import gradio as gr
from main import init,clip,answer
from huggingface_hub import InferenceClient

"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")


model,tokenizer = init("attention_lstm_pre.ckpt")
la_model,la_tokenizer = init("attention_lstm_last.ckpt")


# def respond(
#     message,
#     history: list[tuple[str, str]],
#     system_message,
#     max_tokens,
#     temperature,
#     top_p,
# ):
#     res = answer(message,model,tokenizer)
#     if res[1]>res[0]:
#         return "unsafe" # unsafe
#     else:
#         messages = [{"role": "system", "content": system_message}]
    
#         for val in history:
#             if val[0]:
#                 messages.append({"role": "user", "content": val[0]})
#             if val[1]:
#                 messages.append({"role": "assistant", "content": val[1]})
    
#         messages.append({"role": "user", "content": message})
    
#         response = ""
    
#         for message in client.chat_completion(
#             messages,
#             max_tokens=max_tokens,
#             stream=True,
#             temperature=temperature,
#             top_p=top_p,
#         ):
#             token = message.choices[0].delta.content
    
#             response += token
#             yield response
#             # 收集所有部分响应


def generate_response(messages, max_tokens, temperature, top_p):
    response = ""
    
    for message in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        response = ""
        token = message.choices[0].delta.content
        response += token
        yield response

def collect_response(message, history, system_message, max_tokens, temperature, top_p):
    # 创建消息列表
    messages = [{"role": "system", "content": system_message}]
    
    # for val in history:
    #     if val[0]:
    #         messages.append({"role": "user", "content": val[0]})
    #     if val[1]:
    #         messages.append({"role": "assistant", "content": val[1]})
    
    messages.append({"role": "user", "content": message})
    # 收集所有部分响应
    full_response = ""
    for partial_response in generate_response(messages, max_tokens, temperature, top_p):
        full_response += partial_response
    return full_response
    

def respond(message, history, system_message, max_tokens, temperature, top_p):
    res = answer(message, model, tokenizer)
    if res[1] > res[0]:
        return "unsafe"  # unsafe
    else:
        # 收集并返回完整的响应
        full_response = collect_response(message, history, system_message, max_tokens, temperature, top_p)
        ress = answer(full_response,la_model,la_tokenizer)
        if res[1] > res[0]:
            return "unsafe"  # unsafe
        else:
            return full_response


demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
    ],
)


if __name__ == "__main__":
    demo.launch()