Create app.py

app.py

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+import gradio as gr
+from transformers import TextClassificationPipeline
+
+model_path = "leadingbridge/sentiment-analysis"
+tokenizer = BertTokenizerFast.from_pretrained(model_path)
+model = TFBertForSequenceClassification.from_pretrained(model_path, id2label={0: 'negative', 1: 'positive'} )
+
+def sentiment_analysis(text):
+  pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer)
+  result = pipe(text)
+  return result
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("Choose the Chinese NLP model you want to use.")
+    with gr.Tab("Sentiment Analysis"):
+        text_button = gr.Button("proceed")          
+        text_button.click(fn=sentiment_analysis,inputs=gr.Textbox(placeholder="Enter a positive or negative sentence here..."),
+                 outputs=gr.Textbox(label="Sentiment Analysis"))
+
+demo.launch()