Create app.py

app.py

@@ -0,0 +1,60 @@
+import gradio as gr
+import torch
+from peft import PeftModel
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import json
+
+# Load model and tokenizer
+base_model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=6)
+model = PeftModel.from_pretrained(base_model, "katsuchi/bert-dair-ai-emotion")
+tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
+
+def predict_emotion(text):
+    # Tokenize input
+    tokens = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)
+    
+    # Get model prediction
+    with torch.no_grad():
+        outputs = model(tokens['input_ids'])
+        probs = torch.softmax(outputs.logits, dim=-1)
+    
+    # Convert probabilities to percentages
+    percentages = (probs * 100).squeeze().tolist()
+    
+    # Create emotion-percentage mapping
+    emotions = ['sadness', 'joy', 'love', 'anger', 'fear', 'surprise']
+    emotion_probs = {
+        emotion: f"{percentage:.1f}%" 
+        for emotion, percentage in zip(emotions, percentages)
+    }
+    
+    # Sort by probability in descending order
+    sorted_emotions = dict(
+        sorted(emotion_probs.items(), 
+               key=lambda x: float(x[1].rstrip('%')), 
+               reverse=True)
+    )
+    
+    # Format output
+    return json.dumps(sorted_emotions, indent=2)
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=predict_emotion,
+    inputs=gr.Textbox(
+        lines=3, 
+        placeholder="Enter text here..."
+    ),
+    outputs=gr.JSON(),
+    title="Emotion Classifier",
+    description="Predict emotions in text with confidence percentages",
+    examples=[
+        ["I am so happy to see you!"],
+        ["I'm really disappointed with the results."],
+        ["That's absolutely terrifying!"],
+        ["I love spending time with my family."]
+    ]
+)
+
+if __name__ == "__main__":
+    iface.launch()