list to a float32 array that the OpenAPI client expects
float_array = np.array(float_list, dtype="float32")
# Get raw bytes
bytes_array = float_array.tobytes()
# Encode bytes into base64
encoded_bytes = base64.b64encode(bytes_array)
# Turn raw base64 encoded bytes into ASCII
ascii_string = encoded_bytes.decode('ascii')
return ascii_string
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 openai_error(self, message, code = 500, error_type = 'APIError', param = '', internal_message = ''):
self.send_response(code)
self.send_access_control_headers()
self.send_header('Content-Type', 'application/json')
self.end_headers()
error_resp = {
'error': {
'message': message,
'code': code,
'type': error_type,
'param': param,
}
}
if internal_message:
error_resp['internal_message'] = internal_message
response = json.dumps(error_resp)
self.wfile.write(response.encode('utf-8'))
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 do_GET(self):
if self.path.startswith('/v1/engines') or self.path.startswith('/v1/models'):
current_model_list = [ shared.model_name ] # The real chat/completions model, maybe "None"
embeddings_model_list = [ st_model ] if embedding_model else [] # The real sentence transformer embeddings model
pseudo_model_list = [ # these are expected by so much, so include some here as a dummy
'gpt-3.5-turbo', # /v1/chat/completions
'text-curie-001', # /v1/completions, 2k context
'text-davinci-002' # /v1/embeddings text-embedding-ada-002:1536, text-davinci-002:768
]
is_legacy = 'engines' in self.path
is_list = self.path in ['/v1/engines', '/v1/models']
resp = ''
if is_legacy and not is_list: # load model
model_name = self.path[self.path.find('/v1/engines/') + len('/v1/engines/'):]
resp = {
"id": model_name,
"object": "engine",
"owner": "self",
"ready": True,
}
if model_name not in pseudo_model_list + embeddings_model_list + current_model_list: # Real model only
# No args. Maybe it works anyways!
# TODO: hack some heuristics into args for better results
shared.model_name = model_name
unload_model()
model_settings = get_model_settings_from_yamls(shared.model_name)
shared.settings.update(model_settings)
update_model_parameters(model_settings, initial=True)
if shared.settings['mode'] != 'instruct':
shared.settings['instruction_template'] = None
shared.model, shared.tokenizer = load_model(shared.model_name)
if not shared.model: # load failed.
shared.model_name = "None"
resp['id'] = "None"
resp['ready'] = False
elif is_list:
# TODO: Lora's?
available_model_list = get_available_models()
all_model_list = current_model_list + embeddings_model_list + pseudo_model_list + available_model_list
models = {}
if is_legacy:
models = [{ "id": id, "object": "engine", "owner": "user", "ready": True } for id in all_model_list ]
if not shared.model:
models[0]['ready'] = False
else:
models = [{ "id": id, "object": "model", "owned_by": "user", "permission": [] } for id in all_model_list ]
resp = {
"object": "list",
"data": models,
}
else:
the_model_name = self.path[len('/v1/models/'):]
resp = {
"id": the_model_name,
"object": "model",
"owned_by": "user",
"permission": []
}
self.send_response(200)
self.send_access_control_headers()
self.send_header('Content-Type', 'application/json')
self.end_headers()
response = json.dumps(resp)
self.wfile.write(response.encode('utf-8'))
elif '/billing/usage' in self.path:
# Ex. /v1/dashboard/billing/usage?start_date=2023-05-01&end_date=2023-05-31
self.send_response(200)
self.send_access_control_headers()
self.send_header('Content-Type', 'application/json')
self.end_headers()
response = json.dumps({
"total_usage": 0,
})
self.wfile.write(response.encode('utf-8'))
else:
self.send_error(404)
def do_POST(self):
if debug:
print(self.headers) # did you know... python-openai sends your linux kernel & python version?
content_length = int(self.headers['Content-Length'])
body = json.loads(self.rfile.read(content_length).decode('utf-8'))
if debug:
print(body)
if '/completions' in self.path or '/generate' in self.path:
if not shared.model:
self.openai_error("No model loaded.")
