Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+roberta_sentiment = pipeline(
+    "text-classifier", model="rasyosef/roberta-base-finetuned-sst2"
+)
+
+
+def predict_sentiment(text):
+    return roberta_sentiment(text)
+
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """
+        # Sentiment Classifier
+
+        This model is a fine-tuned version of roberta-base on 
+        the glue sst2 dataset for sentiment classification. 
+        The model classifies the input text as having either 
+        `positive` or `negative` sentiment. 
+        """
+    )
+    with gr.Row():
+        with gr.Column():
+            inp = gr.Textbox(label="Input text", placeholder="Enter text here", lines=3)
+            btn = gr.Button("Classify")
+        with gr.Column():
+            out = gr.Textbox(label="Sentiment")
+
+    btn.click(fn=predict_sentiment, inputs=inp, outputs=out)
+    gr.Examples(
+        examples=["This movie was awesome.", "The movie was boring."],
+        inputs=[inp],
+    )
+
+demo.launch()