import streamlit as st
import numpy as np

# Page setup
st.title("🎵 Music Genre Classifier")
st.write("Upload an audio file to analyze its genre")

# Create two columns for better layout
col1, col2 = st.columns(2)

with col1:
    # File upload
    audio_file = st.file_uploader("Upload an audio file (MP3, WAV)", type=['mp3', 'wav'])

    if audio_file is not None:
        # Display audio player
        st.audio(audio_file)
        st.success("File uploaded successfully!")

        # Add a classify button
        if st.button("Classify Genre"):
            with st.spinner("Analyzing..."):
                # Simulate genre classification (we'll replace this with real model later)
                genres = ["Rock", "Pop", "Hip Hop", "Classical", "Jazz"]
                confidences = np.random.dirichlet(np.ones(5))  # Random probabilities that sum to 1

                # Show results
                st.write("### Genre Analysis Results:")
                for genre, confidence in zip(genres, confidences):
                    st.write(f"{genre}: {confidence:.2%}")

                # Show top prediction
                top_genre = genres[np.argmax(confidences)]
                st.write(f"**Predicted Genre:** {top_genre}")

with col2:
    # Display some tips and information
    st.write("### Tips for best results:")
    st.write("- Upload files in MP3 or WAV format")
    st.write("- Ensure good audio quality")
    st.write("- Try to upload songs without too much background noise")
    st.write("- Ideal length: 10-30 seconds")

    # Add a sample counter
    if 'analyzed_count' not in st.session_state:
        st.session_state.analyzed_count = 0

    if audio_file is not None:
        st.session_state.analyzed_count += 1
        st.write(f"Songs analyzed this session: {st.session_state.analyzed_count}")

# Footer
st.markdown("---")
st.write("Made with ❤️ using Streamlit")