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app.py

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+import gradio as gr
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
+
+
+class TwitterEmotionClassifier:
+    def __init__(self, model_name: str, model_type: str):
+        self.is_gpu = False
+        self.model_type = model_type
+        device = torch.device("cuda") if self.is_gpu else torch.device("cpu")
+        model = AutoModelForSequenceClassification.from_pretrained(model_name)
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        model.to(device)
+        model.eval()
+        self.bertweet = pipeline(
+            "text-classification",
+            model=model,
+            tokenizer=tokenizer,
+            device=self.is_gpu - 1,
+        )
+        self.deberta = None
+        self.emotions = {
+            "LABEL_0": "sadness",
+            "LABEL_1": "joy",
+            "LABEL_2": "love",
+            "LABEL_3": "anger",
+            "LABEL_4": "fear",
+            "LABEL_5": "surprise",
+        }
+
+    def get_model(self, model_type: str):
+        if self.model_type == "bertweet" and model_type == self.model_type:
+            return self.bertweet
+        elif model_type == "deberta":
+            if self.deberta:
+                return self.deberta
+            model = AutoModelForSequenceClassification.from_pretrained(
+                "Emanuel/twitter-emotion-deberta-v3-base"
+            )
+            tokenizer = AutoTokenizer.from_pretrained(
+                "Emanuel/twitter-emotion-deberta-v3-base"
+            )
+            self.deberta = pipeline(
+                "text-classification",
+                model=model,
+                tokenizer=tokenizer,
+                device=self.is_gpu - 1,
+            )
+            return self.deberta
+
+    def predict(self, twitter: str, model_type: str):
+        classifier = self.get_model(model_type)
+        preds = classifier(twitter, return_all_scores=True)
+        if preds:
+            pred = preds[0]
+            res = {
+                "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 res
+        return None
+
+
+def main():
+
+    model = TwitterEmotionClassifier("Emanuel/bertweet-emotion-base", "bertweet")
+    interFace = gr.Interface(
+        fn=model.predict,
+        inputs=[
+            gr.inputs.Textbox(
+                placeholder="What's happenning?", label="Tweet content", lines=5
+            ),
+            gr.inputs.Radio(["bertweet", "deberta"], label="Model"),
+        ],
+        outputs=gr.outputs.Label(num_top_classes=6, label="Emotions of this tweet is "),
+        verbose=True,
+        examples=[
+            ["This GOT show just remember LOTR times!", "bertweet"],
+            [
+                "Man, can't believe that my 30 days of training just got a NaN loss",
+                "bertweet",
+            ],
+            ["I couldn't see 3 Tom Hollands coming...", "bertweet"],
+            [
+                "There is nothing better than a soul-warming coffee in the morning",
+                "bertweet",
+            ],
+            ["I fear the vanishing gradient", "deberta"],
+        ],
+        title="Emotion classification 🤖",
+        description="",
+        theme="huggingface",
+    )
+    interFace.launch()
+
+
+if __name__ == "__main__":
+    main()