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| import os | |
| import torch | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| import openai | |
| # from huggingface_hub.inference_api import InferenceApi | |
| class ChatService: | |
| def __init__(self, api="openai", model_id = "gpt-3.5-turbo"): | |
| # def __init__(self, api="huggingface", model_id = "OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5"): | |
| self._api = api | |
| self._device = "cuda:0" if torch.cuda.is_available() else "cpu" | |
| self._system_prompt = None | |
| self._user_name = None | |
| self._agent_name = None | |
| if self._api=="openai": | |
| openai.api_key = os.getenv("OPENAI_API_KEY") | |
| self._model_id = model_id | |
| elif self._api=="huggingface": | |
| self._system_prompt = "Below are a series of dialogues between various people and an AI assistant. The AI tries to be helpful, polite, honest, sophisticated, emotionally aware, and humble-but-knowledgeable. The assistant is happy to help with almost anything, and will do its best to understand exactly what is needed. It also tries to avoid giving false or misleading information, and it caveats when it isn't entirely sure about the right answer. That said, the assistant is practical and really does its best, and doesn't let caution get too much in the way of being useful.\n-----\n" | |
| self._user_name = "<|prompter|>" | |
| self._agent_name = "<|assistant|>" | |
| self._tokenizer = AutoTokenizer.from_pretrained(model_id) | |
| self._model = AutoModelForCausalLM.from_pretrained(model_id,torch_dtype=torch.float16) | |
| # self._model = AutoModelForCausalLM.from_pretrained(model_id).half() | |
| self._model.eval().to(self._device) | |
| else: | |
| raise Exception(f"Unknown API: {self._api}") | |
| self.reset() | |
| def reset(self): | |
| self._user_history = [] | |
| self._agent_history = [] | |
| self._full_history = self._system_prompt if self._system_prompt else "" | |
| self._messages = [] | |
| if self._system_prompt: | |
| self._messages.append({"role": "system", "content": self._system_prompt}) | |
| def _chat(self, prompt): | |
| if self._api=="openai": | |
| response = openai.ChatCompletion.create( | |
| model=self._model_id, | |
| messages=self._messages, | |
| ) | |
| agent_response = response['choices'][0]['message']['content'] | |
| elif self._api=="huggingface": | |
| tokens = self._tokenizer.encode(prompt, return_tensors="pt", padding=True) | |
| tokens = tokens.to(self._device) | |
| outputs = self._model.generate( | |
| tokens, | |
| early_stopping=True, | |
| max_new_tokens=200, | |
| do_sample=True, | |
| top_k=40, | |
| temperature=1.0, # use 1.0 for debugging/deteministic results | |
| pad_token_id=self._tokenizer.eos_token_id, | |
| ) | |
| agent_response = self._tokenizer.decode(outputs[0], truncate_before_pattern=[r"\n\n^#", "^'''", "\n\n\n"]) | |
| else: | |
| raise Exception(f"API not implemented: {self._api}") | |
| return agent_response | |
| def chat(self, prompt): | |
| if self._user_name: | |
| self._full_history += f"{self._user_name}: {prompt}\n" | |
| else: | |
| self._full_history += f"{prompt}\n" | |
| self._messages.append({"role": "user", "content": prompt}) | |
| self._user_history.append(prompt) | |
| agent_response = self._chat(self._full_history) | |
| self._messages.append({"role": "assistant", "content": agent_response}) | |
| if self._agent_name: | |
| self._full_history += f"{self._agent_name}: {agent_response}\n" | |
| else: | |
| self._full_history += f"{agent_response}\n" | |
| self._agent_history.append(agent_response) | |
| return agent_response |