Upload app.py

app.py

@@ -0,0 +1,39 @@
+import gradio as gr
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
+
+class Emotionclass:
+    def __init__(self, model: str):
+        self.model = AutoModelForSequenceClassification.from_pretrained(model)
+        self.tokenizer = AutoTokenizer.from_pretrained(model)
+        self.pipeline = pipeline(
+            "text-classification",
+            model=self.model,
+            tokenizer=self.tokenizer,
+            return_all_scores=True,
+        )
+
+    def predict(self, input: str):
+        output = self.pipeline(input)[0]
+        result = {
+            "sad": output[0]["score"],
+            "joy": output[1]["score"],
+            "love": output[2]["score"],
+            "anger": output[3]["score"],
+            "fear": output[4]["score"],
+            "surprise": output[5]["score"],
+        }
+        return result    
+
+if __name__ == "__main__":
+    model = Emotionclass("ncduy/bert-base-cased-finetuned-emotion")
+    iface = gr.Interface(
+        fn=model.predict,
+        inputs=gr.inputs.Textbox(
+            lines=3,
+            placeholder="type here ...",
+            label="Input",
+        ),
+        outputs="label",
+        title="Emotion Classifier",
+    )
+    iface.launch()