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IntelligenzaArtificiale
96 lines
4.0 KiB
Python
96 lines
4.0 KiB
Python
from EdgeGPT import Chatbot, ConversationStyle
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import asyncio
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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 BingChat(LLM):
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history_data: Optional[List] = []
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cookiepath : Optional[str]
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chatbot : Optional[Chatbot] = None
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conversation_style : Optional[str]
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conversation_style_on : Optional[ConversationStyle] = ConversationStyle.precise
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search_result : Optional[bool] = False
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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 select_conversation(self, conversation_style: str):
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if conversation_style == "precise":
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self.conversation_style_on = ConversationStyle.precise
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elif conversation_style == "creative":
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self.conversation_style_on = ConversationStyle.creative
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elif conversation_style == "balanced":
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self.conversation_style_on = ConversationStyle.balanced
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else:
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raise ValueError("conversation_style must be precise, creative or balaced")
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self.conversation_style = conversation_style
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async 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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#cookiepath 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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raise ValueError("Need a COOKIE , check https://github.com/acheong08/EdgeGPT/tree/master#getting-authentication-required for get your COOKIE AND SAVE")
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else:
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#if self.chatbot == None:
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self.chatbot = await Chatbot.create(cookie_path=self.cookiepath)
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if self.conversation_style is not None:
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self.conversation_style_on = self.select_conversation(self.conversation_style)
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response = await self.chatbot.ask(prompt=prompt, conversation_style=self.conversation_style, search_result=self.search_result)
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response_messages = response.get("item", {}).get("messages", [])
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response_text = response_messages[1].get("text", "")
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if response_text == "":
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hidden_text = response_messages[1].get("hiddenText", "")
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print(">>>> [DEBBUGGER] hidden_text = " + str(hidden_text) + " [DEBBUGGER] <<<<")
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print(">>>> [DEBBUGGER] BING CHAT dont is open Like CHATGPT , BingCHAT have refused to respond. [DEBBUGGER] <<<<")
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response_text = hidden_text
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"""
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# reset the chatbot and remake the call
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print("[DEBUGGER] Chatbot failed to respond. Resetting and trying again. [DEBUGGER]")
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print("[ INFO DEBUGGER ] \n<Response>\n" + str(response) + "\n</Response>\n\n")
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sleep(10)
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self.chatbot = await Chatbot.create(cookie_path=self.cookiepath)
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sleep(2)
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response = await self.chatbot.ask(prompt=prompt)
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response_messages = response.get("item", {}).get("messages", [])
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response_text = response_messages[1].get("text", "")
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"""
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#add to history
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self.history_data.append({"prompt":prompt,"response":response_text})
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return response_text
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def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
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return asyncio.run(self.call(prompt=prompt))
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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": "BingCHAT", "cookiepath": self.cookiepath}
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#llm = BingChat(cookiepath = "YOUR-COOKIE") #for start new chat
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#llm = BingChat(cookiepath = "YOUR-COOKIE", conversation_style = "precise") #precise, creative or balaced
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#llm = BingChat(cookiepath = "YOUR-COOKIE" , conversation_style = "precise" , search_result=True) #with web access
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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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