return
is_legacy = '/generate' in self.path
is_chat_request = 'chat' in self.path
resp_list = 'data' if is_legacy else 'choices'
# XXX model is ignored for now
# model = body.get('model', shared.model_name) # ignored, use existing for now
model = shared.model_name
created_time = int(time.time())
cmpl_id = "chatcmpl-%d" % (created_time) if is_chat_request else "conv-%d" % (created_time)
# Request Parameters
# Try to use openai defaults or map them to something with the same intent
req_params = default_req_params.copy()
stopping_strings = []
if 'stop' in body:
if isinstance(body['stop'], str):
stopping_strings.extend([body['stop']])
elif isinstance(body['stop'], list):
stopping_strings.extend(body['stop'])
truncation_length = default(shared.settings, 'truncation_length', 2048)
truncation_length = clamp(default(body, 'truncation_length', truncation_length), 1, truncation_length)
default_max_tokens = truncation_length if is_chat_request else 16 # completions default, chat default is 'inf' so we need to cap it.
max_tokens_str = 'length' if is_legacy else 'max_tokens'
max_tokens = default(body, max_tokens_str, default(shared.settings, 'max_new_tokens', default_max_tokens))
# if the user assumes OpenAI, the max_tokens is way too large - try to ignore it unless it's small enough
req_params['max_new_tokens'] = max_tokens
req_params['truncation_length'] = truncation_length
req_params['temperature'] = clamp(default(body, 'temperature', default_req_params['temperature']), 0.001, 1.999) # fixup absolute 0.0
req_params['top_p'] = clamp(default(body, 'top_p', default_req_params['top_p']), 0.001, 1.0)
req_params['top_k'] = default(body, 'best_of', default_req_params['top_k'])
req_params['suffix'] = default(body, 'suffix', default_req_params['suffix'])
req_params['stream'] = default(body, 'stream', default_req_params['stream'])
req_params['echo'] = default(body, 'echo', default_req_params['echo'])
req_params['seed'] = shared.settings.get('seed', default_req_params['seed'])
req_params['add_bos_token'] = shared.settings.get('add_bos_token', default_req_params['add_bos_token'])
is_streaming = req_params['stream']
self.send_response(200)
self.send_access_control_headers()
if is_streaming:
self.send_header('Content-Type', 'text/event-stream')
self.send_header('Cache-Control', 'no-cache')
# self.send_header('Connection', 'keep-alive')
else:
self.send_header('Content-Type', 'application/json')
self.end_headers()
token_count = 0
completion_token_count = 0
prompt = ''
stream_object_type = ''
object_type = ''
if is_chat_request:
# Chat Completions
stream_object_type = 'chat.completions.chunk'
object_type = 'chat.completions'
messages = body['messages']
role_formats = {
'user': 'user: {message}\n',
'assistant': 'assistant: {message}\n',
'system': '{message}',
'context': 'You are a helpful assistant. Answer as concisely as possible.',
'prompt': 'assistant:',
}
# Instruct models can be much better
if shared.settings['instruction_template']:
try:
instruct = yaml.safe_load(open(f"characters/instruction-following/{shared.settings['instruction_template']}.yaml", 'r'))
template = instruct['turn_template']
system_message_template = "{message}"
system_message_default = instruct['context']
bot_start = template.find('<|bot|>') # So far, 100% of instruction templates have this token
user_message_template = template[:bot_start].replace('<|user-message|>', '{message}').replace('<|user|>', instruct['user'])
bot_message_template = template[bot_start:].replace('<|bot-message|>', '{message}').replace('<|bot|>', instruct['bot'])
bot_prompt = bot_message_template[:bot_message_template.find('{message}')].rstrip(' ')
role_formats = {
'user': user_message_template,
'assistant': bot_message_template,
'system': system_message_template,
'context': system_message_default,
'prompt': bot_prompt,
}
if 'Alpaca' in shared.settings['instruction_template']:
stopping_strings.extend(['\n###'])
elif instruct['user']: # WizardLM and some others have no user prompt.
