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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() |