add Application File

app.py

@@ -9,7 +9,7 @@ import gradio as gr
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
 
 # Specifying the model path, which points to the Hugging Face Model Hub
-model_path = f'Mbabazi/twitter-roberta-base-sentiment-latest'
+model_path = f'Mbabazi/cardiffnlp_twitter_roberta_base_sentiment_latest_Nov2023'
 tokenizer = AutoTokenizer.from_pretrained(model_path)
 model = AutoModelForSequenceClassification.from_pretrained(model_path)
 
@@ -34,13 +34,19 @@ def predict_tweet(tweet):
 
 # Create a Gradio Interface for the tweet sentiment prediction function
 iface = gr.Interface(
-    fn=predict_tweet,  # Set the prediction function
-    inputs="text",  # Specify input type as text
+    fn=predict_tweet, # Set the prediction function
+    inputs="text",    # Specify input type as text
     outputs="label",  # Specify output type as label
-    title="Tweet Sentiment Classifier",  # Set the title of the interface
-    description="Enter a tweet to determine if the sentiment is negative, neutral, or positive."  # Provide a brief description
+    title="Vaccine Sentiment Classifier", # Set the title of the interface
+    description="Enter a text about vaccines to determine if the sentiment is negative, neutral, or positive.", # Provide a brief description
+    examples=[
+        ["Vaccinations have been a game-changer in public health, significantly reducing the incidence of many dangerous diseases and saving countless lives."],
+        ["Vaccinations are a medical intervention that introduces a vaccine to stimulate an individual’s immune response against a particular disease."],
+        ["Vaccines are rushed to the market without proper testing and are pushed by corporations that value profits over the well-being of the public."]
+    ]
 )
 
+
 iface.launch()
 
 # with gr.Blocks() as demo: