Gradio App

app.py

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+import gradio as gr
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+model_path = f'Feiiisal/cardiffnlp_twitter_roberta_base_sentiment_latest_Nov2023'
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForSequenceClassification.from_pretrained(model_path)
+
+def predict_tweet(tweet):
+    inputs = tokenizer(tweet, return_tensors="pt", padding=True, truncation=True, max_length=128)
+    outputs = model(**inputs)
+    probs = outputs.logits.softmax(dim=-1)
+    sentiment_classes = ['Negative', 'Neutral', 'Positive']
+    return {sentiment_classes[i]: float(probs.squeeze()[i]) for i in range(len(sentiment_classes))}
+
+iface = gr.Interface(
+    fn=predict_tweet,
+    inputs="text",
+    outputs="label",
+    title="Vaccine Sentiment Classifier",
+    description="Enter a text about vaccines to determine if the sentiment is negative, neutral, or positive.",
+    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()

requirements.txt

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+gradio 
+transformers