app.py

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

# Set an environment variable
HF_TOKEN = os.environ.get("HF_TOKEN", None)


DESCRIPTION = '''
<div>
<h1 style="text-align: center;">ContenteaseAI custom trained model</h1>
</div>
'''

LICENSE = """
<p/>

---
For more information, visit our [website](https://contentease.ai).
"""

PLACEHOLDER = """
<div style="padding: 30px; text-align: center; display: flex; flex-direction: column; align-items: center;">
   <h1 style="font-size: 28px; margin-bottom: 2px; opacity: 0.55;">ContenteaseAI Custom AI trained model</h1>
   <p style="font-size: 18px; margin-bottom: 2px; opacity: 0.65;">Enter the text extracted from the PDF:</p>
</div>
"""


css = """
h1 {
  text-align: center;
  display: block;
}

"""
# Load the tokenizer and model with quantization
model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16
)

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    quantization_config=bnb_config,
    torch_dtype=torch.bfloat16
)
model.generation_config.pad_token_id = tokenizer.pad_token_id

terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

SYS_PROMPT = """
Extract all relevant keywords and add quantity from the following text and format the result in nested JSON, ignoring personal details and focusing only on the scope of work as shown in the example:
Good JSON example: {'lobby': {'frcm': {'replace': {'carpet': 1, 'carpet_pad': 1, 'base': 1, 'window_treatments': 1, 'artwork_and_decorative_accessories': 1, 'portable_lighting': 1, 'upholstered_furniture_and_decorative_pillows': 1, 'millwork': 1} } } }
Bad JSON example: {'lobby': { 'frcm': { 'replace': [ 'carpet', 'carpet_pad', 'base', 'window_treatments', 'artwork_and_decorative_accessories', 'portable_lighting', 'upholstered_furniture_and_decorative_pillows', 'millwork'] } } }
Make sure to fetch details from the provided text and ignore unnecessary information. The response should be in JSON format only, without any additional comments.
"""

@spaces.GPU(duration=120)
def chat_llama3_8b(message: str, history: list, temperature: float, max_new_tokens: int):
    """
    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 = [{"role": "system", "content": SYS_PROMPT}]
    
    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").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,
        pad_token_id=tokenizer.eos_token_id
    )
    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.Markdown(DESCRIPTION)
    
    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=9012, step=1, value=512, label="Max new tokens", render=False),
        ]
    )
    
    gr.Markdown(LICENSE)
    
if __name__ == "__main__":
    demo.launch(show_error=True, debug=True)