app.py

from huggingface_hub import InferenceClient
import gradio as gr

client = InferenceClient(
    "mistralai/Mistral-7B-Instruct-v0.1"
)


def format_prompt(message, history):
  prompt = "<s>"
  for user_prompt, bot_response in history:
    prompt += f"[INST] {user_prompt} [/INST]"
    prompt += f" {bot_response}</s> "
  prompt += f"[INST] {message} [/INST]"
  return prompt

def generate(
    prompt, history, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,
):
    temperature = float(temperature)
    if temperature < 1e-2:
        temperature = 1e-2
    top_p = float(top_p)

    generate_kwargs = dict(
        temperature=temperature,
        max_new_tokens=max_new_tokens,
        top_p=top_p,
        repetition_penalty=repetition_penalty,
        do_sample=True,
        seed=42,
    )

    formatted_prompt = format_prompt(prompt, history)

    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
    output = ""

    for response in stream:
        output += response.token.text
        yield output
    return output


additional_inputs=[
    gr.Slider(
        label="Temperature",
        value=0.9,
        minimum=0.0,
        maximum=1.0,
        step=0.05,
        interactive=True,
        info="Higher values produce more diverse outputs",
    ),
    gr.Slider(
        label="Max new tokens",
        value=256,
        minimum=0,
        maximum=1048,
        step=64,
        interactive=True,
        info="The maximum numbers of new tokens",
    ),
    gr.Slider(
        label="Top-p (nucleus sampling)",
        value=0.90,
        minimum=0.0,
        maximum=1,
        step=0.05,
        interactive=True,
        info="Higher values sample more low-probability tokens",
    ),
    gr.Slider(
        label="Repetition penalty",
        value=1.2,
        minimum=1.0,
        maximum=2.0,
        step=0.05,
        interactive=True,
        info="Penalize repeated tokens",
    )
]

css = """
  #mkd {
    height: 200px; 
    overflow: auto; 
    border: 1px solid #ccc; 
  }
"""

with gr.Blocks(css=css) as demo:
  
    gr.ChatInterface(
        generate,
        additional_inputs=additional_inputs,       
        examples = [
            ["🎸 Show full verse, chorus, intro, and outro chords and lyrics for top 3 Everclear songs. 🎤"],
            ["🎵 Show full verse, chorus, intro, and outro chords and lyrics for top 3 Taylor Swift songs. 🎶"],
            ["🎙️ Show full verse, chorus, intro, and outro chords and lyrics for top 3 Adele songs. 🎧"],
            ["🎼 Show full verse, chorus, intro, and outro chords and lyrics for top 3 Bruno Mars songs. 🎷"],
            ["🎹 Show full verse, chorus, intro, and outro chords and lyrics for top 3 Lady Gaga songs. 🎺"],
            ["🎻 Show full verse, chorus, intro, and outro chords and lyrics for top 3 Ed Sheeran songs. 🥁"],
            ["🎤 Show full verse, chorus, intro, and outro chords and lyrics for top 3 Drake songs. 🎶"],
            ["🎧 Show full verse, chorus, intro, and outro chords and lyrics for top 3 Rihanna songs. 🎵"],
            ["🎷 Show full verse, chorus, intro, and outro chords and lyrics for top 3 Justin Bieber songs. 🎼"],
            ["🎶 Show full verse, chorus, intro, and outro chords and lyrics for top 3 Beyoncé songs. 🎙️"],
            ["🎺 Show full verse, chorus, intro, and outro chords and lyrics for top 3 Katy Perry songs. 🎹"],
            ["🥁 Show full verse, chorus, intro, and outro chords and lyrics for top 3 Eminem songs. 🎻"],
            ["🎤 Show full verse, chorus, intro, and outro chords and lyrics for top 3 Ariana Grande songs. 🎧"]
       ]
    )
    gr.HTML("""<h2>🤖 Mistral Chat - Gradio 🤖</h2>
        In this demo, you can chat with <a href='https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1'>Mistral-7B-Instruct</a> model. 💬
        Learn more about the model <a href='https://huggingface.co/docs/transformers/main/model_doc/mistral'>here</a>. 📚
        <h2>🛠 Model Features 🛠</h2>
        <ul>
          <li>🪟 Sliding Window Attention with 128K tokens span</li>
          <li>🚀 GQA for faster inference</li>
          <li>📝 Byte-fallback BPE tokenizer</li>
        </ul>
        <h3>📜 License 📜  Released under Apache 2.0 License</h3>
        <h3>📦 Usage 📦</h3>
        <ul>
          <li>📚 Available on Huggingface Hub</li>
          <li>🐍 Python code snippets for easy setup</li>
          <li>📈 Expected speedups with Flash Attention 2</li>
        </ul>
    """)

