|
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 = """\ |
|
# ESM2Text Demo |
|
""" |
|
|
|
MAX_MAX_NEW_TOKENS = 256 |
|
DEFAULT_MAX_NEW_TOKENS = 100 |
|
|
|
|
|
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") |
|
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained('habdine/Esm2Text-Base-v1-1', |
|
trust_remote_code=True) |
|
model = AutoModelForCausalLM.from_pretrained('habdine/Esm2Text-Base-v1-1', |
|
trust_remote_code=True).to(device) |
|
model.eval() |
|
|
|
|
|
@spaces.GPU(duration=90) |
|
def generate( |
|
message: str, |
|
chat_history: list[dict], |
|
max_new_tokens: int = 1024, |
|
do_sample: bool = False, |
|
temperature: float = 0.6, |
|
top_p: float = 0.9, |
|
top_k: int = 50, |
|
repetition_penalty: float = 1.2, |
|
) -> Iterator[str]: |
|
|
|
|
|
streamer = TextIteratorStreamer(tokenizer, timeout=20.0, skip_prompt=True, skip_special_tokens=True) |
|
generate_kwargs = dict( |
|
protein_sequence=message, |
|
tokenizer=tokenizer, |
|
device=device, |
|
streamer=streamer, |
|
max_new_tokens=max_new_tokens, |
|
do_sample=do_sample, |
|
top_p=top_p, |
|
top_k=top_k, |
|
temperature=temperature, |
|
num_beams=1, |
|
repetition_penalty=repetition_penalty, |
|
) |
|
t = Thread(target=model.generate_protein_description, kwargs=generate_kwargs) |
|
t.start() |
|
|
|
outputs = [] |
|
for text in streamer: |
|
outputs.append(text) |
|
yield "".join(outputs) |
|
|
|
|
|
chat_interface = gr.ChatInterface( |
|
fn=generate, |
|
additional_inputs=[ |
|
gr.Slider( |
|
label="Max new tokens", |
|
minimum=1, |
|
maximum=MAX_MAX_NEW_TOKENS, |
|
step=1, |
|
value=DEFAULT_MAX_NEW_TOKENS, |
|
), |
|
gr.Checkbox(label="Do Sample"), |
|
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=1.0, |
|
), |
|
], |
|
stop_btn=None, |
|
examples=[ |
|
['AEQAERYEEMVEFMEKL'], |
|
["MAVVLPAVVEELLSEMAAAVQESARIPDEYLLSLKFLFGSSATQALDLVDRQSITLISSPSGRRVYQVLGSSSKTYTCLASCHYCSCPAFAFSVLRKSDSILCKHLLAVYLSQVMRTCQQLSVSDKQLTDILLMEKKQEA"], |
|
], |
|
cache_examples=False, |
|
type="messages", |
|
) |
|
|
|
with gr.Blocks(css_paths="style.css", fill_height=True) as demo: |
|
gr.Markdown(DESCRIPTION) |
|
gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button") |
|
chat_interface.render() |
|
|
|
if __name__ == "__main__": |
|
demo.queue(max_size=20).launch() |
|
|
