import os
import json
import requests
import logging
from models.custom_parsers import CustomStringOutputParser
from app_config import ENDPOINT_NAMES
from langchain.chains import ConversationChain
from langchain_core.callbacks.manager import CallbackManagerForLLMRun
from langchain_core.language_models.llms import LLM
from langchain.prompts import PromptTemplate

from typing import Any, List, Mapping, Optional, Dict

class DatabricksCustomBizLLM(LLM):
    issue:str
    language:str
    temperature:float = 0.8
    max_tokens: int = 128
    db_url:str
    headers:Mapping[str,str] = {'Authorization': f'Bearer {os.environ.get("DATABRICKS_TOKEN")}', 'Content-Type': 'application/json'}

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

    def _call(
        self,
        prompt: str,
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> str:
        data_ = {'inputs': {
            'prompt': [prompt],
            'issue': [self.issue],
            'language': [self.language],
            'temperature': [self.temperature], 
            'max_tokens': [self.max_tokens],
        }}
        data_json = json.dumps(data_, allow_nan=True)
        response = requests.request(method='POST', headers=self.headers, url=self.db_url, data=data_json)
        
        if response.status_code != 200:
            raise Exception(f'Request failed with status {response.status_code}, {response.text}')
        return response.json()["predictions"][0]["generated_text"]

_DATABRICKS_TEMPLATE_ = """{history}
helper: {input}
texter:"""

def get_databricks_biz_chain(source, issue, language, memory, temperature=0.8):

    PROMPT = PromptTemplate(
        input_variables=['history', 'input'],
        template=_DATABRICKS_TEMPLATE_
    )
    llm = DatabricksCustomBizLLM(
        issue=issue,
        language=language,
        temperature=temperature,
        max_tokens=256,
        db_url = os.environ['DATABRICKS_URL'].format(endpoint_name=ENDPOINT_NAMES.get(source, "conversation_simulator"))
    )
    llm_chain = ConversationChain(
        llm=llm,
        prompt=PROMPT,
        memory=memory,
        output_parser=CustomStringOutputParser()
    )
    logging.debug(f"loaded Databricks Biz model")
    return llm_chain, "helper:"