import gradio as gr
import time
from transformers import Qwen2AudioForConditionalGeneration, AutoProcessor
from io import BytesIO
from urllib.request import urlopen
import librosa
import os, json
from sys import argv
from vllm import LLM, SamplingParams

from huggingface_hub import login
TOKEN = os.environ.get("TOKEN", None)
login(token=TOKEN)

print("transformers version:", transformers.__version__)
print("vllm version:", vllm.__version__)
print("gradio version:", gradio.__version__)


def load_model_processor(model_path):
    processor = AutoProcessor.from_pretrained(model_path)
    llm = LLM(
        model=model_path, trust_remote_code=True, gpu_memory_utilization=0.8,  
        enforce_eager=True,  device = "cuda",
        limit_mm_per_prompt={"audio": 5},
    )
    return llm, processor

model_path1 = "SeaLLMs/SeaLLMs-Audio-7B"
model1, processor1 = load_model_processor(model_path1)

def response_to_audio(audio_url, text, model=None, processor=None, temperature = 0.1,repetition_penalty=1.1, top_p = 0.9,max_new_tokens = 2048):
    if text == None:
        conversation = [
            {"role": "user", "content": [
                {"type": "audio", "audio_url": audio_url},
            ]},]
    elif audio_url == None:
        conversation = [
            {"role": "user", "content": [
                {"type": "text", "text": text},
           ]},]
    else:
        conversation = [
            {"role": "user", "content": [
                {"type": "audio", "audio_url": audio_url},
                {"type": "text", "text": text},
           ]},]

    text = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False)
    audios = []
    for message in conversation:
        if isinstance(message["content"], list):
            for ele in message["content"]:
                if ele["type"] == "audio":
                    if ele['audio_url'] != None:
                        audios.append(librosa.load(
                            ele['audio_url'], 
                            sr=processor.feature_extractor.sampling_rate)[0]
                        )

    sampling_params = SamplingParams(
        temperature=temperature, max_tokens=max_new_tokens, repetition_penalty=repetition_penalty, top_p=top_p, top_k=20,
        stop_token_ids=[],
    )

    input = {
            'prompt': text,
            'multi_modal_data': {
                'audio': [(audio, 16000) for audio in audios]
            }
            }

    output = model.generate([input], sampling_params=sampling_params)[0]
    response = output.outputs[0].text
    return response

def clear_inputs():
    return None, "", ""

def compare_responses(audio_url, text):
    response1 = response_to_audio(audio_url, text, model1, processor1)
    return response1

with gr.Blocks() as demo:
    # gr.Markdown(f"Evaluate {model_path1}")
    # gr.Markdown("""<p align="center"><img src="images/seal_logo.png" style="height: 80px"/><p>""")
    # gr.Image("images/seal_logo.png", elem_id="seal_logo", show_label=False,height=80,show_fullscreen_button=False)
    gr.Markdown(
        """<div style="text-align: center; font-size: 32px; font-weight: bold;">SeaLLMs-Audio ChatBot</div>""",
    )

    # Description text
    gr.Markdown(
        """<div style="text-align: center; font-size: 16px;">
    This WebUI is based on SeaLLMs-Audio-7B-Chat, developed by Alibaba DAMO Academy.<br>
    You can interact with the chatbot in <b>English, Chinese, Indonesian, Thai, or Vietnamese</b>.<br>
    For each round, you can input <b>audio and/or text</b>.
    </div>""",
    )

    # Links with proper formatting
    gr.Markdown(
        """<div style="text-align: center; font-size: 16px;">
        <a href="https://huggingface.co/SeaLLMs/SeaLLMs-v3-7B-Chat">[Website]</a> &nbsp; 
        <a href="https://huggingface.co/SeaLLMs/SeaLLMs-v3-7B-Chat">[Model🤗]</a> &nbsp; 
        <a href="https://github.com/liuchaoqun/SeaLLMs-Audio">[Github]</a>
        </div>""",
    )

    # gr.Markdown(insturctions)
    # with gr.Row():
    #     with gr.Column():
    #         temperature = gr.Slider(minimum=0, maximum=1, value=0.3, step=0.1, label="Temperature")
    #     with gr.Column():
    #         top_p = gr.Slider(minimum=0.1, maximum=1, value=0.5, step=0.1, label="Top P")
    #     with gr.Column():
    #         repetition_penalty = gr.Slider(minimum=0, maximum=2, value=1.1, step=0.1, label="Repetition Penalty")
    
    with gr.Row():
        with gr.Column():
            # mic_input = gr.Microphone(label="Record Audio", type="filepath", elem_id="mic_input")
            mic_input = gr.Audio(sources = ['upload', 'microphone'], label="Record Audio", type="filepath", elem_id="mic_input")
        with gr.Column():
            additional_input = gr.Textbox(label="Text Input")
    
    # Button to trigger the function
    with gr.Row():
        btn_submit = gr.Button("Submit")
        btn_clear = gr.Button("Clear")

    with gr.Row():
        output_text1 = gr.Textbox(label=model_path1.split('/')[-1], interactive=False, elem_id="output_text1")

    btn_submit.click(
        fn=response_to_audio,
        inputs=[mic_input, additional_input],
        outputs=[output_text1],
    )

    btn_clear.click(
        fn=clear_inputs,
        inputs=None,
        outputs=[mic_input, additional_input, output_text1],
        queue=False,
    )


# demo.launch(
#     share=False,
#     inbrowser=True,
#     server_port=7950,
#     server_name="0.0.0.0",
#     max_threads=40
# )

demo.launch(share=True)
demo.queue(default_concurrency_limit=40).launch(share=True)