import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig
import torch
import os

MODEL_NAMES = {
    "DeepSeek-R1-Distill-Qwen-7B": "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
    "DeepSeek-R1-Distill-Llama-8B": "deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
}

HF_TOKEN = os.getenv("HF_TOKEN")

def load_model(model_path):
    tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True, token=HF_TOKEN)
    config = AutoConfig.from_pretrained(model_path, trust_remote_code=True, token=HF_TOKEN)
    if hasattr(config, "quantization_config"):
        del config.quantization_config  # 刪除量化配置，避免使用 FP8

    model = AutoModelForCausalLM.from_pretrained(
        model_path,
        config=config,
        trust_remote_code=True,
        token=HF_TOKEN,
        torch_dtype=torch.float16,
        device_map="auto",
    )
    return model, tokenizer

current_model, current_tokenizer = load_model("deepseek-ai/DeepSeek-R1-Distill-Llama-8B")

def chat(message, history, model_name):
    global current_model, current_tokenizer

    if model_name != current_model:
        current_model, current_tokenizer = load_model(MODEL_NAMES[model_name])

    device = "cuda" if torch.cuda.is_available() else "cpu"
    inputs = current_tokenizer(message, return_tensors="pt").to(device)
    outputs = current_model.generate(**inputs, max_length=1024)
    response = current_tokenizer.decode(outputs[0], skip_special_tokens=True)

    return response

with gr.Blocks() as app:
    gr.Markdown("## Chatbot with DeepSeek Models")

    with gr.Row():
        chat_interface = gr.ChatInterface(
            chat,
            type="messages",
            flagging_mode="manual",
            save_history=True,
        )
        model_selector = gr.Dropdown(
            choices=list(MODEL_NAMES.keys()),
            value="DeepSeek-R1-Distill-Llama-8B",
            label="Select Model",
        )


app.launch()