    markdown="""
    | Feature | Description | Byline |
    |---------|-------------|--------|
    | 🪟 Sliding Window Attention with 128K tokens span | Enables the model to have a larger context for each token. | Increases model's understanding of context, resulting in more coherent and contextually relevant outputs. |
    | 🚀 GQA for faster inference | Graph Query Attention allows faster computation during inference. | Speeds up the model inference time without sacrificing too much on accuracy. |
    | 📝 Byte-fallback BPE tokenizer | Uses Byte Pair Encoding but can fall back to byte-level encoding. | Allows the tokenizer to handle a wider variety of input text while keeping token size manageable. |
    | 📜 License | Released under Apache 2.0 License | Gives you a permissive free software license, allowing you freedom to use, modify, and distribute the code. |
    | 📦 Usage | | |
    | 📚 Available on Huggingface Hub | The model can be easily downloaded and set up from Huggingface. | Makes it easier to integrate the model into various projects. |
    | 🐍 Python code snippets for easy setup | Provides Python code snippets for quick and easy model setup. | Facilitates rapid development and deployment, especially useful for prototyping. |
    | 📈 Expected speedups with Flash Attention 2 | Upcoming update expected to bring speed improvements. | Keep an eye out for this update to benefit from performance gains. |
# 🛠 Model Features and More 🛠
## Features
- 🪟 Sliding Window Attention with 128K tokens span  
  - **Byline**: Increases model's understanding of context, resulting in more coherent and contextually relevant outputs.
- 🚀 GQA for faster inference  
  - **Byline**: Speeds up the model inference time without sacrificing too much on accuracy.
- 📝 Byte-fallback BPE tokenizer  
  - **Byline**: Allows the tokenizer to handle a wider variety of input text while keeping token size manageable.
- 📜 License: Released under Apache 2.0 License  
  - **Byline**: Gives you a permissive free software license, allowing you freedom to use, modify, and distribute the code.
## Usage 📦
- 📚 Available on Huggingface Hub  
  - **Byline**: Makes it easier to integrate the model into various projects.
- 🐍 Python code snippets for easy setup  
  - **Byline**: Facilitates rapid development and deployment, especially useful for prototyping.
- 📈 Expected speedups with Flash Attention 2  
  - **Byline**: Keep an eye out for this update to benefit from performance gains.
    """
    gr.Markdown(markdown)  
    
            
    def SpeechSynthesis(result):
        documentHTML5='''
        <!DOCTYPE html>
        <html>
        <head>
            <title>Read It Aloud</title>
            <script type="text/javascript">
                function readAloud() {
                    const text = document.getElementById("textArea").value;
                    const speech = new SpeechSynthesisUtterance(text);
                    window.speechSynthesis.speak(speech);
                }
            </script>
        </head>
        <body>
            <h1>🔊 Read It Aloud</h1>
            <textarea id="textArea" rows="10" cols="80">
        '''
        documentHTML5 = documentHTML5 + result
        documentHTML5 = documentHTML5 + '''
            </textarea>
            <br>
            <button onclick="readAloud()">🔊 Read Aloud</button>
        </body>
        </html>
        '''
        gr.HTML(documentHTML5)
        # components.html(documentHTML5, width=1280, height=1024)
        #return result
    SpeechSynthesis(markdown)
    
    
demo.queue().launch(debug=True)