from fastapi import FastAPI
from fastapi.responses import StreamingResponse
from pydantic import BaseModel
from huggingface_hub import InferenceClient
import uvicorn


app = FastAPI()

client = InferenceClient("SanjiWatsuki/Silicon-Maid-7B")
# client = InferenceClient("TheBloke/Mixtral-8x7B-Instruct-v0.1")
# client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.2")


class Item(BaseModel):
    prompt: str
    history: list
    system_prompt: str
    temperature: float = 0.0
    max_new_tokens: int = 1048
    top_p: float = 0.15
    repetition_penalty: float = 1.0

def format_prompt(message, history):
    prompt = "<s>"
    for user_prompt, bot_response in history:
        prompt += f"[INST] {user_prompt} [/INST]"
        prompt += f" {bot_response}</s> "
    prompt += f"[INST] {message} [/INST]"
    return prompt

def generate(item: Item):
    temperature = float(item.temperature)
    if temperature < 1e-2:
        temperature = 1e-2
    top_p = float(item.top_p)

    generate_kwargs = dict(
        temperature=temperature,
        max_new_tokens=item.max_new_tokens,
        top_p=top_p,
        repetition_penalty=item.repetition_penalty,
        do_sample=True,
        seed=42,
    )

    formatted_prompt = format_prompt(f"{item.system_prompt}, {item.prompt}", item.history)
    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=False, return_full_text=False)
    # output = ""

    # for response in stream:
    #     output += response.token.text
    # return output
    return stream

@app.post("/generate/")
async def generate_text(item: Item):
    response = generate(item)
    return StreamingResponse(response, media_type="text/event-stream")