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 = "meta-llama/Meta-Llama-3.1-8B-Instruct"
    model_id = "google/gemma-2b"
    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 { MatchaTTSRaw } from "https://akjava.github.io/Matcha-TTS-Japanese/js-esm/matcha_tts_raw.js";
        import { webWavPlay } from "https://akjava.github.io/Matcha-TTS-Japanese/js-esm/web_wav_play.js";
        import { arpa_to_ipa } from "https://akjava.github.io/Matcha-TTS-Japanese/js-esm/arpa_to_ipa.js";
        import { loadCmudict } from "https://akjava.github.io/Matcha-TTS-Japanese/js-esm/cmudict_loader.js";
        import { env,textToArpa} from "https://akjava.github.io/Matcha-TTS-Japanese/js-esm/text_to_arpa.js";
        
        env.allowLocalModels = true;
        env.localModelPath = "https://akjava.github.io/Matcha-TTS-Japanese/models/";
        env.backends.onnx.logLevel = "fatal";
        
        let matcha_tts_raw;
        let cmudict ={};
        let speaking = false;
        async function main(text,speed=1.0,tempature=0.5,spk=0) {
            console.log(text)
            if (speaking){
                console.log("speaking return")
            }
            speaking = true
            console.log("main called")
            if(!matcha_tts_raw){
                matcha_tts_raw = new MatchaTTSRaw()
                console.time("load model");
                await matcha_tts_raw.load_model('https://huggingface.co./spaces/Akjava/matcha-tts-onnx-benchmarks/resolve/main/models/matcha-tts/ljspeech_sim.onnx',{ executionProviders: ['webgpu','wasm'] });
                
                console.timeEnd("load model");
                
                let cmudictReady = loadCmudict(cmudict,'https://akjava.github.io/Matcha-TTS-Japanese/dictionaries/cmudict-0.7b')
                await cmudictReady
            }else{
                console.log("session exist skip load model")
            }
           
            const arpa_text = await textToArpa(cmudict,text)
            const ipa_text = arpa_to_ipa(arpa_text).replace(/\s/g, "");
            console.log(ipa_text)
            const spks = 0
            
            console.time("infer");
            const result = await matcha_tts_raw.infer(ipa_text, tempature, speed,spks);
            
            if (result!=null){
                console.timeEnd("infer");
                webWavPlay(result)
               
            }
    
            speaking = false
        }
        window.MatchaTTSEn = main
        console.log(MatchaTTSRaw)
</script>
'''
with gr.Blocks(title="LLM with TTS",head=head) as demo:
    gr.Markdown("**Qwen2.5-0.5B-Instruct/LJSpeech**.LLM and TTS models will change without notice.")
    js = """
    function(chatbot){
    text = (chatbot[chatbot.length -1])["content"]
    window.MatchaTTSEn(text)
    }
    """
    chatbot = gr.Chatbot(type="messages")
    chatbot.change(None,[chatbot],[],js=js)
    msg = gr.Textbox()
    clear = gr.ClearButton([msg, chatbot])
    
#demo = gr.ChatInterface(call_generate_text,chatbot=chatbot,type="messages")
    msg.submit(call_generate_text, [msg, chatbot], [msg, chatbot])

if __name__ == "__main__":
    init()
    demo.launch(share=True)