First commit with the first version of my demo

app.py

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+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+model_name = "macapa/emotion-classifier"
+tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+labels = {0: 'sadness',
+          1: 'joy',
+          2: 'love',
+          3: 'anger',
+          4: 'fear',
+          5: 'surprise'}
+
+def predict(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    inputs = inputs.to(device)
+    outputs = model(**inputs)
+    predictions = torch.argmax(outputs.logits, dim=1)
+    label = labels[predictions.item()]
+    return label
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Textbox(lines=2, placeholder="Enter text here..."),
+    outputs="textbox",
+    title="Emotion Classification",
+    description="Enter some text and the model will predict the emotion.",
+)
+
+# Launch the interface
+iface.launch()

requirements.txt

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+transformers==4.47.0
+gradio
+torch