app.py

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

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# 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
)

@lru_cache(maxsize=1)
def load_model_and_tokenizer():
    try:
        start_time = time.time()
        logger.info("Loading tokenizer...")
        tokenizer = AutoTokenizer.from_pretrained(model_id)
        logger.info("Loading model...")
        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
        end_time = time.time()
        logger.info(f"Model and tokenizer loaded successfully in {end_time - start_time} seconds.")
        return model, tokenizer
    except Exception as e:
        logger.error(f"Error loading model or tokenizer: {e}")
        raise

try:
    model, tokenizer = load_model_and_tokenizer()
except Exception as e:
    logger.error(f"Failed to load model and tokenizer: {e}")
    raise

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.
"""

def chunk_text(text, chunk_size=5000):
    """
    Splits the input text into chunks of specified size.
    
    Args:
        text (str): The input text to be chunked.
        chunk_size (int): The size of each chunk in tokens.
        
    Returns:
        list: A list of text chunks.
    """
    words = text.split()
    chunks = [' '.join(words[i:i + chunk_size]) for i in range(0, len(words), chunk_size)]
    return chunks

def combine_responses(responses):
    """
    Combines the responses from all chunks into a final output string.
    
    Args:
        responses (list): A list of responses from each chunk.
        
    Returns:
        str: The combined output string.
    """
    combined_output = " ".join(responses)
    return combined_output

def generate_response_for_chunk(chunk, history, temperature, max_new_tokens):
    start_time = time.time()
    
    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": chunk})

    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)
    
    end_time = time.time()
    logger.info(f"Time taken for generating response for a chunk: {end_time - start_time} seconds")
    
    return "".join(outputs)

@spaces.GPU(duration=110)
def chat_llama3_8b(message: str, history: list, temperature: float, max_new_tokens: int):
    """
    Generate a streaming response using the llama3-8b model with chunking.
    
    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.
    """
    try:
        start_time = time.time()
        
        chunks = chunk_text(message)
        responses = []
        for chunk in chunks:
            response = generate_response_for_chunk(chunk, history, temperature, max_new_tokens)
            responses.append(response)
        final_output = combine_responses(responses)
        
        end_time = time.time()
        logger.info(f"Total time taken for generating response: {end_time - start_time} seconds")
        
        yield final_output
    except Exception as e:
        logger.error(f"Error generating response: {e}")
        yield "An error occurred while generating the response. Please try again."

# 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=2000, step=1, value=700, label="Max new tokens", render=False),
        ]
    )
    
    gr.Markdown(LICENSE)
    
if __name__ == "__main__":
    try:
        demo.launch(show_error=True)
    except Exception as e:
        logger.error(f"Error launching Gradio demo: {e}")