import gradio as gr
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
"""
from new_chat import Conversation, ChatgptAPI

chat_api = ChatgptAPI()

def predict(system_input, password_input, user_in_file, user_input, conversation):
    if password_input != '112233':
       return [(None, "Wrong password!")], conversation, user_input

    if conversation.is_initialized() == False:
        conversation = Conversation(system_input, 5)
    conversation = chat_api.get_single_round_completion(user_in_file, user_input, conversation)
    return conversation, conversation, None
    #_, conversation = chat_api.get_multi_round_completion(user_input, conversation)
    #return conversation.get_history_messages(), conversation, None


def clear_history(conversation):
    conversation.clear()
    return None, conversation


with gr.Blocks(css="#chatbot{height:350px} .overflow-y-auto{height:600px}") as demo:
    chatbot = gr.Chatbot(elem_id="chatbot")
    conversation = gr.State(value=Conversation())

    with gr.Row():
        system_in_txt = gr.Textbox(lines=1, label="System role content:", placeholder="Enter system role content")
        password_in_txt = gr.Textbox(lines=1, label="Password:", placeholder="Enter password")
       
    with gr.Row():
        user_in_file = gr.File(label="Upload File")
        user_in_txt = gr.Textbox(lines=3, label="User role content:", placeholder="Enter text...").style(container=False)
    
    with gr.Row():
        submit_button = gr.Button("Submit")
        reset_button = gr.Button("Reset")
    
    submit_button.click(predict, [system_in_txt, password_in_txt, user_in_file, user_in_txt, conversation], [chatbot, conversation, user_in_txt])
    reset_button.click(clear_history, [conversation], [chatbot, conversation], queue=False)

'''
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")


def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    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

"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
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()