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import gradio as gr
from evodiff.pretrained import OA_DM_38M, D3PM_UNIFORM_38M
from evodiff.generate import generate_oaardm, generate_d3pm
def make_seq(seq_len, model_type):
if model_type == "EvoDiff-Seq-OADM 38M":
checkpoint = OA_DM_38M()
model, collater, tokenizer, scheme = checkpoint
tokeinzed_sample, generated_sequence = generate_oaardm(model, tokenizer, seq_len, batch_size=1, device='cpu')
if model_type == "EvoDiff-D3PM-Uniform 38M":
checkpoint = D3PM_UNIFORM_38M(return_all=True)
model, collater, tokenizer, scheme, timestep, Q_bar, Q = checkpoint
tokeinzed_sample, generated_sequence = generate_d3pm(model, tokenizer, Q, Q_bar, timestep, seq_len, batch_size=1, device='cpu')
return generated_sequence
# iface = gr.Interface(
# fn=make_seq,
# inputs=gr.Slider(10, 100),
# outputs="text"
# )
# iface.launch()
with gr.Blocks() as edapp:
with gr.Row():
gr.Markdown(
"""
# EvoDiff
## Generation of protein sequences and evolutionary alignments via discrete diffusion models
Created By: Microsoft Research [Sarah Alamdari, Nitya Thakkar, Rianne van den Berg, Alex X. Lu, Nicolo Fusi, ProfileAva P. Amini, and Kevin K. Yang]
Spaces App By: Colby T. Ford
"""
)
with gr.Row():
gr.Markdown(
"""
## Unconditional sequence generation
Generate a sequence with EvoDiff-Seq-OADM 38M
""")
gr.Interface(
fn=make_seq,
inputs=[
gr.Slider(10, 100, label = "Sequence Length")
gr.Dropdown(["EvoDiff-Seq-OADM 38M", "EvoDiff-D3PM-Uniform 38M"], type="value"),
gr.()
],
outputs="text"
)
if __name__ == "__main__":
edapp.launch() |