Deploying on HF Space

app.py

@@ -0,0 +1,57 @@
+import gradio as gr
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
+
+MODEL_ID = "tharakap/deberta-sentiment-analyser" 
+
+CUSTOM_ID_TO_LABEL = {
+    0: "anger",
+    1: "fear",
+    2: "joy",
+    3: "sadness",
+    4: "surprise"
+}
+
+def load_model_and_pipeline():
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+    
+    model = AutoModelForSequenceClassification.from_pretrained(
+        MODEL_ID, 
+        id2label=CUSTOM_ID_TO_LABEL
+    )
+    
+    return pipeline("text-classification", model=model, tokenizer=tokenizer) 
+try:
+    classifier = load_model_and_pipeline()
+except Exception as e:
+    print(f"Error loading model: {e}")
+    classifier = pipeline("sentiment-analysis") 
+
+
+def predict_sentiment(text):
+    result = classifier(text)[0]
+    
+    label = result['label'].upper()
+    score = result['score']
+    
+    return f"Prediction: **{label}** (Confidence: {score:.4f})"
+
+
+iface = gr.Interface(
+    fn=predict_sentiment,
+    inputs=gr.Textbox(
+        lines=5, 
+        placeholder="Enter text for emotion analysis...",
+        label="Input Text"
+    ),
+    outputs="markdown",
+    title="DeBERTa Emotion Analysis Demo",
+    description=f"An emotion classification demo using the DeBERTa model: {MODEL_ID}.",
+    examples=[
+        ["I am thrilled to hear the good news!"],
+        ["I cannot believe what just happened; I am completely shocked."],
+        ["I feel a deep sense of despair and helplessness right now."] 
+    ]
+)
+
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

@@ -0,0 +1,3 @@
+gradio>=4.0
+transformers
+torch