Update app.py

app.py

@@ -2,12 +2,12 @@ from transformers import pipeline
 import gradio as gr
 
 # Load models only once to improve performance
-model1 = pipeline(model="cardiffnlp/twitter-roberta-base-sentiment-latest")
+model1 = pipeline(model="siebert/sentiment-roberta-large-english")
 model2 = pipeline(model="finiteautomata/bertweet-base-sentiment-analysis")
 
 def predict_sentiment(text, model_choice):
     try:
-        if model_choice == "Model 1 (twitter-roberta-base)":
+        if model_choice == "Model 1 (RoBERTa-large)":
             predictions = model1(text)
         elif model_choice == "Model 2 (BERTweet-base)":
             predictions = model2(text)
@@ -21,7 +21,7 @@ def documentation():
 
     This demo utilizes two different models from the Hugging Face Transformers library:
     
-    - **Model 1**: RoBERTa-base for sentiment analysis specifically fine-tuned for English Tweets.
+        - **Model 1**: RoBERTa-large for sentiment analysis fine-tuned for diverse English text sources to enhance generalization across different types of texts (reviews, tweets, etc.).
     - **Model 2**: BERTweet for sentiment analysis specifically fine-tuned for English Tweets.
 
     Choose a model from the dropdown and enter text to see the sentiment prediction.
@@ -41,7 +41,7 @@ tab1 = gr.Interface(
     fn=predict_sentiment,
     title="Sentiment Analysis",
     description="Select a model and enter text to analyze sentiment.",
-    inputs=[gr.Textbox(label="Input Text"), gr.Radio(["Model 1 (Default Transformer)", "Model 2 (BERTweet-base)"], label="Model Choice")],
+    inputs=[gr.Textbox(label="Input Text"), gr.Radio(["Model 1 (RoBERTa-large)", "Model 2 (BERTweet-base)"], label="Model Choice")],
     outputs="text",
     examples=exams
 )