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README.md

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+---
+title: Spotify Genre Classifier
+emoji: 🎵
+colorFrom: blue
+colorTo: purple
+sdk: gradio
+sdk_version: 4.44.0
+app_file: app.py
+pinned: false
+license: mit
+---
+
+# 🎵 Spotify Genre Classifier
+
+This model predicts the genre of a song based on its track name.
+
+## Features
+- Fine-tuned GPT-2 model
+- 114 different genres
+- Real-time predictions
+
+## How to Use
+1. Enter a track name
+2. Click "Predict Genre"
+3. See the predicted genre and confidence
+
+## Training Your Own
+Check out the training pipeline: https://github.com/huggingface/transformers

app.py

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+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from pathlib import Path
+
+# Load model
+MODEL_PATH = "outputs/final_model"
+
+if Path(MODEL_PATH).exists():
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
+    model = AutoModelForSequenceClassification.from_pretrained(MODEL_PATH)
+    model.eval()
+    MODEL_LOADED = True
+else:
+    MODEL_LOADED = False
+    model = None
+    tokenizer = None
+
+def predict_genre(track_name):
+    """Predict genre for a track name"""
+    if not MODEL_LOADED:
+        return "Model not found. Please train first."
+    
+    if not track_name:
+        return "Please enter a track name"
+    
+    # Tokenize
+    inputs = tokenizer(track_name, return_tensors='pt', padding=True, truncation=True, max_length=256)
+    
+    # Predict
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probs = torch.softmax(outputs.logits, dim=-1)
+        pred_id = torch.argmax(probs, dim=-1).item()
+        confidence = probs[0, pred_id].item()
+    
+    # Get label
+    pred_label = model.config.id2label.get(pred_id, f"Class_{pred_id}")
+    
+    return f"**Genre:** {pred_label}\n\n**Confidence:** {confidence:.2%}"
+
+# Create Gradio interface
+with gr.Blocks(title="Spotify Genre Classifier", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🎵 Spotify Genre Classifier")
+    gr.Markdown("Enter a song track name to predict its genre using a fine-tuned GPT-2 model.")
+    
+    with gr.Row():
+        with gr.Column():
+            track_input = gr.Textbox(
+                label="Track Name",
+                placeholder="e.g., Bohemian Rhapsody",
+                lines=1
+            )
+            predict_btn = gr.Button("🔮 Predict Genre", variant="primary")
+        
+        with gr.Column():
+            output = gr.Textbox(label="Prediction")
+    
+    # Examples
+    gr.Examples(
+        examples=[
+            "Bohemian Rhapsody",
+            "Shape of You",
+            "Old Town Road",
+            "Blinding Lights",
+            "Bad Guy",
+            "Stairway to Heaven",
+            "Smells Like Teen Spirit",
+            "Billie Jean",
+        ],
+        inputs=track_input
+    )
+    
+    predict_btn.click(fn=predict_genre, inputs=track_input, outputs=output)
+    track_input.submit(fn=predict_genre, inputs=track_input, outputs=output)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+transformers>=4.35.0
+torch>=2.0.0
+gradio>=4.0.0