Try to fix flair error in HF spaces

app.py

@@ -30,21 +30,23 @@ from transformers_interpret import SequenceClassificationExplainer
 
 
 # USE_model = hub.load("https://tfhub.dev/google/universal-sentence-encoder/4")
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 @st.experimental_singleton
 def get_sentence_embedding_model():
     return SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
 
+
 @st.experimental_singleton
 def get_spacy():
     nlp = spacy.load('en_core_web_lg')
     return nlp
 
-@st.experimental_singleton
-def get_flair_tagger():
-    tagger = SequenceTagger.load("flair/ner-english-ontonotes-fast")
-    return tagger
 
+# @st.experimental_singleton
+@st.cache(suppress_st_warning=True, allow_output_mutation=True)
+def get_flair_tagger():
+    return SequenceTagger.load("flair/ner-english-ontonotes-fast")
 
 
 # Page setup
@@ -101,16 +103,10 @@ def display_summary(article_name: str):
     return HTML_WRAPPER.format(soup)
 
 
-
-
-
-
-
-
 def get_all_entities_per_sentence(text):
     # load all NER models
-    #nlp = get_spacy()
-    #tagger = get_flair_tagger()
+    # nlp = get_spacy()
+    # tagger = get_flair_tagger()
     doc = nlp(text)
 
     sentences = list(doc.sents)
@@ -195,7 +191,7 @@ def render_dependency_parsing(text: str):
 
 # If deps for article: True, otherwise deps for summary calc
 def check_dependency(article: bool):
-    #nlp = spacy.load('en_core_web_lg')
+    # nlp = spacy.load('en_core_web_lg')
     if article:
         text = st.session_state.article_text
         all_entities = get_all_entities_per_sentence(text)

requirements.txt

@@ -5,6 +5,5 @@ transformers-interpret==0.5.2
 sentence-transformers==2.2.0
 spacy==3.0.0
 spacy_streamlit==1.0.3
-flair
-en_core_web_sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.0.0/en_core_web_sm-3.0.0.tar.gz
+flair==0.11.1
 en_core_web_lg @ https://github.com/explosion/spacy-models/releases/download/en_core_web_lg-3.0.0/en_core_web_lg-3.0.0.tar.gz