from transformers import pipeline
import gradio as gr
from gradio import Interface, Audio, Label, Number

username = 'bvallegc' ## Complete your username
model_id = f"{username}/wav2vec2_spoof_dection1-finetuned-spoofing-classifier"
pipe = pipeline("audio-classification", model=model_id)

def classify_audio(filepath):
    """
    Goes from
    [{'score': 0.8339303731918335, 'label': 'country'},
  {'score': 0.11914275586605072, 'label': 'rock'},]
   to
   {"country":  0.8339303731918335, "rock":0.11914275586605072}
  """
    preds = pipe(filepath)
    classification = [{"label": p["label"], "score": p["score"]} for p in preds]
    label = classification[0]["label"]
    number = classification[0]["score"]
    return label, number

examples=['TTS_F_LA_E_7682468.wav', 'TTS_M_LA_E_3371601.wav', 'TTS_M_LA_E_7056254.wav']
examples = [[f"./{f}"] for f in examples]

gr.Interface(
    fn = classify_audio,
    inputs=[
        gr.inputs.Audio(source="microphone", type='filepath', optional=True),
        gr.inputs.Audio(source="upload", type='filepath', optional=True),
        gr.Textbox(label="Paste audio here"),
    ],
    
    outputs=[
        gr.outputs.Textbox(label="Verification"),
        gr.Number(label="Probability"),
    ],
    
    verbose=True,
    examples = examples,
    title="Spoofing verification classifier",
    description="Detect machine created audios from human-speech.",
    theme="huggingface"
).launch()