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import gradio as gr
from evodiff.pretrained import OA_DM_38M, D3PM_UNIFORM_38M, MSA_OA_DM_MAXSUB
from evodiff.generate import generate_oaardm, generate_d3pm
from evodiff.generate_msa import generate_query_oadm_msa_simple
import re
def a3m_file(file):
return "tmp.a3m"
def make_uncond_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
def make_cond_seq(seq_len, msa_file, model_type):
if model_type == "EvoDiff-MSA":
checkpoint = MSA_OA_DM_MAXSUB()
model, collater, tokenizer, scheme = checkpoint
tokeinzed_sample, generated_sequence = generate_query_oadm_msa_simple(msa_file, model, tokenizer, n_sequences=64, seq_length=seq_len, device='cpu', selection_type='random')
return generated_sequence
usg_app = gr.Interface(
fn=make_uncond_seq,
inputs=[
gr.Slider(10, 100, label = "Sequence Length"),
gr.Dropdown(["EvoDiff-Seq-OADM 38M", "EvoDiff-D3PM-Uniform 38M"], type="value", label = "Model")
],
outputs="text",
title = "Unconditional sequence generation",
description="Generate a sequence with `EvoDiff-Seq-OADM 38M` (smaller/faster) or `EvoDiff-D3PM-Uniform 38M` (larger/slower) models."
)
csg_app = gr.Interface(
fn=make_cond_seq,
inputs=[
gr.Slider(10, 100, label = "Sequence Length"),
gr.File(file_types=["a3m"], label = "MSA File"),
gr.Dropdown(["EvoDiff-MSA"], type="value", label = "Model")
],
outputs="text",
title = "Conditional sequence generation",
description="Evolutionary guided sequence generation with the `EvoDiff-MSA` model."
)
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](httos://github.com/colbyford)
"""
)
with gr.Row():
gr.TabbedInterface([usg_app, csg_app], ["Unconditional sequence generation", "Conditional generation"])
if __name__ == "__main__":
edapp.launch() |