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edmundmiller
commited on
Commit
•
c513aeb
1
Parent(s):
685d6d8
Add a run button
Browse files
app.py
CHANGED
@@ -13,7 +13,9 @@ st.write("Use hg19, HindIII, chr11, CPGZ and LoopDenoise for the Demo data")
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#########
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# INPUT #
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#########
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-
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training_set = st.selectbox("Select Training Set", ["CPGZ", "H9"], index=0)
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depth = st.selectbox("Select Depth", ["LoopDenoise", "50M", "101K"], index=0)
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# TODO Throw a warning that h9 only has LoopDenoise and 100M
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@@ -45,7 +47,6 @@ digestion_enzyme = st.selectbox(
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# val_cols
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# keep_zeros
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-
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# Load the model from hugging face
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from huggingface_hub import from_pretrained_keras
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@@ -53,40 +54,38 @@ model = from_pretrained_keras(f"funlab/DeepLoop-{training_set}-{depth}")
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from huggingface_hub import snapshot_download
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anchors = snapshot_download(
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repo_id=f"funlab/{genome}_{digestion_enzyme}_anchor_bed", repo_type="dataset"
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)
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anchor_dir=anchors,
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chromosome=chromosome,
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small_matrix_size=128,
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step_size=128,
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dummy=5,
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# max_dist,
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val_cols=["obs", "exp", "pval"],
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# keep_zeros,
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)
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st.
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# Print and list the files
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st.download_button(
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label="Download Denoised Results",
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data=os.path.join(prefix, chromosome + ".denoised.anchor.to.anchor"),
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file_name=chromosome + ".denoised.anchor.to.anchor",
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mime="text/csv",
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)
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st.dataframe(denoised_anchor_to_anchor)
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st.write(os.listdir(prefix))
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#########
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# INPUT #
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#########
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HiCorr_data_hf_repo = st.text_input(
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"HiCorr Data 🤗 Dataset", default="funlab/HiCorr_test_data"
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)
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training_set = st.selectbox("Select Training Set", ["CPGZ", "H9"], index=0)
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depth = st.selectbox("Select Depth", ["LoopDenoise", "50M", "101K"], index=0)
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# TODO Throw a warning that h9 only has LoopDenoise and 100M
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# val_cols
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# keep_zeros
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# Load the model from hugging face
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from huggingface_hub import from_pretrained_keras
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from huggingface_hub import snapshot_download
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HiCorr_data = snapshot_download(HiCorr_data_hf_repo, repo_type="dataset")
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anchors = snapshot_download(
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repo_id=f"funlab/{genome}_{digestion_enzyme}_anchor_bed", repo_type="dataset"
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)
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if st.button("Run Deeploop", type="primary"):
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denoised_anchor_to_anchor = None
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with st.spinner("Running the model"):
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denoised_anchor_to_anchor = predict_and_write(
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model,
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full_matrix_dir=HiCorr_data + f"/anchor_2_anchor.loop.{chromosome}",
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input_name=HiCorr_data + f"/anchor_2_anchor.loop.{chromosome}.p_val",
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outdir=prefix,
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anchor_dir=anchors,
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chromosome=chromosome,
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small_matrix_size=128,
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step_size=128,
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dummy=5,
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# max_dist,
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val_cols=["obs", "exp", "pval"],
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# keep_zeros,
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)
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st.success("Done!")
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# Print and list the files
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st.download_button(
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label="Download Denoised Results",
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data=os.path.join(prefix, chromosome + ".denoised.anchor.to.anchor"),
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file_name=chromosome + ".denoised.anchor.to.anchor",
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mime="text/csv",
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)
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st.dataframe(denoised_anchor_to_anchor)
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st.write(os.listdir(prefix))
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