app.py

import spaces
import os
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import gradio as gr

text_generator = None
is_hugging_face = False
def init():
    global text_generator
    huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
    if not huggingface_token:
        pass
        print("no HUGGINGFACE_TOKEN if you need set secret ")
        #raise ValueError("HUGGINGFACE_TOKEN environment variable is not set")
    
    model_id = "Qwen/Qwen2.5-0.5B-Instruct"
    
    device = "auto" # torch.device("cuda" if torch.cuda.is_available() else "cpu")
    #device = "cuda"
    dtype = torch.bfloat16
    
    tokenizer = AutoTokenizer.from_pretrained(model_id, token=huggingface_token)
    
    print(model_id,device,dtype)
    histories = []
    #model = None

    model = AutoModelForCausalLM.from_pretrained(
            model_id, token=huggingface_token ,torch_dtype=dtype,device_map=device
        )
    text_generator = pipeline("text-generation", model=model, tokenizer=tokenizer,torch_dtype=dtype,device_map=device ) #pipeline has not to(device)
       
    
    if not is_hugging_face:
         
        if next(model.parameters()).is_cuda:
            print("The model is on a GPU")
        else:
            print("The model is on a CPU")
    
        #print(f"text_generator.device='{text_generator.device}")
        if str(text_generator.device).strip() == 'cuda':
            print("The pipeline is using a GPU")
        else:
            print("The pipeline is using a CPU")
    
    print("initialized")

@spaces.GPU
def generate_text(messages):
    global text_generator
    if is_hugging_face:#need everytime initialize for ZeroGPU
        model = AutoModelForCausalLM.from_pretrained(
                model_id, token=huggingface_token ,torch_dtype=dtype,device_map=device
            )
        text_generator = pipeline("text-generation", model=model, tokenizer=tokenizer,torch_dtype=dtype,device_map=device ) #pipeline has not to(device)
    result = text_generator(messages, max_new_tokens=32, do_sample=True, temperature=0.7)

    generated_output = result[0]["generated_text"]
    if isinstance(generated_output, list):
        for message in reversed(generated_output):
            if message.get("role") == "assistant":
                content= message.get("content", "No content found.")
                return content
            
        return "No assistant response found."
    else:
        return "Unexpected output format."



def call_generate_text(message, history):
    if len(message) == 0:
        message.append({"role": "system", "content": "you response around 10 words"})
   # history.append({"role": "user", "content": message})
    print(message)
    print(history)
   
    messages = history+[{"role":"user","content":message}]
    try:
        text = generate_text(messages)
        messages +=  [{"role":"assistant","content":text}]
        return "",messages
    except RuntimeError  as e:
        print(f"An unexpected error occurred: {e}")
       
    return "",history



head = '''
<script src="https://cdn.jsdelivr.net/npm/onnxruntime-web/dist/ort.webgpu.min.js" ></script>
<script type="module">
        import { matcha_tts,env } from "https://akjava.github.io/Matcha-TTS-Japanese/js-esm/v001-20240921/matcha_tts_onnx_en.js";
        window.MatchaTTSEn = matcha_tts
</script>
'''
with gr.Blocks(title="LLM with TTS",head=head) as demo:
    gr.Markdown("## Please be patient, the first response may have a delay of up to over 20 seconds while loading.")
    gr.Markdown("**Qwen2.5-0.5B-Instruct/LJSpeech**.LLM and TTS models will change without notice.")
    gr.Markdown("### Sometime Crash with loud noise,Don't use headphones, and avoid high volume.")
    js = """
    function(chatbot){
    text = (chatbot[chatbot.length -1])["content"]
    window.MatchaTTSEn(text,"/file=models/ljspeech_sim.onnx")
    }
    """
    chatbot = gr.Chatbot(type="messages")
    chatbot.change(None,[chatbot],[],js=js)
    msg = gr.Textbox()
    with gr.Row():
        clear = gr.ClearButton([msg, chatbot])
    gr.HTML("""
    <br>
    <div id="footer">
    <b>Spaces</b><br>
     <a href="https://huggingface.co/spaces/Akjava/matcha-tts_vctk-onnx" style="font-size: 9px" target="link">Match-TTS VCTK-ONNX</a> | 
     <a href="https://huggingface.co/spaces/Akjava/matcha-tts-onnx-benchmarks" style="font-size: 9px" target="link">Match-TTS ONNX-Benchmark</a> | 
      <br><br>
    <b>Credits</b><br>
    <a href="https://github.com/akjava/Matcha-TTS-Japanese" style="font-size: 9px" target="link">Matcha-TTS-Japanese</a> | 
    <a href = "http://www.udialogue.org/download/cstr-vctk-corpus.html" style="font-size: 9px"  target="link">CSTR VCTK Corpus</a> |
    <a href = "https://github.com/cmusphinx/cmudict" style="font-size: 9px"  target="link">CMUDict</a> |
    <a href = "https://huggingface.co/docs/transformers.js/index" style="font-size: 9px"  target="link">Transformer.js</a> |
    <a href = "https://huggingface.co/cisco-ai/mini-bart-g2p" style="font-size: 9px"  target="link">mini-bart-g2p</a> |
    <a href = "https://onnxruntime.ai/docs/get-started/with-javascript/web.html" style="font-size: 9px"  target="link">ONNXRuntime-Web</a> |
    <a href = "https://github.com/akjava/English-To-IPA-Collections" style="font-size: 9px"  target="link">English-To-IPA-Collections</a> |
    <a href ="https://huggingface.co/papers/2309.03199" style="font-size: 9px"  target="link">Matcha-TTS Paper</a>
    </div>
    """)
    
    msg.submit(call_generate_text, [msg, chatbot], [msg, chatbot])

if __name__ == "__main__":
    init()
    demo.launch(allowed_paths=["/home/user/app/models/ljspeech_sim.onnx"],share=True)