stopping_strings.extend(['\n' + instruct['user'], instruct['user']])
if debug:
print(f"Loaded instruction role format: {shared.settings['instruction_template']}")
except Exception as e:
stopping_strings.extend(['\nuser:'])
print(f"Exception: When loading characters/instruction-following/{shared.settings['instruction_template']}.yaml: {repr(e)}")
print("Warning: Loaded default instruction-following template for model.")
else:
stopping_strings.extend(['\nuser:'])
print("Warning: Loaded default instruction-following template for model.")
system_msgs = []
chat_msgs = []
# You are ChatGPT, a large language model trained by OpenAI. Answer as concisely as possible. Knowledge cutoff: {knowledge_cutoff} Current date: {current_date}
context_msg = role_formats['system'].format(message=role_formats['context']) if role_formats['context'] else ''
if context_msg:
system_msgs.extend([context_msg])
# Maybe they sent both? This is not documented in the API, but some clients seem to do this.
if 'prompt' in body:
prompt_msg = role_formats['system'].format(message=body['prompt'])
system_msgs.extend([prompt_msg])
for m in messages:
role = m['role']
content = m['content']
msg = role_formats[role].format(message=content)
if role == 'system':
system_msgs.extend([msg])
else:
chat_msgs.extend([msg])
# can't really truncate the system messages
system_msg = '\n'.join(system_msgs)
if system_msg and system_msg[-1] != '\n':
system_msg = system_msg + '\n'
system_token_count = len(encode(system_msg)[0])
remaining_tokens = truncation_length - system_token_count
chat_msg = ''
while chat_msgs:
new_msg = chat_msgs.pop()
new_size = len(encode(new_msg)[0])
if new_size <= remaining_tokens:
chat_msg = new_msg + chat_msg
remaining_tokens -= new_size
else:
print(f"Warning: too many messages for context size, dropping {len(chat_msgs) + 1} oldest message(s).")
break
prompt = system_msg + chat_msg + role_formats['prompt']
token_count = len(encode(prompt)[0])
else:
# Text Completions
stream_object_type = 'text_completion.chunk'
object_type = 'text_completion'
# ... encoded as a string, array of strings, array of tokens, or array of token arrays.
if is_legacy:
prompt = body['context'] # Older engines.generate API
else:
prompt = body['prompt'] # XXX this can be different types
if isinstance(prompt, list):
self.openai_error("API Batched generation not yet supported.")
return
token_count = len(encode(prompt)[0])
if token_count >= truncation_length:
new_len = int(len(prompt) * shared.settings['truncation_length'] / token_count)
prompt = prompt[-new_len:]
new_token_count = len(encode(prompt)[0])
print(f"Warning: truncating prompt to {new_len} characters, was {token_count} tokens. Now: {new_token_count} tokens.")
token_count = new_token_count
if truncation_length - token_count < req_params['max_new_tokens']:
print(f"Warning: Ignoring max_new_tokens ({req_params['max_new_tokens']}), too large for the remaining context. Remaining tokens: {truncation_length - token_count}")
req_params['max_new_tokens'] = truncation_length - token_count
print(f"Warning: Set max_new_tokens = {req_params['max_new_tokens']}")
if is_streaming:
# begin streaming
chunk = {
"id": cmpl_id,
"object": stream_object_type,
"created": created_time,
"model": shared.model_name,
resp_list: [{
"index": 0,
"finish_reason": None,
}],
}
if stream_object_type == 'text_completion.chunk':
chunk[resp_list][0]["text"] = ""
else:
# So yeah... do both methods? delta and messages.
chunk[resp_list][0]["message"] = {'role': 'assistant', 'content': ''}
chunk[resp_list][0]["delta"] = {'role': 'assistant', 'content': ''}
response = 'data: ' + json.dumps(chunk) + '\r\n\r\n'
self.wfile.write(response.encode('utf-8'))
# generate reply #######################################
if debug:
print({'prompt': prompt, 'req_params': req_params})
generator = generate_reply(prompt, req_params, stopping_strings=stopping_strings, is_chat=False)
answer = ''
seen_content = ''
longest_stop_len = max([len(x) for x in stopping_strings] + [0])
for a in generator:
answer = a
stop_string_found = False
len_seen = len(seen_content)
search_start = max(len_seen - longest_stop_len, 0)
for string in stopping_strings:
idx = answer.find(string, search_start)
if idx != -1:
answer = answer[:idx] # clip it.
