app.py

from threading import Thread
from typing import Iterator

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

MAX_MAX_NEW_TOKENS = 256
DEFAULT_MAX_NEW_TOKENS = 1024
MAX_INPUT_TOKEN_LENGTH = 10240

DESCRIPTION = """\
# CLEX-7B-Chat-16K

This Space demonstrates model [CLEX-7B-Chat-16K](https://huggingface.co/DAMO-NLP-SG/CLEX-7B-Chat-16K), a Llama-2-7B model fine-tuned using our [CLEX](https://arxiv.org/abs/2310.16450) method. Feel free to play with it, or duplicate to run generations without a queue! If you want to run your own service, you can also [deploy the model on Inference Endpoints](https://huggingface.co/inference-endpoints).

The model supports the maximun input sequence length of 64k now.

"""

# LICENSE = """
# <p/>

# ---
# As a derivate work of [Llama-2-7b-chat](https://huggingface.co/meta-llama/Llama-2-7b-chat) by Meta,
# this demo is governed by the original [license](https://huggingface.co/spaces/huggingface-projects/llama-2-7b-chat/blob/main/LICENSE.txt) and [acceptable use policy](https://huggingface.co/spaces/huggingface-projects/llama-2-7b-chat/blob/main/USE_POLICY.md).
# """


CITE = """
If you find our project useful, hope you can star our repo and cite our paper as follows:
```
@article{damonlpsg2023clex,
  author = {Chen, Guanzheng and Li, Xin and Meng, Zaiqiao and Liang, Shangsong and Bing, Lidong},
  title = {CLEX: Continuous Length Extrapolation for Large Language Models},
  year = 2023,
  url = {https://arxiv.org/abs/2310.16450}
}
```
"""

if not torch.cuda.is_available():
    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"


# if torch.cuda.is_available():
model_id = "DAMO-NLP-SG/CLEX-7b-Chat-16K"
from transformers import AutoModelForCausalLM
from modeling_llama import LlamaForCausalLM
# from configuration_clex import CLEXLlamaConfig
# config = CLEXLlamaConfig.from_pretrained(
#         model_id
#     )
model = LlamaForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, trust_remote_code=True, low_cpu_mem_usage=True, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True, use_fast=False)
tokenizer.use_default_system_prompt = False

import PyPDF2
from io import BytesIO

def process_pdf(input_pdf):
    # Read the binary data from the input_pdf
    # pdf_data = BytesIO(input_pdf)
    # if pdf_data.getvalue().strip() == b'':
    #     return ""
    # Create a PDF reader object
    reader = PyPDF2.PdfReader(input_pdf.name)
    # Extract the text from each page of the PDF
    text = ""
    for page in reader.pages:
        text += page.extract_text()
    # Close the PDF reader and reset the pointer
    # reader.close()
    # pdf_data.seek(0)
    # Return the extracted text
    return text



def build_chat():
    from fastchat.model import get_conversation_template
    conv = get_conversation_template("vicuna")
    conv.append_message(conv.roles[0], prompt)
    conv.append_message(conv.roles[1], None)
    prompt = conv.get_prompt() 
    return prompt

from fastchat.model import get_conversation_template

@spaces.GPU
def generate(
    message: str,
    chat_history: list[tuple[str, str]],
    system_prompt: 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]:
    conv = get_conversation_template("vicuna")
    conv.append_message(conv.roles[0], message)
    conv.append_message(conv.roles[1], None)
    prompt = conv.get_prompt()
    # if system_prompt:
    #     conversation.append({"role": "system", "content": system_prompt})
    # for user, assistant in chat_history:
    #     conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])
    # conversation.append({"role": "user", "content": message})

    # chat = tokenizer.apply_chat_template(conversation, tokenize=False)
    inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
    if len(inputs) > MAX_INPUT_TOKEN_LENGTH:
        inputs = inputs[-MAX_INPUT_TOKEN_LENGTH:]
        gr.Warning("Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")

    streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
    generate_kwargs = dict(
        inputs,
        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()

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


def generate_with_pdf(
    message: str,
    chat_history: list[tuple[str, str]],
    system_prompt: str,
    input_pdf: BytesIO = None,
    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]:
    if input_pdf is not None:
        pdf_text = process_pdf(input_pdf)
        # print(pdf_text)
        message += f"\nThis is the beginning of a pdf\n{pdf_text}This is the end of a pdf\n"
    yield from generate(
        message,
        chat_history,
        system_prompt,
        max_new_tokens,
        temperature,
        top_p,
        top_k,
        repetition_penalty
    )

chat_interface = gr.ChatInterface(
    fn=generate_with_pdf,
    additional_inputs=[
        gr.Textbox(label="System prompt", lines=6),
        gr.File(label="PDF File", accept=".pdf"),
        gr.Slider(
            label="Max new tokens",
            minimum=1,
            maximum=MAX_MAX_NEW_TOKENS,
            step=1,
            value=DEFAULT_MAX_NEW_TOKENS,
        ),
        gr.Slider(
            label="Temperature",
            minimum=0.1,
            maximum=4.0,
            step=0.1,
            value=0.6,
        ),
        gr.Slider(
            label="Top-p (nucleus sampling)",
            minimum=0.05,
            maximum=1.0,
            step=0.05,
            value=0.9,
        ),
        gr.Slider(
            label="Top-k",
            minimum=1,
            maximum=1000,
            step=1,
            value=50,
        ),
        gr.Slider(
            label="Repetition penalty",
            minimum=1.0,
            maximum=2.0,
            step=0.05,
            value=1.2,
        ),
    ],
    stop_btn=None,
    examples=[
        ["Hello there! How are you doing?"],
        ["Can you explain briefly to me what is the Python programming language?"],
        ["Explain the plot of Cinderella in a sentence."],
        ["How many hours does it take a man to eat a Helicopter?"],
        ["Write a 100-word article on 'Benefits of Open-Source in AI research'"],
    ],
)



with gr.Blocks(css="style.css") as demo:
    gr.Markdown(DESCRIPTION)
    gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")

    chat_interface.render()
    gr.Markdown(CITE)

if __name__ == "__main__":
    demo.queue(max_size=20).launch(share=False)