Updated requirements, pipeline

app.py

@@ -12,7 +12,7 @@ def load_model(opt):
 
     # Load the chosen sentiment analysis model from transformers
     if opt == DEFAULT_OPT:
-        return pipeline("sentiment-analysis"), tokenizer
+        return model, tokenizer
     elif opt == 'bertweet-base-sentiment-analysis':
         tokenizer = AutoTokenizer.from_pretrained("finiteautomata/bertweet-base-sentiment-analysis")
         model = AutoModelForSequenceClassification.from_pretrained("finiteautomata/bertweet-base-sentiment-analysis")
@@ -29,9 +29,9 @@ def load_model(opt):
     return model, tokenizer
 
 def sentiment_analysis(model, tokenizer):
-    if tokenizer:
+    if model and tokenizer:
         return pipeline('text-classification', model=model, tokenizer=tokenizer)
-    else: return pipeline('text-classification', model=model)
+    else: return pipeline('text-classification')
 
 # Title the Streamlit app 'Sentiment Analysis'
 st.title('Sentiment Analysis')

requirements.txt

@@ -1,3 +1,4 @@
 streamlit
 transformers
-torch
+torch
+huggingface_hub