import torch
from pydantic import Field
from transformers import AutoModel, AutoTokenizer


class CodegeexChatModel:
    device: str = Field(description="device to load the model")
    tokenizer = Field(description="model's tokenizer")
    model = Field(description="Codegeex model")
    temperature: float = Field(description="temperature to use for the model.")

    def __init__(self, model_name_or_path):
        super().__init__()
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        self.tokenizer = AutoTokenizer.from_pretrained(
            model_name_or_path, trust_remote_code=True
        )
        self.model = (
            AutoModel.from_pretrained(model_name_or_path, trust_remote_code=True)
            .to(self.device)
            .eval()
        )
        print("Model has been initialized.")

    def chat(self, prompt, temperature=0.2, top_p=0.95):
        try:
            response, _ = self.model.chat(
                self.tokenizer,
                query=prompt,
                max_length=120000,
                temperature=temperature,
                top_p=top_p,
            )
            return response
        except Exception as e:
            return f"error: {e}"

    def stream_chat(self, prompt, temperature=0.2, top_p=0.95):

        try:
            for response, _ in self.model.stream_chat(
                    self.tokenizer,
                    query=prompt,
                    max_length=120000,
                    temperature=temperature,
                    top_p=top_p,
            ):
                yield response
        except Exception as e:
            yield f"error: {e}"