import os

from langchain.llms import CTransformers
from fastapi import FastAPI
from pydantic import BaseModel

file_name = "zephyr-7b-beta.Q4_K_S.gguf"
config = {
    "max_new_token": 1024,
    "repetition_penalty": 1.1,
    "temperature": 0.5,
    "top_k": 50,
    "top_p": 0.9,
    "stream": True,
    "threads": int(os.cpu_count() / 2),
}
llm = CTransformers(
    model=file_name,
    model_type="mistral",
    lib="avx2",
    **config
)
print(llm)


class validation(BaseModel):
    prompt: str
#Fast API


app = FastAPI()

@app.post("/llm_on_cpu")
async def stream(item: validation):
    system_prompt = 'Below is an instruction that describes a task. Write a response that appropriately completes the request.'
    E_INST = "</s>"
    user, assistant = "<|user|>", "<|assistant|>"
    prompt = f"{system_prompt}{E_INST}\n{user}\n{item.prompt}{E_INST}\n{assistant}\n"

    return llm(prompt)