text

app.py

@@ -1,9 +1,8 @@
-# Uncomment if run locally
 import os
-#import sys
-#
-#sys.path.append(os.path.abspath("../../../molvault"))
-#sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
+# Uncomment if run locally
+# import sys
+# sys.path.append(os.path.abspath("../../../molvault"))
+# sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
 
 from requests import head
 from concrete.ml.deployment import FHEModelClient
@@ -68,6 +67,15 @@ formatted_text = (
 
 st.markdown(formatted_text, unsafe_allow_html=True)
 
+# make a small hint that the app needs a few seconds to start
+st.markdown(
+    "<p style='text-align: center; color: grey;'>"
+    + "The app needs a second to start..."
+    + "</p>",
+    unsafe_allow_html=True,
+)
+
+
 interesting_text = """
 Machine learning (**ML**) has become a cornerstone of modern drug discovery. However, the data used to evaluate the ML models is often **confidential**.
 This is especially true for the pharmaceutical industry where new drug candidates are considered as the most valuable asset.
@@ -272,11 +280,9 @@ def run_fhe(user_id):
             )
         else:
             print("Invalid task number")
-        # pdb.set_trace()
+        
         encrypted_prediction = base64.b64decode(response.json()["encrypted_prediction"])
-
-        # Save encrypted_prediction in a file, since too large to pass through regular Gradio
-        # buttons, https://github.com/gradio-app/gradio/issues/1877
+        
         numpy.save(f"tmp/tmp_encrypted_prediction_{user_id}.npy", encrypted_prediction)
         encrypted_prediction_shorten = list(encrypted_prediction)[
             :ENCRYPTED_DATA_BROWSER_LIMIT