Fixed app.py and added requirements

app.py.txt → app.py

@@ -2,13 +2,13 @@ import streamlit as st
 from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer, TFAutoModelForSequenceClassification, DistillbertForSequenceClassification
 
 # Options for models from transformers library
-MODEL_OPTS = ['default', 'bertweet-base-sentiment-analysis, 'twitter-roberta-base', 'distilRoberta-financial-sentiment']
-DEFAULT_OPT = model_opts[0]
+MODEL_OPTS = ['default', 'bertweet-base-sentiment-analysis', 'twitter-roberta-base', 'distilRoberta-financial-sentiment']
+DEFAULT_OPT = MODEL_OPTS[0]
 
 # returns loaded model and tokenizer, if any
 def load_model(opt):
     if opt not in MODEL_OPTS: print("Incorrect model selection. Try again!")
-    model, tokenizer = NULL, NULL
+    model, tokenizer = None, None
 
     # Load the chosen sentiment analysis model from transformers
     if opt == DEFAULT_OPT:
@@ -26,13 +26,13 @@ def load_model(opt):
         tokenizer = AutoTokenizer.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
         model = AutoModelForSequenceClassification.from_pretrained("nlptown/bert-base-multilingual-uncas
     
-    elif model is NULL and tokenizer is NULL: 
+    elif not model and not tokenizer: 
         print("Model not loaded correctly. Try again!")
 
     return model, tokenizer
 
 def sentiment_analysis(model, tokenizer):
-    if tokenizer not NULL:
+    if tokenizer:
         return pipeline('text-classification', model=model, tokenizer=tokenizer)
     else: return pipeline('text-classification', model=model)
 

requirements.txt

@@ -0,0 +1 @@
+streamlit