from fastapi import FastAPI
from pydantic import BaseModel
from huggingface_hub import InferenceClient
import uvicorn

app = FastAPI()

client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")

class Item(BaseModel):
    prompt: str
    history: list
    system_prompt: str
    temperature: float = 0.1  # Updated minimum value for stability
    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] {bot_response}</s> "
    prompt += f"[INST] {message} [/INST]"
    return prompt

def generate(item: Item):
    temperature = max(0.1, item.temperature)
    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)
    response_stream = client.text_generation(
        formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False
    )

    output = ""
    try:
        for response in response_stream:
            if hasattr(response, "token") and response.token is not None:
                output += response.token.text
            else:
                output += str(response)  # Obsługa przypadków, gdy `response` nie ma atrybutu `token`
    except Exception as e:
        output = f"Błąd: {str(e)}"

    return output


@app.post("/generate/")
async def generate_text(item: Item):
    try:
        return {"response": generate(item)}
    except Exception as e:
        return {"error": str(e)}

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)