stop_string_found = True
if stop_string_found:
break
# If something like "\nYo" is generated just before "\nYou:"
# is completed, buffer and generate more, don't send it
buffer_and_continue = False
for string in stopping_strings:
for j in range(len(string) - 1, 0, -1):
if answer[-j:] == string[:j]:
buffer_and_continue = True
break
else:
continue
break
if buffer_and_continue:
continue
if is_streaming:
# Streaming
new_content = answer[len_seen:]
if not new_content or chr(0xfffd) in new_content: # partial unicode character, don't send it yet.
continue
seen_content = answer
chunk = {
"id": cmpl_id,
"object": stream_object_type,
"created": created_time,
"model": shared.model_name,
resp_list: [{
"index": 0,
"finish_reason": None,
}],
}
# strip extra leading space off new generated content
if len_seen == 0 and new_content[0] == ' ':
new_content = new_content[1:]
if stream_object_type == 'text_completion.chunk':
chunk[resp_list][0]['text'] = new_content
else:
# So yeah... do both methods? delta and messages.
chunk[resp_list][0]['message'] = {'content': new_content}
chunk[resp_list][0]['delta'] = {'content': new_content}
response = 'data: ' + json.dumps(chunk) + '\r\n\r\n'
self.wfile.write(response.encode('utf-8'))
completion_token_count += len(encode(new_content)[0])
if is_streaming:
chunk = {
"id": cmpl_id,
"object": stream_object_type,
"created": created_time,
"model": model, # TODO: add Lora info?
resp_list: [{
"index": 0,
"finish_reason": "stop",
}],
"usage": {
"prompt_tokens": token_count,
"completion_tokens": completion_token_count,
"total_tokens": token_count + completion_token_count
}
}
if stream_object_type == 'text_completion.chunk':
chunk[resp_list][0]['text'] = ''
else:
# So yeah... do both methods? delta and messages.
chunk[resp_list][0]['message'] = {'content': ''}
chunk[resp_list][0]['delta'] = {'content': ''}
response = 'data: ' + json.dumps(chunk) + '\r\n\r\ndata: [DONE]\r\n\r\n'
self.wfile.write(response.encode('utf-8'))
# Finished if streaming.
if debug:
if answer and answer[0] == ' ':
answer = answer[1:]
print({'answer': answer}, chunk)
return
# strip extra leading space off new generated content
if answer and answer[0] == ' ':
answer = answer[1:]
if debug:
print({'response': answer})
completion_token_count = len(encode(answer)[0])
stop_reason = "stop"
if token_count + completion_token_count >= truncation_length:
stop_reason = "length"
resp = {
"id": cmpl_id,
"object": object_type,
"created": created_time,
"model": model, # TODO: add Lora info?
resp_list: [{
"index": 0,
"finish_reason": stop_reason,
}],
"usage": {
"prompt_tokens": token_count,
"completion_tokens": completion_token_count,
"total_tokens": token_count + completion_token_count
}
}
if is_chat_request:
resp[resp_list][0]["message"] = {"role": "assistant", "content": answer}
else:
resp[resp_list][0]["text"] = answer
response = json.dumps(resp)
self.wfile.write(response.encode('utf-8'))
elif '/edits' in self.path:
if not shared.model:
self.openai_error("No model loaded.")
