Update app.py

app.py

@@ -1,257 +1,24 @@
-import os
-import time
-import uuid
-from datetime import datetime
-
 import gradio as gr
-import soundfile as sf
 from huggingface_hub import InferenceClient
 
-from model import get_pretrained_model, language_to_models
-
-
-def MyPrint(s):
-    now = datetime.now()
-    date_time = now.strftime("%Y-%m-%d %H:%M:%S.%f")
-    print(f"{date_time}: {s}")
-
-
-title = "# Next-gen Kaldi: Text-to-speech (TTS)"
-
-description = """
-This space shows how to convert text to speech with Next-gen Kaldi.
-It is running on CPU within a docker container provided by Hugging Face.
-See more information by visiting the following links:
-- <https://github.com/k2-fsa/sherpa-onnx>
-If you want to deploy it locally, please see
-<https://k2-fsa.github.io/sherpa/>
-If you want to use Android APKs, please see
-<https://k2-fsa.github.io/sherpa/onnx/tts/apk.html>
-If you want to use Android text-to-speech engine APKs, please see
-<https://k2-fsa.github.io/sherpa/onnx/tts/apk-engine.html>
-If you want to download an all-in-one exe for Windows, please see
-<https://github.com/k2-fsa/sherpa-onnx/releases/tag/tts-models>
-"""
-
-# css style is copied from
-# https://huggingface.co/spaces/alphacep/asr/blob/main/app.py#L113
-css = """
-.result {display:flex;flex-direction:column}
-.result_item {padding:15px;margin-bottom:8px;border-radius:15px;width:100%}
-.result_item_success {background-color:mediumaquamarine;color:white;align-self:start}
-.result_item_error {background-color:#ff7070;color:white;align-self:start}
-"""
-
-examples = [
-    [
-        "Chinese (Mandarin, 普通话)",
-        "csukuangfj/matcha-icefall-zh-baker|1 speaker",
-        "某某银行的副行长和一些行政领导表示，他们去过长江和长白山; 经济不断增长。2024年12月31号，拨打110或者18920240511。123456块钱。",
-        0,
-        1.0,
-    ],
-    [
-        "Chinese (Mandarin, 普通话)",
-        "csukuangfj/vits-zh-hf-fanchen-wnj|1 speaker",
-        "在一个阳光明媚的夏天，小马、小羊和小狗它们一块儿在广阔的草地上，嬉戏玩耍，这时小猴来了，还带着它心爱的足球活蹦乱跳地跑前、跑后教小马、小羊、小狗踢足球。",
-        0,
-        1.0,
-    ],
-    [
-        "Chinese (Mandarin, 普通话)",
-        "csukuangfj/vits-zh-hf-fanchen-C|187 speakers",
-        '小米的使命是，始终坚持做"感动人心、价格厚道"的好产品，让全球每个人都能享受科技带来的美好生活。',
-        0,
-        1.0,
-    ],
-    ["Min-nan (闽南话)", "csukuangfj/vits-mms-nan", "ài piaǸ chiah ē iaN̂", 0, 1.0],
-    ["Thai", "csukuangfj/vits-mms-tha", "ฉันรักคุณ", 0, 1.0],
-    [
-        "Chinese (Mandarin, 普通话)",
-        "csukuangfj/sherpa-onnx-vits-zh-ll|5 speakers",
-        "当夜幕降临，星光点点，伴随着微风拂面，我在静谧中感受着时光的流转，思念如涟漪荡漾，梦境如画卷展开，我与自然融为一体，沉静在这片宁静的美丽之中，感受着生命的奇迹与温柔。",
-        2,
-        1.0,
-    ],
-]
-
-
-def update_model_dropdown(language: str):
-    if language in language_to_models:
-        choices = language_to_models[language]
-        return gr.Dropdown(
-            choices=choices,
-            value=choices[0],
-            interactive=True,
-        )
-
-    raise ValueError(f"Unsupported language: {language}")
-
-
-def build_html_output(s: str, style: str = "result_item_success"):
-    return f"""
-    <div class='result'>
-        <div class='result_item {style}'>
-          {s}
-        </div>
-    </div>
-    """
-
-
-def process(language: str, repo_id: str, text: str, sid: str, speed: float):
-    MyPrint(f"Input text: {text}. sid: {sid}, speed: {speed}")
-    sid = int(sid)
-    tts = get_pretrained_model(repo_id, speed)
-
-    start = time.time()
-    audio = tts.generate(text, sid=sid)
-    end = time.time()
-
-    if len(audio.samples) == 0:
-        raise ValueError(
-            "Error in generating audios. Please read previous error messages."
-        )
-
-    duration = len(audio.samples) / audio.sample_rate
-
-    elapsed_seconds = end - start
-    rtf = elapsed_seconds / duration
-
-    info = f"""
-    Wave duration  : {duration:.3f} s <br/>
-    Processing time: {elapsed_seconds:.3f} s <br/>
-    RTF: {elapsed_seconds:.3f}/{duration:.3f} = {rtf:.3f} <br/>
-    """
-
-    MyPrint(info)
-    MyPrint(f"\nrepo_id: {repo_id}\ntext: {text}\nsid: {sid}\nspeed: {speed}")
-
-    filename = str(uuid.uuid4())
-    filename = f"{filename}.wav"
-    sf.write(
-        filename,
-        audio.samples,
-        samplerate=audio.sample_rate,
-        subtype="PCM_16",
-    )
-
-    return filename, build_html_output(info)
-
-
-# Démo Next-gen Kaldi TTS
-with gr.Blocks(css=css) as demo_tts:
-    gr.Markdown(title)
-    language_choices = list(language_to_models.keys())
-
-    language_radio = gr.Radio(
-        label="Language",
-        choices=language_choices,
-        value=language_choices[0],
-    )
-
-    model_dropdown = gr.Dropdown(
-        choices=language_to_models[language_choices[0]],
-        label="Select a model",
-        value=language_to_models[language_choices[0]][0],
-    )
-
-    language_radio.change(
-        update_model_dropdown,
-        inputs=language_radio,
-        outputs=model_dropdown,
-    )
-
-    with gr.Tabs():
-        with gr.TabItem("Please input your text"):
-            input_text = gr.Textbox(
-                label="Input text",
-                info="Your text",
-                lines=3,
-                placeholder="Please input your text here",
-            )
-
-            input_sid = gr.Textbox(
-                label="Speaker ID",
-                info="Speaker ID",
-                lines=1,
-                max_lines=1,
-                value="0",
-                placeholder="Speaker ID. Valid only for mult-speaker model",
-            )
-
-            input_speed = gr.Slider(
-                minimum=0.1,
-                maximum=10,
-                value=1,
-                step=0.1,
-                label="Speed (larger->faster; smaller->slower)",
-            )
-
-            input_button = gr.Button("Submit")
-
-            output_audio = gr.Audio(label="Output")
-
-            output_info = gr.HTML(label="Info")
-
-            gr.Examples(
-                examples=examples,
-                fn=process,
-                inputs=[
-                    language_radio,
-                    model_dropdown,
-                    input_text,
-                    input_sid,
-                    input_speed,
-                ],
-                outputs=[
-                    output_audio,
-                    output_info,
-                ],
-            )
-
-        input_button.click(
-            process,
-            inputs=[
-                language_radio,
-                model_dropdown,
-                input_text,
-                input_sid,
-                input_speed,
-            ],
-            outputs=[
-                output_audio,
-                output_info,
-            ],
-        )
-
-    gr.Markdown(description)
-
-
-def download_espeak_ng_data():
-    os.system(
-        """
-    cd /tmp
-    wget -qq https://github.com/k2-fsa/sherpa-onnx/releases/download/tts-models/espeak-ng-data.tar.bz2
-    tar xf espeak-ng-data.tar.bz2
-    """
-    )
-
-
-# --- Deuxième script : Chat via InferenceClient ---
-
 client = InferenceClient("google/gemma-1.1-2b-it")
 client = InferenceClient("mistralai/Mistral-Nemo-Instruct-2407")
 
