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# app.py
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="Sequence Generation with Transformers",
description="Generate sequences using selected transformer models."
).launch()
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