return
self.send_response(200)
self.send_access_control_headers()
self.send_header('Content-Type', 'application/json')
self.end_headers()
created_time = int(time.time())
# Using Alpaca format, this may work with other models too.
instruction = body['instruction']
input = body.get('input', '')
# Request parameters
req_params = default_req_params.copy()
stopping_strings = []
# Alpaca is verbose so a good default prompt
default_template = (
"Below is an instruction that describes a task, paired with an input that provides further context. "
"Write a response that appropriately completes the request.\n\n"
"### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n"
)
instruction_template = default_template
# Use the special instruction/input/response template for anything trained like Alpaca
if shared.settings['instruction_template']:
if 'Alpaca' in shared.settings['instruction_template']:
stopping_strings.extend(['\n###'])
else:
try:
instruct = yaml.safe_load(open(f"characters/instruction-following/{shared.settings['instruction_template']}.yaml", 'r'))
template = instruct['turn_template']
template = template\
.replace('<|user|>', instruct.get('user', ''))\
.replace('<|bot|>', instruct.get('bot', ''))\
.replace('<|user-message|>', '{instruction}\n{input}')
instruction_template = instruct.get('context', '') + template[:template.find('<|bot-message|>')].rstrip(' ')
if instruct['user']:
stopping_strings.extend(['\n' + instruct['user'], instruct['user'] ])
except Exception as e:
instruction_template = default_template
print(f"Exception: When loading characters/instruction-following/{shared.settings['instruction_template']}.yaml: {repr(e)}")
print("Warning: Loaded default instruction-following template (Alpaca) for model.")
else:
stopping_strings.extend(['\n###'])
print("Warning: Loaded default instruction-following template (Alpaca) for model.")
edit_task = instruction_template.format(instruction=instruction, input=input)
truncation_length = default(shared.settings, 'truncation_length', 2048)
token_count = len(encode(edit_task)[0])
max_tokens = truncation_length - token_count
req_params['max_new_tokens'] = max_tokens
req_params['truncation_length'] = truncation_length
req_params['temperature'] = clamp(default(body, 'temperature', default_req_params['temperature']), 0.001, 1.999) # fixup absolute 0.0
req_params['top_p'] = clamp(default(body, 'top_p', default_req_params['top_p']), 0.001, 1.0)
req_params['seed'] = shared.settings.get('seed', default_req_params['seed'])
req_params['add_bos_token'] = shared.settings.get('add_bos_token', default_req_params['add_bos_token'])
if debug:
print({'edit_template': edit_task, 'req_params': req_params, 'token_count': token_count})
generator = generate_reply(edit_task, req_params, stopping_strings=stopping_strings, is_chat=False)
longest_stop_len = max([len(x) for x in stopping_strings] + [0])
answer = ''
seen_content = ''
for a in generator:
answer = a
stop_string_found = False
len_seen = len(seen_content)
search_start = max(len_seen - longest_stop_len, 0)
for string in stopping_strings:
idx = answer.find(string, search_start)
if idx != -1:
answer = answer[:idx] # clip it.
stop_string_found = True
if stop_string_found:
break
# some reply's have an extra leading space to fit the instruction template, just clip it off from the reply.
if edit_task[-1] != '\n' and answer and answer[0] == ' ':
answer = answer[1:]
completion_token_count = len(encode(answer)[0])
resp = {
"object": "edit",
"created": created_time,
"choices": [{
"text": answer,
"index": 0,
}],
"usage": {
"prompt_tokens": token_count,
"completion_tokens": completion_token_count,
"total_tokens": token_count + completion_token_count
}
}
if debug:
print({'answer': answer, 'completion_token_count': completion_token_count})
response = json.dumps(resp)
self.wfile.write(response.encode('utf-8'))
elif '/images/generations' in self.path and 'SD_WEBUI_URL' in os.environ:
