app.py

#llama 3.2 3b IT
import os
from threading import Thread
from typing import Iterator

import gradio as gr
#import spaces
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer

DESCRIPTION = """\
# Llama 3.2 3B Instruct
Llama 3.2 3B is Meta's latest iteration of open LLMs.
This is a demo of [`meta-llama/Llama-3.2-3B-Instruct`](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct), fine-tuned for instruction following.
For more details, please check [our post](https://huggingface.co/blog/llama32).
"""

MAX_MAX_NEW_TOKENS = 2048
DEFAULT_MAX_NEW_TOKENS = 1024
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# Download model from Huggingface Hub
# Change this to meta-llama or the correct org name from Huggingface Hub
model_id = "meta-llama/Llama-3.2-3B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.bfloat16,
)
model.eval()

# Main Gradio inference function
def generate(
    message: str,
    chat_history: list[tuple[str, str]],
    max_new_tokens: int = 1024,
    temperature: float = 0.6,
    top_p: float = 0.9,
    top_k: int = 50,
    repetition_penalty: float = 1.2,
) -> Iterator[str]:

    conversation = [{k: v for k, v in d.items() if k != 'metadata'} for d in chat_history]
    conversation.append({"role": "user", "content": message})

    input_ids = tokenizer.apply_chat_template(conversation, add_generation_prompt=True, 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=20.0, skip_prompt=True, skip_special_tokens=True)
    generate_kwargs = dict(
        {"input_ids": input_ids},
        streamer=streamer,
        max_new_tokens=max_new_tokens,
        do_sample=True,
        top_p=top_p,
        top_k=top_k,
        temperature=temperature,
        num_beams=1,
        repetition_penalty=repetition_penalty,
    )
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()

    conversation.append({"role": "assistant", "content": ""})
    outputs = []
    for text in streamer:
        outputs.append(text)
        bot_response = "".join(outputs)
        conversation[-1]['content'] = bot_response
        yield "", conversation


# Implementing Gradio 5 features and building a ChatInterface UI yourself
PLACEHOLDER = """<div style="padding: 20px; text-align: center; display: flex; flex-direction: column; align-items: center;">
   <img src="https://ysharma-dummy-chat-app.hf.space/file=/tmp/gradio/c21ff9c8e7ecb2f7d957a72f2ef03c610ac7bbc4/Meta_lockup_positive%20primary_RGB_small.jpg" style="width: 80%; max-width: 550px; height: auto; opacity: 0.55; margin-bottom: 10px;">
   <h1 style="font-size: 28px; margin: 0;">Meta llama3.2</h1>
   <p style="font-size: 18px; margin: 5px 0 0; opacity: 0.65;">
       <a href="https://huggingface.co/blog/llama32" target="_blank" style="color: inherit; text-decoration: none;">Learn more about Llama 3.2</a>
   </p>
</div>"""


def handle_retry(history, retry_data: gr.RetryData):
    new_history = history[:retry_data.index]
    previous_prompt = history[retry_data.index]['content']
    yield from generate(previous_prompt, chat_history = new_history, max_new_tokens = 1024, temperature = 0.6, top_p = 0.9, top_k = 50, repetition_penalty = 1.2)

def handle_like(data: gr.LikeData):
    if data.liked:
        print("You upvoted this response: ", data.value)
    else:
        print("You downvoted this response: ", data.value)

def handle_undo(history, undo_data: gr.UndoData):
    chatbot = history[:undo_data.index]
    prompt = history[undo_data.index]['content']
    return chatbot, prompt

def chat_examples_fill(data: gr.SelectData):
    yield from generate(data.value['text'], chat_history = [], max_new_tokens = 1024, temperature = 0.6, top_p = 0.9, top_k = 50, repetition_penalty = 1.2)


with gr.Blocks(theme=gr.themes.Soft(), fill_height=True) as demo:
    with gr.Column(elem_id="container", scale=1):
        chatbot = gr.Chatbot(
            label="Llama3.2 3B Instruct Chatbotw using Gradio 5",
            show_label=False,
            type="messages",
            scale=1,
            suggestions = [
                {"text": "How many R are there in a Strawberry?"},
                {"text": "What is the meaning of life for an AI?"},
                {"text": "Are tomatoes vegetables?"},
                {"text": "There's a llama in my garden 😱 What should I do?"},
                {"text": "What is the best way to open a can of worms?"},
                {"text": "The odd numbers in this group add up to an even number: 15, 32, 5, 13, 82, 7, 1. "},
                {"text": 'How to setup a human base on Mars? Give short answer.'},
                {"text": 'Explain theory of relativity to me like I’m 8 years old.'},
                {"text": 'What is 9,000 * 9,000?'},
                {"text": 'Write a pun-filled happy birthday message to my friend Alex.'},
                {"text": 'Justify why a penguin might make a good king of the jungle.'}
                ],
            placeholder = PLACEHOLDER,
            )

    msg = gr.Textbox(submit_btn=True, show_label=False)
    with gr.Accordion('Additional inputs', open=False):
        max_new_tokens = gr.Slider(label="Max new tokens", minimum=1, maximum=10, step=1, value=23, )
        temperature = gr.Slider(label="Temperature",minimum=0.1, maximum=4.0, step=0.1, value=0.6,)
        top_p = gr.Slider(label="Top-p (nucleus sampling)", minimum=0.05, maximum=1.0, step=0.05, value=0.9, )
        top_k = gr.Slider(label="Top-k", minimum=1, maximum=1000, step=1, value=50, )
        repetition_penalty = gr.Slider(label="Repetition penalty", minimum=1.0, maximum=2.0, step=0.05, value=1.2, )

    msg.submit(generate, [msg, chatbot, max_new_tokens, temperature, top_p, top_k, repetition_penalty], [msg, chatbot])
    chatbot.retry(handle_retry, chatbot, [msg, chatbot])
    chatbot.like(handle_like, None, None)
    chatbot.undo(handle_undo, chatbot, [chatbot, msg])
    chatbot.suggestion_select(chat_examples_fill, None, [msg, chatbot] )


demo.launch()