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
"""
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}] + history
    messages.append({"role":"user","content":message})

    reponse = ""

    # for val in history:
    #     if val[0]:
    #         messages.append({"role": "user", "content": val[0]})
    #     if val[1]:
    #         messages.append({"role": "assistant", "content": val[1]})

    for part in client.chat_completion(
        messages, max_tokens=max_tokens, streams=True, temperature=temperature,
        top_p=top_p
    ):
        token = part.choices[0].delta.content
        if token:
            response += token

    history.append({"role":"user", "content": message})
    history.append({"role":"assistant", "content": respond})

    return history,""

    # 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)",
#         ),
#     ],
# )

with gr.Blocks() as demo:
    gr.Markdown("## Zephyr Chatbot with Custom UI")

    chatbot = gr.Chatbot(type="messages", label="Chatbot")
    state = gr.State([])

    with gr.Row():
        msg = gr.Textbox(label="Type your message...", scale=6)
        send_btn = gr.Button("Send", scale=1)

    role_dropdown = gr.Dropdown(choices=["SDE", "BA"], label="Select Role", value="SDE")

    system = gr.Textbox(value="You are a friendly chatbot.", label="System message")
    max_tokens = gr.Slider(1, 2048, value=512, label="Max tokens")
    temperature = gr.Slider(0.1, 4.0, value=0.7, label="Temperature", step=0.1)
    top_p = gr.Slider(0.1, 1.0, value=0.95, label="Top-p", step=0.05)

    with gr.Row():
        clear_btn = gr.Button("Clear Chat")
        dummy_btn = gr.Button("Dummy Action")

    def handle_submit(message, history, system, max_tokens, temperature, top_p):
        response_gen = response(message, history, system, max_tokens, temperature, top_p)
        final_response = ""
        for r in response_gen:
            final_response = r
        updated_history = history + [(message, final_response)]
        return updated_history, updated_history, ""

    send_btn.click(
        handle_submit,
        [msg, state, system, max_tokens, temperature, top_p],
        [chatbot, state, msg],
    )

    msg.submit(
        handle_submit,
        [msg, state, system, max_tokens, temperature, top_p],
        [chatbot, state, msg],
    )

    clear_btn.click(lambda: ([], [], ""), None, [chatbot, state, msg])

    dummy_btn.click(lambda: gr.Info("Dummy action clicked!"), None, None)


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