maintenance

app.py

@@ -85,30 +85,34 @@ st.set_page_config(layout="centered")
 st.title("Multiclass Emotion Classification")
 st.write("DeepMind Language Perceiver for Multiclass Emotion Classification (Eng). ")
 
-# Variables
-ids = {'perceiver-go-emotions': st.secrets['model_key']}
-labels = load_labels()
-
-# Download all models from drive
-download_models(ids)
-
-# Display labels
-st.markdown(f"__Labels:__ {', '.join(labels)}")
-
-# Model selection
-left, right = st.columns([4, 2])
-inputs = left.text_area('', max_chars=4096, value='This is a space about multiclass emotion classification. Write '
-                                                  'something here to see what happens!')
-model_path = right.selectbox('', options=[k for k in ids], index=0, help='Model to use. ')
-split = right.checkbox('Split into sentences', value=True)
-model = load_model(model_path=f"model/{model_path}.pt")
-right.write(model.device)
-
-if split:
-    if not inputs.isspace() and inputs != "":
-        with st.spinner('Processing text... This may take a while.'):
-            left.write(model(inputs_to_dataset(sent_tokenize(inputs)), batch_size=1))
+maintenance = True
+if maintenance:
+    print("Unavailable for now (file downloads limit). ")
 else:
-    if not inputs.isspace() and inputs != "":
-        with st.spinner('Processing text... This may take a while.'):
-            left.write(model(inputs_to_dataset([inputs]), batch_size=1))
+    # Variables
+    ids = {'perceiver-go-emotions': st.secrets['model_key']}
+    labels = load_labels()
+
+    # Download all models from drive
+    download_models(ids)
+
+    # Display labels
+    st.markdown(f"__Labels:__ {', '.join(labels)}")
+
+    # Model selection
+    left, right = st.columns([4, 2])
+    inputs = left.text_area('', max_chars=4096, value='This is a space about multiclass emotion classification. Write '
+                                                      'something here to see what happens!')
+    model_path = right.selectbox('', options=[k for k in ids], index=0, help='Model to use. ')
+    split = right.checkbox('Split into sentences', value=True)
+    model = load_model(model_path=f"model/{model_path}.pt")
+    right.write(model.device)
+
+    if split:
+        if not inputs.isspace() and inputs != "":
+            with st.spinner('Processing text... This may take a while.'):
+                left.write(model(inputs_to_dataset(sent_tokenize(inputs)), batch_size=1))
+    else:
+        if not inputs.isspace() and inputs != "":
+            with st.spinner('Processing text... This may take a while.'):
+                left.write(model(inputs_to_dataset([inputs]), batch_size=1))