app.py

import gradio as gr
import os
import spaces
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from threading import Thread


TITLE = '''
<h1 style="text-align: center;">Meta Llama3.1 8B <a href="https://huggingface.co/spaces/ysharma/Chat_with_Meta_llama3_1_8b?duplicate=true" id="duplicate-button"><button style="color:white">Duplicate this Space</button></a></h1>
'''

DESCRIPTION = '''
<div>
<p>This Space demonstrates the instruction-tuned model <a href="https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct"><b>Meta Llama3.1 8b Chat</b></a>. Feel free to play with this demo, or duplicate to run privately!</p>
<p>🔨 Interested in trying out more powerful Instruct versions of Llama3.1? Check out the <a href="https://huggingface.co/chat/"><b>Hugging Chat</b></a> integration for 🐘 Meta Llama 3.1 70b, and 🦕 Meta Llama 3.1 405b</p>
<p>🔎 For more details about the Llama3.1 release and how to use the model with <code>transformers</code>, take a look <a href="https://huggingface.co/blog/llama31">at our blog post</a>.</p>
</div>
'''

LICENSE = """
<p/>
---
Built with Llama
"""

PLACEHOLDER = """
<div style="padding: 30px; text-align: center; display: flex; flex-direction: column; align-items: center;">
   <img src="https://ysharma-dummy-chat-app.hf.space/file=/tmp/gradio/8e75e61cc9bab22b7ce3dec85ab0e6db1da5d107/Meta_lockup_positive%20primary_RGB.jpg" style="width: 80%; max-width: 550px; height: auto; opacity: 0.55;  "> 
   <h1 style="font-size: 28px; margin-bottom: 2px; opacity: 0.55;">Meta llama3.1</h1>
   <p style="font-size: 18px; margin-bottom: 2px; opacity: 0.65;">Ask me anything...</p>
</div>
"""

css = """
h1 {
  text-align: center;
  display: block;
  display: flex;
    align-items: center;
    justify-content: center;
}

#duplicate-button {
  margin-left: 10px;
  color: white;
  background: #1565c0;
  border-radius: 100vh;
  font-size: 1rem;
  padding: 3px 5px;
}
"""

model_id = "meta-llama/Meta-Llama-3.1-8B-Instruct"

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

MAX_INPUT_TOKEN_LENGTH = 4096

# Gradio inference function
@spaces.GPU(duration=120)
def chat_llama3_1_8b(message: str, 
              history: list, 
              temperature: float, 
              max_new_tokens: int
             ) -> str:
    """
    Generate a streaming response using the llama3-8b model.
    Args:
        message (str): The input message.
        history (list): The conversation history used by ChatInterface.
        temperature (float): The temperature for generating the response.
        max_new_tokens (int): The maximum number of new tokens to generate.
    Returns:
        str: The generated response.
    """
    conversation = []
    for user, assistant in history:
        conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])
    conversation.append({"role": "user", "content": message})

    input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt")
    if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
        input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
        gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
    input_ids = input_ids.to(model.device)
    
    streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)

    generate_kwargs = dict(
        input_ids= input_ids,
        streamer=streamer,
        max_new_tokens=max_new_tokens,
        do_sample=temperature != 0,  # This will enforce greedy generation (do_sample=False) when the temperature is passed 0, avoiding the crash.
        temperature=temperature,
        eos_token_id=terminators,
    )

    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()

    outputs = []
    for text in streamer:
        outputs.append(text)
        yield "".join(outputs)
        

# Gradio block
chatbot=gr.Chatbot(height=450, placeholder=PLACEHOLDER, label='Gradio ChatInterface')

with gr.Blocks(fill_height=True, css=css) as demo:

    gr.Markdown(TITLE)
    gr.Markdown(DESCRIPTION)
    #gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")
    gr.ChatInterface(
        fn=chat_llama3_1_8b,
        chatbot=chatbot,
        fill_height=True,
        examples_per_page=3,
        additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
        additional_inputs=[
            gr.Slider(minimum=0,
                      maximum=1, 
                      step=0.1,
                      value=0.95, 
                      label="Temperature", 
                      render=False),
            gr.Slider(minimum=128, 
                      maximum=4096,
                      step=1,
                      value=512, 
                      label="Max new tokens", 
                      render=False ),
            ],
        examples=[
            ["There's a llama in my garden 😱 What should I do?"],
            ["What is the best way to open a can of worms?"],
            ["The odd numbers in this group add up to an even number: 15, 32, 5, 13, 82, 7, 1. "],
            ['How to setup a human base on Mars? Give short answer.'],
            ['Explain theory of relativity to me like I’m 8 years old.'],
            ['What is 9,000 * 9,000?'],
            ['Write a pun-filled happy birthday message to my friend Alex.'],
            ['Justify why a penguin might make a good king of the jungle.']
            ],
        cache_examples=False,
                     )
    
    gr.Markdown(LICENSE)
    
if __name__ == "__main__":
    demo.launch()