Create app.py

app.py

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+# required imports
+import gradio as gr
+import torch
+from transformer import AutoTokenizer, AutoModelForSequenceClassification
+
+# loading the model from the repository
+REPO_ID = "adith-ds/deberta-classifier-v1"
+tokenizer = AutoTokenizer.from_pretrained(REPO_ID)
+model = AutoModelForSequenceClassification.from_pretrained(REPO_ID)
+
+# denoting the labels
+LABEL_COLUMNS = ['anger', 'fear', 'joy', 'sadness', 'surprise']
+
+#helper function to make a prediction using the model
+def predict(text):
+    input = tokenizer(text, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**input)
+        logits = outputs.logits
+    probs = torch.sigmoid(logits)[0]
+    return {label: float(prob) for label, prob in zip(LABEL_COLUMNS, probs)}
+
+# gradio configuration stuff
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Textbox(label="Enter your text here"),
+    outputs=gr.Label(num_top_classes=5, label="Emotions detected"),
+    title="Emotion Classifier (using finetuned DeBERTa)",
+    description="Classify your text into the following emotions: Anger, Fear, Joy, Sadness, Surprise",
+    examples=[
+        ["Yikes! What a scare."],
+        ["I hated this movie, it was so boring."],
+        ["Oh wow! The effects were incredible. "]
+    ]
+)
+
+# running the application
+demo.launch()