Updated app for finetuned model.

app.py

@@ -2,8 +2,9 @@ import streamlit as st
 from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer, TFAutoModelForSequenceClassification
 
 # 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 = ['finetuned', 'default', 'bertweet-base-sentiment-analysis', 'twitter-roberta-base', 'distilRoberta-financial-sentiment']
+FINETUNED_OPT = MODEL_OPTS[0]
+DEFAULT_OPT = MODEL_OPTS[1]
 
 # returns loaded model and tokenizer, if any
 def load_model(opt):
@@ -11,7 +12,10 @@ def load_model(opt):
     model, tokenizer = None, None
 
     # Load the chosen sentiment analysis model from transformers
-    if opt == DEFAULT_OPT:
+    if opt == FINETUNED_OPT:
+        tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-uncased')
+        model = DistilBertForSequenceClassification.from_pretrained('saccharinedreams/finetuned-distilbert-base-uncased-for-hupd')
+    else if opt == DEFAULT_OPT:
         return model, tokenizer
     elif opt == 'bertweet-base-sentiment-analysis':
         tokenizer = AutoTokenizer.from_pretrained("finiteautomata/bertweet-base-sentiment-analysis")