app.py

import gradio as gr
import os
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from threading import Thread
from unsloth.chat_templates import get_chat_template
from unsloth import FastLanguageModel
import torch


PLACEHOLDER = """
<div style="padding: 30px; text-align: center; display: flex; flex-direction: column; align-items: center;">

</div>
"""

css = """
h1 {
  text-align: center;
  display: block;
}
#duplicate-button {
  margin: auto;
  color: white;
  background: #1565c0;
  border-radius: 100vh;
}
"""

max_seq_length = 2048  # Choose any! We auto support RoPE Scaling internally!
dtype = None  # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True  # Use 4bit quantization to reduce memory usage. Can be False.

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="umair894/llama3",
    max_seq_length=max_seq_length,
    dtype=dtype,
    load_in_4bit=load_in_4bit,
    
)
FastLanguageModel.for_inference(model)

# Apply chat template to the tokenizer
tokenizer = get_chat_template(
    tokenizer,
    chat_template="llama-3",  # Supports zephyr, chatml, mistral, llama, alpaca, vicuna, vicuna_old, unsloth
    mapping={"role": "from", "content": "value", "user": "human", "assistant": "gpt"},  # ShareGPT style
    map_eos_token=True,  # Maps to </s> instead
)

terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("")
]

# Check if terminators are None and provide a default value if needed
terminators = [token_id for token_id in terminators if token_id is not None]
if not terminators:
    terminators = [tokenizer.eos_token_id]  # Ensure there is a valid EOS token

def chat_llama3_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([{"from": "human", "value": user}, {"from": "gpt", "value": assistant}])
    conversation.append({"from": "human", "value": message})

    input_ids = tokenizer.apply_chat_template(
        conversation,
        tokenize=True,
        add_generation_prompt=True,  # Must add for generation
        return_tensors="pt",
    ).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=True,
        temperature=temperature,
        eos_token_id=terminators,
    )

    if temperature == 0:
        generate_kwargs['do_sample'] = False
        
    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.ChatInterface(
        fn=chat_llama3_8b,
        chatbot=chatbot,
        fill_height=True,
        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=[
            ['How can i file for a student loan case?']
        ],
        cache_examples=False,
    )
    
    
if __name__ == "__main__":
    demo.launch(debug=True)