Upload app.py

app.py

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+import gradio as gr
+import joblib
+import numpy as np
+from sklearn.preprocessing import LabelEncoder
+import warnings
+
+# Suppress warnings
+warnings.filterwarnings("ignore")
+
+# Load model and encoder
+emotion_model = joblib.load("emotion_model.pkl")
+song_encoder = joblib.load("song_encoder.pkl")
+emotion_decoder = joblib.load("emotion_decoder.pkl")  # For converting predicted label to text
+
+css = """
+body {
+    background-image: url('https://i.postimg.cc/Y0gQkvSb/music-mood-bg.jpg');
+    background-size: cover;
+    background-repeat: no-repeat;
+    background-attachment: fixed;
+}
+.gradio-container, .gradio-interface, .gradio-box, .title-box {
+    background-color: rgba(255, 255, 255, 0.7) !important;
+    border-radius: 10px !important;
+    padding: 20px !important;
+}
+.track-btn {
+    background: linear-gradient(135deg, #6a11cb, #2575fc) !important;
+    border: none !important;
+    color: white !important;
+    margin-top: 15px !important;
+    font-weight: bold !important;
+    border-radius: 25px !important;
+    padding: 10px 25px !important;
+    font-size: 16px !important;
+    box-shadow: 0 4px 8px rgba(106, 17, 203, 0.3) !important;
+    transition: all 0.3s ease !important;
+}
+.track-btn:hover {
+    background: linear-gradient(135deg, #2575fc, #6a11cb) !important;
+    transform: translateY(-2px) !important;
+}
+"""
+
+def predict_emotion(song_name):
+    try:
+        # Encode input song
+        encoded_song = song_encoder.transform([song_name])
+        emotion_label = emotion_model.predict(np.array(encoded_song).reshape(1, -1))[0]
+        emotion = emotion_decoder.inverse_transform([emotion_label])[0]
+    except Exception as e:
+        return f"<p style='color: red;'>Error: {str(e)}</p>", gr.update(visible=False)
+    
+    return f"""
+    <div style='text-align: center;'>
+        <h2>🎵 Mood Detected 🎵</h2>
+        <p>Your current emotional vibe is:</p>
+        <h1 style='font-size: 2.5em; color: #6a11cb;'>{emotion}</h1>
+    </div>
+    """, gr.update(visible=True)
+
+def clear_form():
+    return "", "", gr.update(visible=False)
+
+# Gradio interface
+with gr.Blocks(title="🎧 Song2Mood Tracker", theme=gr.themes.Soft(), css=css) as app:
+    with gr.Column(elem_classes="title-box"):
+        gr.Markdown("""
+        <div style="text-align: center;">
+            <h1>🎧 Song2Mood Tracker</h1>
+            <p>Type a song that reflects your vibe right now—and we'll tell you your mood.</p>
+        </div>
+        """)
+    
+    song_input = gr.Textbox(label="🎵 What song are you listening to?", placeholder="e.g., Heather by Conan Gray")
+
+    submit_btn = gr.Button("Track Emotion 🎶", elem_classes="track-btn")
+    output_html = gr.HTML()
+    clear_btn = gr.Button("Try Another Song", visible=False, elem_classes="track-btn")
+
+    submit_btn.click(predict_emotion, inputs=song_input, outputs=[output_html, clear_btn])
+    clear_btn.click(clear_form, inputs=None, outputs=[output_html, song_input, clear_btn])
+
+app.launch()