Add a run button

app.py

@@ -13,7 +13,9 @@ st.write("Use hg19, HindIII, chr11, CPGZ and LoopDenoise for the Demo data")
 #########
 # INPUT #
 #########
-HiCorr_data = st.file_uploader("Upload HiCorr Data", type=["tar.gz", "zip"])
+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
@@ -45,7 +47,6 @@ digestion_enzyme = st.selectbox(
 # val_cols
 # keep_zeros
 
-
 # Load the model from hugging face
 from huggingface_hub import from_pretrained_keras
 
@@ -53,40 +54,38 @@ 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"
 )
 
-import os
-
-os.makedirs(prefix, exist_ok=True)
+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,
+        )
 
-denoised_anchor_to_anchor = None
+    st.success("Done!")
 
-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,
+    # 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.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))
+    st.dataframe(denoised_anchor_to_anchor)
+    st.write(os.listdir(prefix))