import streamlit as st
import requests
import numpy as np
import os

# Try to get API_URL from environment variables, if not found set to a default value
try:
    API_URL = os.environ["API_URL"]
except KeyError:
    st.error("API_URL environment variable is not set.")
    st.stop()

# Try to get the Bearer token from environment variables, if not found set to a default value
try:
    BEARER_TOKEN = os.environ["BEARER_TOKEN"]
except KeyError:
    st.error("BEARER_TOKEN environment variable is not set.")
    st.stop()

headers = {
    "Authorization": f"Bearer {BEARER_TOKEN}",
    "Content-Type": "application/json"
}

# Streamlit app title
st.title("Songlabai")

genres = [
    "Pop", "Rock", "Hip Hop", "Jazz", "Blues",
    "Country", "Classical", "Electronic", "Reggae",
    "Folk", "R&B", "Metal", "Punk", "Indie",
    "Dance", "World", "Gospel", "Soul", "Funk",
    "Ambient", "Techno", "Disco", "House", "Trance",
    "Dubstep"
]

genre = st.selectbox("Select Genre:", genres)
energy_levels = ["Low", "Medium", "High"]
energy_level = st.radio("Energy Level:", energy_levels)
description = st.text_input("Description:", "")

# Duration input
duration = st.slider("Duration (in seconds):", min_value=15, max_value=90, value=30, step=1)

# Generate audio based on the user's prompt
if st.button("Generate Audio"):
    prompt = f"{genre}, Energy: {energy_level}, Description: {description}"
    st.text("Generating audio...")
    response = requests.post(API_URL, headers=headers, json={"inputs": {"prompt": prompt, "duration": duration}})
    audio = np.array(response.json()[0]['generated_audio'], dtype=np.float32)
    sample_rate = response.json()[0]['sample_rate']
    st.audio(audio, format="audio/wav", sample_rate=sample_rate, start_time=0)

# Post-processing options
st.sidebar.title("Post-processing Options")
apply_stereo = st.sidebar.checkbox("Apply Stereo Effect")
reverse = st.sidebar.checkbox("Reverse Audio")
change_speed = st.sidebar.checkbox("Change Speed")
if change_speed:
    speed_factor = st.sidebar.slider("Speed Factor:", min_value=0.1, max_value=2.0, value=1.0, step=0.1)

# Pitch shifting
st.sidebar.title("Pitch Shifting")
pitch_shift = st.sidebar.checkbox("Pitch Shift")
if pitch_shift:
    pitch_semitones = st.sidebar.slider("Pitch (semitones):", min_value=-12, max_value=12, value=0, step=1)

# Reverb
st.sidebar.title("Reverb")
add_reverb = st.sidebar.checkbox("Add Reverb")
if add_reverb:
    reverb_room_scale = st.sidebar.slider("Room Scale:", min_value=0.0, max_value=100.0, value=50.0)
    reverb_damping = st.sidebar.slider("Damping:", min_value=0.0, max_value=100.0, value=50.0)
    reverb_wet_only = st.sidebar.checkbox("Wet Only", value=False)

# Apply selected post-processing
if apply_stereo or reverse or change_speed or pitch_shift or add_reverb:
    st.text("Applying post-processing...")

    # Apply selected post-processing
    if apply_stereo:
        # Create a stereo effect by duplicating the audio and panning left and right
        audio_left = audio
        audio_right = audio
        audio = np.stack([audio_left, audio_right], axis=-1)

    if reverse:
        # Reverse the audio array
        audio = np.flip(audio)

    if change_speed:
        # Change the speed by resampling the audio
        new_sample_rate = int(sample_rate * speed_factor)
        audio = scipy.signal.resample(audio, int(len(audio) * speed_factor))

    if pitch_shift:
        # Pitch shift using the Fourier shift method
        pitch_shift_factor = 2 ** (pitch_semitones / 12)
        audio = scipy.signal.resample(audio, int(len(audio) / pitch_shift_factor))

    if add_reverb:
        # Apply reverb using the convolution method
        reverb_ir = scipy.signal.exponential(reverb_room_scale, reverb_damping, reverb_wet_only)
        audio = scipy.signal.fftconvolve(audio, reverb_ir)

    # Play the processed audio
    st.audio(audio, format="audio/wav", sample_rate=sample_rate, start_time=0)