 def models(Query): 
+    
     messages = []
+    
     messages.append({"role": "user", "content": f"[SYSTEM] You are ASSISTANT who answer question asked by user in short and concise manner. [USER] {Query}"})
+
     Response = ""
+
     for message in client.chat_completion(
         messages,
         max_tokens=2048,
         stream=True
     ):
         token = message.choices[0].delta.content
+
         Response += token
         yield Response
 
@@ -286,32 +53,23 @@ Example format:
 <answer> [Final Answer] </answer> (must give final answer in this format)
 <reflection> [Evaluation of the solution] </reflection>
 <reward> [Float between 0.0 and 1.0] </reward> [/INST] [INST] [QUERY] {query} [/INST] [ASSISTANT] """
+
     stream = client.text_generation(message, max_new_tokens=4096, stream=True, details=True, return_full_text=False)
     output = ""
+
     for response in stream:
         output += response.token.text
     return output
 
-description_chat = "# Light ChatBox\n### Enter a question and.. Tada this reponse generate in 0.5 second!"
+description="# Light ChatBox\n### Enter a question and.. Tada this reponse generate in 0.5 second!"
 
 with gr.Blocks() as demo1:
-    gr.Interface(description=description_chat, fn=models, inputs=["text"], outputs="text")
-
+    gr.Interface(description=description,fn=models, inputs=["text"], outputs="text")
 with gr.Blocks() as demo2:
-    gr.Interface(description="Very low but critical thinker", fn=nemo, inputs=["text"], outputs="text", api_name="critical_thinker", concurrency_limit=10)
-
-with gr.Blocks() as demo_chat:
-    gr.TabbedInterface([demo1, demo2], ["Fast", "Critical"])
-
-
-# --- Interface globale combinée ---
-with gr.Blocks() as demo_combined:
-    gr.Markdown("# Application combinée")
-    gr.TabbedInterface([demo_tts, demo_chat], ["TTS", "Chat"])
-
-demo_combined.queue(max_size=300000)
+    gr.Interface(description="Very low but critical thinker",fn=nemo, inputs=["text"], outputs="text", api_name="critical_thinker", concurrency_limit=10)
 
+with gr.Blocks() as demo:
+    gr.TabbedInterface([demo1, demo2] , ["Fast", "Critical"])
 
-if __name__ == "__main__":
-    download_espeak_ng_data()
-    demo_combined.launch()
+demo.queue(max_size=300000)
+demo.launch()