Create app.py

app.py

@@ -0,0 +1,53 @@
+import gradio as gr
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
+
+
+class EmotionClassifier:
+    def __init__(self, model_name: str):
+        self.model = AutoModelForSequenceClassification.from_pretrained(model_name)
+        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+        self.pipeline = pipeline(
+            "text-classification",
+            model=self.model,
+            tokenizer=self.tokenizer,
+            return_all_scores=True,
+        )
+
+    def predict(self, input_text: str):
+        pred = self.pipeline(input_text)[0]
+        result = {
+            "Sadness 😭": pred[0]["score"],
+            "Joy 😂": pred[1]["score"],
+            "Love 😍": pred[2]["score"],
+            "Anger 😠": pred[3]["score"],
+            "Fear 😨": pred[4]["score"],
+            "Surprise 😲": pred[5]["score"],
+        }
+        return result
+
+
+def main():
+    model = EmotionClassifier("bhadresh-savani/bert-base-uncased-emotion")
+    iface = gr.Interface(
+        fn=model.predict,
+        inputs=gr.inputs.Textbox(
+            lines=3,
+            placeholder="Type a phrase that has some emotion",
+            label="Input Text",
+        ),
+        outputs="label",
+        title="Emotion Classification",
+        examples=[
+            "I get so down when I'm alone",
+            "I believe that today everything will work out",
+            "It was so dark there I was afraid to go",
+            "I loved the gift you gave me",
+            "I was very surprised by your presentation.",
+        ],
+    )
+
+    iface.launch()
+
+
+if __name__ == "__main__":
+    main()