import streamlit as st
import numpy as np
import requests
import time
import pickle
import tensorflow as tf
from music21 import *
from midi2audio import FluidSynth
from streamlit_lottie import st_lottie
#import hydralit_components as hc


####################### Music Generation Functions #######################
def generate(seq_len, x):
    """ Generate a piano midi file """
    with open('final_notes', 'rb') as filepath:
        notes = pickle.load(filepath)

    pitchnames = sorted(set(item for item in notes))
    n_vocab = len(set(notes))

    network_input, normalized_input = prepare_sequences(notes, pitchnames, n_vocab, seq_length=seq_len)
    model = create_network(normalized_input, n_vocab)
    prediction_output = generate_notes(model, network_input, pitchnames, n_vocab, x)
    create_midi(prediction_output)

def prepare_sequences(notes, pitchnames, n_vocab, seq_length):
    note_to_int = dict((note, number) for number, note in enumerate(pitchnames))
    network_input = []
    normalized_input = []
    output = []
    for i in range(0, len(notes) - seq_length, 1):
        sequence_in = notes[i:i + seq_length]
        sequence_out = notes[i + sequence_length]
        network_input.append([note_to_int[char] for char in sequence_in])
        output.append(note_to_int[sequence_out])

    n_patterns = len(network_input)
    normalized_input = np.reshape(network_input, (n_patterns, seq_length, 1))
    normalized_input = normalized_input / float(n_vocab)

    return (network_input, normalized_input)


def create_network(network_input, n_vocab):
    model = tf.keras.Sequential()
    model.add(tf.keras.layers.LSTM(512, input_shape=(network_input.shape[1], network_input.shape[2]), return_sequences=True, recurrent_dropout=0.3))
    model.add(tf.keras.layers.LSTM(512, return_sequences=True, recurrent_dropout=0.3))
    model.add(tf.keras.layers.LSTM(256))
    model.add(tf.keras.layers.BatchNormalization())
    model.add(tf.keras.layers.Dropout(0.2))
    model.add(tf.keras.layers.Dense(256, activation='relu'))
    model.add(tf.keras.layers.BatchNormalization())
    model.add(tf.keras.layers.Dropout(0.2))
    model.add(tf.keras.layers.Dense(n_vocab, activation='softmax'))
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    model.load_weights('best2.h5')
    return model

def generate_notes(model, network_input, pitchnames, n_vocab, x):
    start = np.random.randint(0, len(network_input)-1)
    int_to_note = dict((number, note) for number, note in enumerate(pitchnames))
    pattern = network_input[start]
    prediction_output = []

    for note_index in range(x):
        prediction_input = np.reshape(pattern, (1, len(pattern), 1))
        prediction_input = prediction_input / float(n_vocab)
        prediction = model.predict(prediction_input, verbose=0)
        index = np.argmax(prediction)
        result = int_to_note[index]
        prediction_output.append(result)
        pattern.append(index)
        pattern = pattern[1:len(pattern)]

    return prediction_output

def create_midi(prediction_output):
    offset = 0
    output_notes = []
    for pattern in prediction_output:
        if ('.' in pattern) or pattern.isdigit():
            notes_in_chord = pattern.split('.')
            notes = []
            for current_note in notes_in_chord:
                new_note = note.Note(int(current_note))
                new_note.storedInstrument = instrument.Piano()
                notes.append(new_note)
            new_chord = chord.Chord(notes)
            new_chord.offset = offset
            output_notes.append(new_chord)
        elif pattern == 'r':
            new_note = note.Rest(pattern)
            new_note.offset = offset
            new_note.storedInstrument = instrument.Piano()
            output_notes.append(new_note)
        else:
            new_note = note.Note(pattern)
            new_note.offset = offset
            new_note.storedInstrument = instrument.Piano()
            output_notes.append(new_note)
        offset += 0.5

    midi_stream = stream.Stream(output_notes)
    midi_stream.write('midi', fp='test_output2.mid')

    
# Set page config
st.set_page_config(page_title="Music Generation", page_icon=":tada:", layout="wide")


# Header section
with st.container():
    left_column, right_column = st.columns(2)
    with left_column:
        st.subheader("Music Generation :musical_keyboard:")
        st.write(
            "Our website is an application of piano music generation, you can listen to new musical notes generated by LSTM artificial neural network, which is used in fields of AI and deep learning. Let's get it started :notes:"
        )
    with right_column:
        # Display a GIF instead of Lottie animation
        st.image("im.gif", use_column_width=True)  

# Sidebar for user input
# Sidebar for user input
with st.sidebar:
    # Set a default value for len_notes
    default_len_notes = 100  # Example default value
    len_notes = st.slider('Please Choose The Notes Length', 20, 750, default_len_notes, 4)
    st.write("Notes Length = ", len_notes)

# Music generation functionality
if st.sidebar.button('Generate My Music'):
    # Use the default value if len_notes is not explicitly set by the user
    if len_notes is not None:
        with st.container():
            st.write("---")
            with st.spinner('✨ Your music is now under processing... ✨'):
                time.sleep(10)  # Simulate processing time
                generate(10, len_notes)

                fs = FluidSynth('font.sf2', sample_rate=44100)
                fs.midi_to_audio('test_output2.mid', 'output.wav')

                st.audio('output.wav')
                st.markdown("Here you are! You can download your music by right-clicking on the media player.")
    else:
        # Fallback to the default value if no selection is made
        with st.container():
            st.write("---")
            st.warning("No notes length selected. Using default value of 100.")
            with st.spinner('✨ Your music is now under processing... ✨'):
                time.sleep(10)  # Simulate processing time
                generate(10, default_len_notes)

                fs = FluidSynth('font.sf2', sample_rate=44100)
                fs.midi_to_audio('test_output2.mid', 'output.wav')

                st.audio('output.wav')
                st.markdown("Here you are! You can download your music by right-clicking on the media player.")