First model version

app.py

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+import gradio as gr
+from transformers import pipeline
+
+classifier = pipeline("sentiment-analysis", model="Tirendaz/my_distilbert_model")
+
+def text_classification(text):
+    result= classifier(text)
+    sentiment_label = result[0]['label']
+    sentiment_score = result[0]['score']
+    formatted_output = f"This sentiment is {sentiment_label} with the probability {sentiment_score*100:.2f}%"
+    return formatted_output
+
+examples=["This is wonderful movie!", "The movie was really bad; I didn't like it."]
+
+io = gr.Interface(fn=text_classification, 
+                         inputs= gr.Textbox(lines=2, label="Text", placeholder="Enter title here..."), 
+                         outputs=gr.Textbox(lines=2, label="Text Classification Result"),
+                         title="Text Classification",
+                         description="Enter a text and see the text classification result!",
+                         examples=examples)
+
+io.launch(inline=False, share=True)
+

requirements.txt

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+transformers
+gradio