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import os |
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from threading import Thread |
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from typing import Iterator |
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import gradio as gr |
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import spaces |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer |
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DESCRIPTION = """\ |
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# Prot2Text Demo |
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A demo to generate a protein's funtion with its amino acid sequence and its structure using [Prot2Text Base v1.1](https://huggingface.co/habdine/Prot2Text-Base-v1-1). To test this model, only enter below, the AlphaFoldDB ID of the protein. |
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""" |
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MAX_MAX_NEW_TOKENS = 256 |
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DEFAULT_MAX_NEW_TOKENS = 100 |
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") |
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tokenizer = AutoTokenizer.from_pretrained('habdine/Prot2Text-Base-v1-1', |
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trust_remote_code=True) |
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model = AutoModelForCausalLM.from_pretrained('habdine/Prot2Text-Base-v1-1', |
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trust_remote_code=True).to(device) |
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model.eval() |
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@spaces.GPU(duration=90) |
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def generate( |
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message: str, |
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chat_history: list[dict], |
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max_new_tokens: int = 1024, |
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do_sample: bool = False, |
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temperature: float = 0.6, |
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top_p: float = 0.9, |
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top_k: int = 50, |
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repetition_penalty: float = 1.2, |
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) -> Iterator[str]: |
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streamer = TextIteratorStreamer(tokenizer, timeout=20.0, skip_prompt=True, skip_special_tokens=True) |
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generate_kwargs = dict( |
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protein_sequence=message, |
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tokenizer=tokenizer, |
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device=device, |
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streamer=streamer, |
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max_new_tokens=max_new_tokens, |
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do_sample=do_sample, |
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top_p=top_p, |
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top_k=top_k, |
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temperature=temperature, |
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num_beams=1, |
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repetition_penalty=repetition_penalty, |
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) |
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t = Thread(target=model.generate_protein_description, kwargs=generate_kwargs) |
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t.start() |
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outputs = [] |
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for text in streamer: |
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outputs.append(text) |
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yield "".join(outputs) |
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chat_interface = gr.ChatInterface( |
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fn=generate, |
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additional_inputs=[ |
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gr.Slider( |
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label="Max new tokens", |
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minimum=1, |
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maximum=MAX_MAX_NEW_TOKENS, |
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step=1, |
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value=DEFAULT_MAX_NEW_TOKENS, |
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), |
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gr.Checkbox(label="Do Sample"), |
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gr.Slider( |
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label="Temperature", |
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minimum=0.1, |
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maximum=4.0, |
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step=0.1, |
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value=0.6, |
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), |
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gr.Slider( |
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label="Top-p (nucleus sampling)", |
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minimum=0.05, |
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maximum=1.0, |
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step=0.05, |
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value=0.9, |
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), |
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gr.Slider( |
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label="Top-k", |
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minimum=1, |
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maximum=1000, |
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step=1, |
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value=50, |
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), |
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gr.Slider( |
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label="Repetition penalty", |
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minimum=1.0, |
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maximum=2.0, |
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step=0.05, |
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value=1.0, |
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), |
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], |
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stop_btn=None, |
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examples=[ |
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['P0A0V1'], |
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["Q10MK9"], |
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["A0A0P0W604"], |
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["Q6K5W5"], |
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["Q65WY8"] |
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], |
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cache_examples=False, |
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type="messages", |
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) |
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with gr.Blocks(css_paths="style.css", fill_height=True) as demo: |
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gr.Markdown(DESCRIPTION) |
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gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button") |
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chat_interface.render() |
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if __name__ == "__main__": |
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demo.queue(max_size=20).launch() |
