app.py

from reader import get_article
import gradio as gr
from transformers import pipeline

info = get_article()

#article='''
# Team members
 #- Drishti Sharma [(DrishtiSharma)](https://huggingface.co/DrishtiSharma)
 #- Manuel Fernandez Moya  [(Mnauel)](https://huggingface.co/Mnauel)
 #- Antonio Alberto Soto Hernández [(Asotoh)](https://huggingface.co/Asotoh)
 #-
 #-
#'''

#Model_1 = "hackathon-pln-es/wav2vec2-base-finetuned-sentiment-classification-MESD"
#Model_2 ="hackathon-pln-es/wav2vec2-base-finetuned-sentiment-mesd"

#model_name2id = {"Model A": "hackathon-pln-es/wav2vec2-base-finetuned-sentiment-classification-MESD", "Model B": "hackathon-pln-es/wav2vec2-base-finetuned-sentiment-mesd"}

def classify_sentiment(audio):
  pipe = pipeline("audio-classification", model="hackathon-pln-es/wav2vec2-base-finetuned-sentiment-classification-MESD")
  pred = pipe(audio)
  return {dic["label"]: dic["score"] for dic in pred}

input_audio = [gr.inputs.Audio(source="microphone", type="filepath", label="Record/ Drop audio")]
label = gr.outputs.Label(num_top_classes=5)

################### Gradio Web APP ################################


#gr.Interface(
    #fn = classify_sentiment,
    #inputs = input_audio,
    #outputs = label,
    #examples=[["basta_neutral.wav"], ["detras_disgust.wav"], ["mortal_sadness.wav"], ["respiracion_happiness.wav"], ["robo_fear.wav"]],
    #title = "🔊 Audio Sentiment Classifier",
    #description = "Demostración de Gradio para la clasificación de sentimientos de audios usando Wav2Vec2",
    #theme="huggingface").launch()

# generate and launch interface
interface = gr.Interface(fn=classify_sentiment, inputs=input_audio, outputs=label, examples=[["basta_neutral.wav"], ["detras_disgust.wav"], ["mortal_sadness.wav"], ["respiracion_happiness.wav"], ["robo_fear.wav"]], article=info['article'], css=info['css'], theme='huggingface', title=info['title'], allow_flagging='never', description=info['description'])
interface.launch()