Test using a subprocess

app.py

@@ -1,5 +1,5 @@
-# TODO: requirments.txt
 import os
+import subprocess
 import streamlit as st
 
 import datasets
@@ -186,6 +186,9 @@ with tab2:
         progress_text = f"Performing inference on {len(sentences)} sentences..."
         progress_bar = st.progress(0, text=progress_text)
 
+        subprocess.Popen(["python", "script.py"])
+
+        # TODO: Switch to stqdm
         predictions = []
         for i, sentence in enumerate(sentences):
             predictions.append(
@@ -195,7 +198,7 @@ with tab2:
                 min(i / len(sentences), 1),
                 text=progress_text,
             )
-            print(f"{model_name} - Progress: {i}/{len(sentences)}")
+            print(f"{model_name} - Progress: {i/len(sentences)}")
         progress_bar.empty()
 
         # Store the predictions in a private dataset

script.py

@@ -0,0 +1,5 @@
+import time
+
+for i in range(1000):
+    time.sleep(1)
+    print(i)