import requests
from langchain.llms.base import LLM
from typing import Optional, List, Mapping, Any
import pydantic


class sqlchatGPT3(LLM):
    
    history_data: Optional[List] = []

    @property
    def _llm_type(self) -> str:
        return "custom"

    def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
        if stop is not None:
            raise ValueError("stop kwargs are not permitted.")
        full_message = ""
        headers = {
            'authority': 'www.sqlchat.ai',
            'accept': '*/*',
            'accept-language': 'en,fr-FR;q=0.9,fr;q=0.8,es-ES;q=0.7,es;q=0.6,en-US;q=0.5,am;q=0.4,de;q=0.3',
            'content-type': 'text/plain;charset=UTF-8',
            'origin': 'https://www.sqlchat.ai',
            'referer': 'https://www.sqlchat.ai/',
            'sec-fetch-dest': 'empty',
            'sec-fetch-mode': 'cors',
            'sec-fetch-site': 'same-origin',
            'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.0.0 Safari/537.36',
        }

        data = {
            'messages':[
                {'role':'system','content':''},
                {'role':'user','content':prompt},
            ],
            'openAIApiConfig':{
                'key':'',
                'endpoint':''
            }
        }

        response = requests.post('https://www.sqlchat.ai/api/chat', headers=headers, json=data, stream=True)
        for message in response.iter_content(chunk_size=1024):
            full_message += message.decode()
            

        self.history_data.append({"question": prompt, "answer": full_message})
        
        return full_message

    @property
    def _identifying_params(self) -> Mapping[str, Any]:
        """Get the identifying parameters."""
        return {"model": "sqlchat.ai"}


#llm = sqlchatGPT3()

#print(llm("Hello, how are you?"))
#print(llm("what is AI?"))
#print(llm("how have i question in before?"))