Create app.py

app.py

@@ -0,0 +1,34 @@
+import gradio as gr
+from transformers import pipeline
+pipe_classification = pipeline("text-classification", model="nlptown/bert-base-multilingual-uncased-sentiment")     
+
+# print(pipe_classification("i like this product")[0])
+def classification_fun(sentence):
+    result = pipe_classification(sentence)[0]
+    return result["label"]
+
+custom_css = """
+body {
+  backgound-image: #E3F2FD;
+  background-size: cover;
+  background-repeat: no-repeat;
+  background-attachment: fixed;
+}
+"""
+
+ex = [
+["I love this product! It's amazing!"],
+["This was the worst experience I've ever had."],
+["The movie was okay, not great but not bad either."],
+["Absolutely fantastic! I would recommend it to everyone."]
+]
+
+intrface = gr.Interface(
+    fn=classification_fun,
+    inputs=gr.Textbox(label="Enter Your Sentence"),
+    outputs=gr.Textbox(label="predicted sentiment"),
+    examples=ex,
+    css=custom_css
+)
+
+intrface.launch()