Update app.py

app.py

@@ -13,7 +13,7 @@ def predict_sentiment(text, model):
     if model == "finiteautomata/bertweet-base-sentiment-analysis":
         pipe = pipeline("text-classification", model="finiteautomata/bertweet-base-sentiment-analysis")
         out = pipe(text, return_all_scores=True)
-        return {pred["label"]: pred["score"] for pred in out}
+        return {pred["label"]: pred["score"] for pred in out[0]}
     elif model == "vader":
         nltk.download('vader_lexicon')
         sia = SentimentIntensityAnalyzer()