import tempfile
import gradio as gr
import os
import tensorflow as tf
import sys
import numpy as np
import csv
import datetime
import joblib
from huggingface_hub import hf_hub_download

# NO GPU
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'

# Cacher le nom du repo
python_path = hf_hub_download(repo_id=os.environ['REPO_ID'], repo_type="space", filename=os.environ['MODEL_FILE'],
                                use_auth_token=os.environ['TOKEN'])
print(python_path)
sys.path.append(os.environ['PRIVATE_DIR'])
from models import *
preprocess_model, model = get_models()
url_dict = get_durl()
audio_names = get_audio_names()
index = get_index()
#encoder_text = get_encoder_text()
encoder_text = tf.keras.models.load_model("encoder_text_retrievaltext_bmg_221022_54_clean")
def process(prompt, lang):
    now = datetime.datetime.now()
    print()
    print('*************')
    print("Current Time: ", str(now))
    print("Text input : ", prompt)
    print('*************')
    print()

    embed_query  = get_predict(encoder_text, prompt, preprocess_model, model)
    do_normalize(embed_query)
    D, I = get_distance(index, embed_query, TOP)
    #print(I)
    #print(D)
    print("----")
    for i in range(len(I[0])):
        print(audio_names[I[0][i]], " with distance ", D[0][i])
        print("    url : ", get_url(I[0][i], audio_names, url_dict))
        
    return [get_url(I[0][0], audio_names, url_dict), 
            get_url(I[0][1], audio_names, url_dict), 
            get_url(I[0][2], audio_names, url_dict), 
            get_url(I[0][3], audio_names, url_dict), 
            get_url(I[0][4], audio_names, url_dict)]

inputs = [gr.Textbox(label="Input", value="type your description", max_lines=2), 
            gr.Radio(label="Language", choices=["en"], value="en")]

poc_examples = [
          ["Mysterious filmscore with Arabic influenced instruments","en"],
          ["Let's go on a magical adventure with wizzards, dragons and castles","en"],
          ["Creepy piano opening evolves and speeds up into a cinematic orchestral piece","en"],
          ["Chilled electronic","en"],
          #["","en"],
          ["Relax piano","en"],
          ["Halloween rock with creepy organ","en"],
          ["Rhythmic electro dance track for sport, motivation and sweating","en"],
          ["soundtrack for an action movie from the eighties in a retro synth wave style","en"],
          ["Choral female singing is rhythmically accompanied in a church with medieval instruments","en"],
          ["Christmas","en"],
          ["love romantic with piano, strings and vocals","en"],
          ["Electronic soundscapes for chilling and relaxing","en"],
          ["Minimal, emotional, melancholic piano","en"],
          ["A calm and romantic acoustic guitar melody","en"],
          ["horror suspense piano","en"],
          ["Big Band","en"],
          ["90 eurodance beat","en"],
]

outputs = [gr.Audio(label="Track 1"), gr.Audio(label="Track 2"), gr.Audio(label="Track 3"), gr.Audio(label="Track 4"), gr.Audio(label="Track 5")]
demo1 = gr.Interface(fn=process, inputs=inputs, outputs=outputs, examples=poc_examples, cache_examples=False, examples_per_page=20)

demo1.launch(debug=False)