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import json
import re
import requests
from messagers.message_outputer import OpenaiStreamOutputer
from utils.logger import logger
from utils.enver import enver
from huggingface_hub import InferenceClient
class MessageStreamer:
MODEL_MAP = {
"mixtral-8x7b": "mistralai/Mixtral-8x7B-Instruct-v0.1",
}
def __init__(self, model: str):
self.model = model
self.model_fullname = self.MODEL_MAP[model]
def parse_line(self, line):
line = line.decode("utf-8")
line = re.sub(r"data:\s*", "", line)
data = json.loads(line)
content = data["token"]["text"]
return content
def chat(
self,
prompt: str = None,
temperature: float = 0.01,
max_new_tokens: int = 32000,
stream: bool = True,
yield_output: bool = False,
):
# https://huggingface.co/docs/text-generation-inference/conceptual/streaming#streaming-with-curl
self.request_url = (
f"https://api-inference.huggingface.co/models/{self.model_fullname}"
)
self.message_outputer = OpenaiStreamOutputer()
self.request_headers = {
"Content-Type": "application/json",
}
# huggingface_hub/inference/_client.py: class InferenceClient > def text_generation()
self.request_body = {
"inputs": prompt,
"parameters": {
"temperature": temperature,
"max_new_tokens": max_new_tokens,
"return_full_text": False,
},
"stream": stream,
}
print(self.request_url)
enver.set_envs(proxies=True)
stream = requests.post(
self.request_url,
headers=self.request_headers,
json=self.request_body,
proxies=enver.requests_proxies,
stream=stream,
)
print(stream.status_code)
for line in stream.iter_lines():
if not line:
continue
content = self.parse_line(line)
if content.strip() == "</s>":
content_type = "Finished"
logger.mesg("\n[Finished]")
else:
content_type = "Completions"
logger.mesg(content, end="")
if yield_output:
output = self.message_outputer.output(
content=content, content_type=content_type
)
yield output
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