# Stable Diffusion callout wrapper for txt2img
# Low effort implementation for compatibility. With only "prompt" being passed and assuming DALL-E
# the results will be limited and likely poor. SD has hundreds of models and dozens of settings.
# If you want high quality tailored results you should just use the Stable Diffusion API directly.
# it's too general an API to try and shape the result with specific tags like "masterpiece", etc,
# Will probably work best with the stock SD models.
# SD configuration is beyond the scope of this API.
# At this point I will not add the edits and variations endpoints (ie. img2img) because they
# require changing the form data handling to accept multipart form data, also to properly support
# url return types will require file management and a web serving files... Perhaps later!
self.send_response(200)
self.send_access_control_headers()
self.send_header('Content-Type', 'application/json')
self.end_headers()
width, height = [ int(x) for x in default(body, 'size', '1024x1024').split('x') ] # ignore the restrictions on size
response_format = default(body, 'response_format', 'url') # or b64_json
payload = {
'prompt': body['prompt'], # ignore prompt limit of 1000 characters
'width': width,
'height': height,
'batch_size': default(body, 'n', 1) # ignore the batch limits of max 10
}
resp = {
'created': int(time.time()),
'data': []
}
# TODO: support SD_WEBUI_AUTH username:password pair.
sd_url = f"{os.environ['SD_WEBUI_URL']}/sdapi/v1/txt2img"
response = requests.post(url=sd_url, json=payload)
r = response.json()
# r['parameters']...
for b64_json in r['images']:
if response_format == 'b64_json':
resp['data'].extend([{'b64_json': b64_json}])
else:
resp['data'].extend([{'url': f'data:image/png;base64,{b64_json}'}]) # yeah it's lazy. requests.get() will not work with this
response = json.dumps(resp)
self.wfile.write(response.encode('utf-8'))
elif '/embeddings' in self.path and embedding_model is not None:
self.send_response(200)
self.send_access_control_headers()
self.send_header('Content-Type', 'application/json')
self.end_headers()
input = body['input'] if 'input' in body else body['text']
if type(input) is str:
input = [input]
embeddings = embedding_model.encode(input).tolist()
def enc_emb(emb):
# If base64 is specified, encode. Otherwise, do nothing.
if body.get("encoding_format", "") == "base64":
return float_list_to_base64(emb)
else:
return emb
data = [{"object": "embedding", "embedding": enc_emb(emb), "index": n} for n, emb in enumerate(embeddings)]
response = json.dumps({
"object": "list",
"data": data,
"model": st_model, # return the real model
"usage": {
"prompt_tokens": 0,
"total_tokens": 0,
}
})
if debug:
print(f"Embeddings return size: {len(embeddings[0])}, number: {len(embeddings)}")
self.wfile.write(response.encode('utf-8'))
elif '/moderations' in self.path:
# for now do nothing, just don't error.
self.send_response(200)
self.send_access_control_headers()
self.send_header('Content-Type', 'application/json')
self.end_headers()
response = json.dumps({
"id": "modr-5MWoLO",
"model": "text-moderation-001",
"results": [{
"categories": {
"hate": False,
"hate/threatening": False,
"self-harm": False,
"sexual": False,
"sexual/minors": False,
"violence": False,
"violence/graphic": False
},
"category_scores": {
"hate": 0.0,
"hate/threatening": 0.0,
"self-harm": 0.0,
"sexual": 0.0,
"sexual/minors": 0.0,
"violence": 0.0,
"violence/graphic": 0.0
},
"flagged": False
}]
})
self.wfile.write(response.encode('utf-8'))
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.
self.send_response(200)
self.send_access_control_headers()
self.send_header('Content-Type', 'application/json')
self.end_headers()
tokens = encode(body['prompt'])[0]
response = json.dumps({
'results': [{
'tokens': len(tokens)
}]
})
self.wfile.write(response.encode('utf-8'))
else:
print(self.path, self.headers)
self.send_error(404)
def run_server():
global embedding_model
try:
embedding_model = SentenceTransformer(st_model)
print(f"\nLoaded embedding model: {st_model}, max sequence length: {embedding_model.max_seq_length}")
except:
print(f"\nFailed to load embedding model: {st_model}")
pass
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'Starting OpenAI compatible api at\nOPENAI_API_BASE={public_url}/v1')
except ImportError:
print('You should install flask_cloudflared manually')
else:
print(f'Starting OpenAI compatible api:\nOPENAI_API_BASE=http://{server_addr[0]}:{server_addr[1]}/v1')
server.serve_forever()
def setup():
Thread(target=run_server, daemon=True).start()