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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
A demo to generate a protein's funtion with its amino acid sequence using [ESM2Text Base v1.1](https://huggingface.co/habdine/Esm2Text-Base-v1-1). To test this model, only enter below, the amino acid sequence of the protein without any spaces.
"""
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()
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