| |
|
|
| import gradio as gr |
| from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer |
|
|
| def generate_sequences(model_name, prompt): |
| if model_name == "nferruz/ProtGPT2": |
| protgpt2 = pipeline('text-generation', model="nferruz/ProtGPT2") |
| sequences = protgpt2(prompt, max_length=100, do_sample=True, top_k=950, repetition_penalty=1.2, num_return_sequences=10, eos_token_id=0) |
| return "\n".join([seq['generated_text'] for seq in sequences]) |
| elif model_name == "lightonai/RITA_xl": |
| model = AutoModelForCausalLM.from_pretrained("lightonai/RITA_xl", trust_remote_code=True) |
| tokenizer = AutoTokenizer.from_pretrained("lightonai/RITA_xl") |
| rita_gen = pipeline('text-generation', model=model, tokenizer=tokenizer) |
| sequences = rita_gen(prompt, max_length=20, do_sample=True, top_k=950, repetition_penalty=1.2, num_return_sequences=2, eos_token_id=2) |
| return "\n".join([seq['generated_text'].replace(' ', '') for seq in sequences]) |
| else: |
| return "Model not supported" |
|
|
| model_options = ["nferruz/ProtGPT2", "lightonai/RITA_xl"] |
|
|
| gr.Interface( |
| fn=generate_sequences, |
| inputs=[ |
| gr.Dropdown(model_options, label="Select Model"), |
| gr.Textbox(lines=2, placeholder="Enter your prompt here...", label="Prompt") |
| ], |
| outputs="text", |
| title="Novel Protein Sequence Generation", |
| description="Generate sequences using selected protein language models." |
| ).launch() |
|
|
