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import streamlit as st
from predict_chromosome import predict_and_write
st.title("DeepLoop")
st.write("Looking for some example data?")
st.write(
"http://hiview.case.edu/ssz20/tmp.HiCorr.ref/HiCorr_test_data/HiCorr_output.tar.gz"
)
st.write("Use hg19, HindIII, chr11, CPGZ and LoopDenoise for the Demo data")
#########
# INPUT #
#########
HiCorr_data_hf_repo = st.text_input(
"HiCorr Data 🤗 Dataset", default="funlab/HiCorr_test_data"
)
training_set = st.selectbox("Select Training Set", ["CPGZ", "H9"], index=0)
depth = st.selectbox("Select Depth", ["LoopDenoise", "50M", "101K"], index=0)
# TODO Throw a warning that h9 only has LoopDenoise and 100M
chromosome = st.selectbox("Select Chromosome", ["chr11"])
prefix = "results"
genome = st.selectbox(
"Reference Genome",
[
"hg19",
"hg38",
"mm10",
],
)
digestion_enzyme = st.selectbox(
"Digestion Enzyme",
[
"HindIII",
"Arima",
# FIXME "microC_5kb_bin"
"DPNII",
],
index=0,
)
# TODO Add the other toggles
# small_matrix_size
# step_size
# max_dist
# dummy
# val_cols
# keep_zeros
# Load the model from hugging face
from huggingface_hub import from_pretrained_keras
model = from_pretrained_keras(f"funlab/DeepLoop-{training_set}-{depth}")
from huggingface_hub import snapshot_download
HiCorr_data = snapshot_download(HiCorr_data_hf_repo, repo_type="dataset")
anchors = snapshot_download(
repo_id=f"funlab/{genome}_{digestion_enzyme}_anchor_bed", repo_type="dataset"
)
if st.button("Run Deeploop", type="primary"):
denoised_anchor_to_anchor = None
with st.spinner("Running the model"):
denoised_anchor_to_anchor = predict_and_write(
model,
full_matrix_dir=HiCorr_data + f"/anchor_2_anchor.loop.{chromosome}",
input_name=HiCorr_data + f"/anchor_2_anchor.loop.{chromosome}.p_val",
outdir=prefix,
anchor_dir=anchors,
chromosome=chromosome,
small_matrix_size=128,
step_size=128,
dummy=5,
# max_dist,
val_cols=["obs", "exp", "pval"],
# keep_zeros,
)
st.success("Done!")
# Print and list the files
st.download_button(
label="Download Denoised Results",
data=os.path.join(prefix, chromosome + ".denoised.anchor.to.anchor"),
file_name=chromosome + ".denoised.anchor.to.anchor",
mime="text/csv",
)
st.dataframe(denoised_anchor_to_anchor)
st.write(os.listdir(prefix))
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