Create app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Emotions
+emotions = ["Anger", "Love", "Fear", "Joy", "Sadness", "Surprise"]
+
+# Load fine-tuned model
+model_path = "./model"
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForSequenceClassification.from_pretrained(model_path)
+
+def predict_emotions(comment):
+    inputs = tokenizer(comment, return_tensors="pt", truncation=True)
+    outputs = model(**inputs)
+    scores = torch.sigmoid(outputs.logits)[0].detach().numpy()
+    return {emotion: float(scores[i]) for i, emotion in enumerate(emotions)}
+
+demo = gr.Interface(
+    fn=predict_emotions,
+    inputs=gr.Textbox(lines=4, placeholder="Enter GitHub comment here..."),
+    outputs=gr.Label(num_top_classes=6),
+    title="GitHub Comment Emotion Detector",
+    description="Detects Anger, Love, Fear, Joy, Sadness, and Surprise in GitHub comments."
+)
+
+if __name__ == "__main__":
+    demo.launch()