Create app.py

app.py

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+import streamlit as st
+import os
+import tempfile
+from transformers import pipeline
+import librosa
+import soundfile as sf
+from streamlit.components.v1 import html
+import base64
+
+# Set page configuration
+st.set_page_config(page_title="Music Genre Classifier", layout="wide")
+
+# Custom CSS for styling
+custom_css = """
+<style>
+    .stApp {
+        background-color: #f0f0f5;
+    }
+    .main-title {
+        color: #1e1e1e;
+        font-size: 3em;
+        font-weight: bold;
+        text-align: center;
+        margin-bottom: 30px;
+    }
+    .sub-title {
+        color: #4a4a4a;
+        font-size: 1.5em;
+        text-align: center;
+        margin-bottom: 20px;
+    }
+    .result-container {
+        background-color: #ffffff;
+        border-radius: 10px;
+        padding: 20px;
+        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
+    }
+    .genre-result {
+        font-size: 2em;
+        font-weight: bold;
+        text-align: center;
+        color: #2c3e50;
+    }
+    .confidence-bar {
+        height: 30px;
+        background-color: #3498db;
+        border-radius: 15px;
+    }
+</style>
+"""
+
+# Render custom CSS
+st.markdown(custom_css, unsafe_allow_html=True)
+
+# Load the audio classification model
+@st.cache_resource
+def load_model():
+    try:
+        return pipeline("audio-classification", model="sandychoii/distilhubert-finetuned-gtzan-audio-classification")
+    except Exception as e:
+        st.error(f"Error loading the model: {str(e)}")
+        return None
+
+pipe = load_model()
+
+# Function to classify audio
+def classify_audio(audio_file):
+    try:
+        # Load audio file
+        y, sr = librosa.load(audio_file, sr=None)
+        
+        # Ensure the audio is at least 3 seconds long (model requirement)
+        if len(y) < 3 * sr:
+            y = librosa.util.fix_length(y, size=3 * sr)
+        
+        # Classification
+        result = pipe(y, sampling_rate=sr)
+        return result
+    except Exception as e:
+        st.error(f"Error during classification: {str(e)}")
+        return None
+
+# Main app
+def main():
+    st.markdown("<h1 class='main-title'>🎵 Music Genre Classifier 🎸</h1>", unsafe_allow_html=True)
+    st.markdown("<p class='sub-title'>Upload a music file and let AI detect its genre!</p>", unsafe_allow_html=True)
+
+    # File uploader
+    uploaded_file = st.file_uploader("Choose an audio file", type=["wav", "mp3", "ogg"])
+
+    if uploaded_file is not None:
+        # Display audio player
+        st.audio(uploaded_file)
+
+        # Classify button
+        if st.button("Classify Genre"):
+            with st.spinner("Analyzing the music... 🎧"):
+                # Save uploaded file temporarily
+                with tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(uploaded_file.name)[1]) as tmp_file:
+                    tmp_file.write(uploaded_file.getvalue())
+                    tmp_file_path = tmp_file.name
+
+                # Perform classification
+                result = classify_audio(tmp_file_path)
+
+                # Remove temporary file
+                os.unlink(tmp_file_path)
+
+                if result:
+                    # Display results
+                    st.markdown("<div class='result-container'>", unsafe_allow_html=True)
+                    st.markdown(f"<h2 class='genre-result'>Detected Genre: {result[0]['label'].capitalize()}</h2>", unsafe_allow_html=True)
+                    
+                    # Display confidence bar
+                    confidence = result[0]['score']
+                    st.markdown(f"<div class='confidence-bar' style='width: {confidence*100}%;'></div>", unsafe_allow_html=True)
+                    st.write(f"Confidence: {confidence:.2%}")
+                    
+                    # Display top 3 predictions
+                    st.write("Top 3 Predictions:")
+                    for r in result[:3]:
+                        st.write(f"- {r['label'].capitalize()}: {r['score']:.2%}")
+                    st.markdown("</div>", unsafe_allow_html=True)
+
+    # Add information about the model
+    st.sidebar.title("About")
+    st.sidebar.info("This app uses a fine-tuned DistilHuBERT model to classify music genres. It can identify genres like rock, pop, hip-hop, classical, and more!")
+
+    # Add a footer
+    footer_html = """
+    <div style="position: fixed; bottom: 0; width: 100%; text-align: center; padding: 10px; background-color: #f0f0f5;">
+        <p>Created with ❤️ by AI. Powered by Streamlit and Hugging Face Transformers.</p>
+    </div>
+    """
+    st.markdown(footer_html, unsafe_allow_html=True)
+
+if __name__ == "__main__":
+    main()