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