Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+MODEL_ID = "j-hartmann/emotion-english-distilroberta-base"
+analyzer = pipeline("text-classification", model=MODEL_ID, return_all_scores=False)
+
+SUGGESTIONS = {
+    "joy": "Keep up the good vibes! 🥳 Share your happiness with someone.",
+    "sadness": "Take a short break, drink some water, or go for a walk. 🌿",
+    "anger": "Take a deep breath and try to relax. 💪",
+    "fear": "Talk to someone you trust. 💬",
+    "love": "Send a kind message to someone you care about. 💖",
+    "surprise": "Write down what surprised you — curiosity is powerful! 🤔",
+    "neutral": "Take a moment to stretch and breathe. 🌸",
+}
+
+def detect_emotion(text):
+    if not text or not text.strip():
+        return {"Emotion": "—", "Confidence": "—", "Suggestion": "Please write something 🙂"}
+    result = analyzer(text)[0]
+    label = result["label"]
+    score = result["score"]
+    suggestion = SUGGESTIONS.get(label.lower(), SUGGESTIONS["neutral"])
+    return {
+        "Emotion": label,
+        "Confidence": f"{score*100:.1f}%",
+        "Suggestion": suggestion
+    }
+
+demo = gr.Interface(
+    fn=detect_emotion,
+    inputs=gr.Textbox(lines=2, placeholder="Type how you feel... (English preferred)"),
+    outputs="json",
+    title="🪞 MoodMirror",
+    description="Detects the main emotion in your text and gives a self-care suggestion 💬"
+)
+
+if __name__ == "__main__":
+    demo.launch()