import torch
import torch.nn.functional as F

from transformers import WhisperForConditionalGeneration, WhisperProcessor
from transformers.models.whisper.tokenization_whisper import LANGUAGES
from transformers.pipelines.audio_utils import ffmpeg_read

import gradio as gr


device = "cuda" if torch.cuda.is_available() else "CPU"

model_ckpt = "ivanlau/language-detection-fine-tuned-on-xlm-roberta-base"
model = AutoModelForSequenceClassification.from_pretrained(model_ckpt)
tokenizer = AutoTokenizer.from_pretrained(model_ckpt)

def detect_language(sentence):
    tokenized_sentence = tokenizer(sentence, return_tensors='pt')
    output = model(**tokenized_sentence)
    predictions = torch.nn.functional.softmax(output.logits, dim=-1)
    probability, pred_idx = torch.max(predictions, dim=-1)
    language = LANGUANGE_MAP[pred_idx.item()]
    return language, probability.item()


def process_audio_file(file):
    with open(file, "rb") as f:
        inputs = f.read()

    audio = ffmpeg_read(inputs, sampling_rate)
    return audio

def transcribe(Microphone, File_Upload):
    warn_output = ""
    if (Microphone is not None) and (File_Upload is not None):
        warn_output = "WARNING: You've uploaded an audio file and used the microphone. " \
                      "The recorded file from the microphone will be used and the uploaded audio will be discarded.\n"
        file = Microphone

    elif (Microphone is None) and (File_Upload is None):
        return "ERROR: You have to either use the microphone or upload an audio file"

    elif Microphone is not None:
        file = Microphone
    else:
        file = File_Upload

    audio_data = process_audio_file(file)

    input_features = processor(audio_data, return_tensors="pt").input_features
    
    with torch.no_grad():
        logits = model.forward(input_features.to(device), decoder_input_ids=decoder_input_ids).logits
    
    pred_ids = torch.argmax(logits, dim=-1)
    transcription = processor.decode(pred_ids[0])
    
    detect_language(transcription.capitalize())


examples=['sample1.mp3', 'sample2.mp3', 'sample3.mp3']

outputs=gr.outputs.Label(label="Language detected:")
article = """
Fine-tuned on xlm-roberta-base model.\n
Supported languages:\n 
    'Arabic', 'Basque', 'Breton', 'Catalan', 'Chinese_China', 'Chinese_Hongkong', 'Chinese_Taiwan', 'Chuvash', 'Czech', 
    'Dhivehi', 'Dutch', 'English', 'Esperanto', 'Estonian', 'French', 'Frisian', 'Georgian', 'German', 'Greek', 'Hakha_Chin', 
    'Indonesian', 'Interlingua', 'Italian', 'Japanese', 'Kabyle', 'Kinyarwanda', 'Kyrgyz', 'Latvian', 'Maltese', 
    'Mangolian', 'Persian', 'Polish', 'Portuguese', 'Romanian', 'Romansh_Sursilvan', 'Russian', 'Sakha', 'Slovenian', 
    'Spanish', 'Swedish', 'Tamil', 'Tatar', 'Turkish', 'Ukranian', 'Welsh'
"""

gr.Interface(
    fn=detect_language,
    fn=transcribe,
    inputs=[
        gr.inputs.Audio(source="microphone", type='filepath', optional=True),
        gr.inputs.Audio(source="upload", type='filepath', optional=True),
    ],
    
    outputs=outputs=[
        gr.outputs.Textbox(label="Language"),
        gr.Number(label="Probability"),
    ],

    verbose=True,
    examples = examples,
    title="Language Identification from Audio",
    description="Detect the Language from Audio.",
    article=article,
    theme="huggingface"
).launch()