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IntelligenzaArtificiale
65 lines
1.9 KiB
Python
65 lines
1.9 KiB
Python
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from hugchat import hugchat
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import requests
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from langchain.llms.base import LLM
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from typing import Optional, List, Mapping, Any
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import pydantic
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import os
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from langchain import PromptTemplate, LLMChain
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from time import sleep
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class HuggingChat(LLM):
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history_data: Optional[List] = []
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chatbot : Optional[hugchat.ChatBot] = None
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conversation : Optional[str] = ""
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cookiepath : Optional[str]
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#### WARNING : for each api call this library will create a new chat on chat.openai.com
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@property
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def _llm_type(self) -> str:
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return "custom"
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def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
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if stop is not None:
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pass
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#raise ValueError("stop kwargs are not permitted.")
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#token is a must check
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if self.chatbot is None:
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if self.cookiepath is None:
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ValueError("Cookie path is required, pls check the documentation on github")
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else:
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if self.conversation == "":
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self.chatbot = hugchat.ChatBot(cookie_path=self.cookiepath)
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else:
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raise ValueError("Something went wrong")
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sleep(2)
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data = self.chatbot.chat(prompt, temperature=0.5, stream=False)
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#conversation_list = self.chatbot.get_conversation_list()
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#print(conversation_list)
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#add to history
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self.history_data.append({"prompt":prompt,"response":data})
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return data
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@property
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def _identifying_params(self) -> Mapping[str, Any]:
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"""Get the identifying parameters."""
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return {"model": "HuggingCHAT"}
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#llm = HuggingChat(cookiepath = "YOUR-COOKIES-PATH") #for start new chat
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#print(llm("Hello, how are you?"))
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#print(llm("what is AI?"))
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#print(llm("Can you resume your previus answer?")) #now memory work well
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