import os from fastai.vision.all import * import gradio as gr # Cargar los modelos learn_emotion = load_learner('emotions_jey.pkl') learn_emotion_labels = learn_emotion.dls.vocab learn_sentiment = load_learner('sentiment_jey.pkl') learn_sentiment_labels = learn_sentiment.dls.vocab # Función de predicción def predict(img_path): img = PILImage.create(img_path) pred_emotion, pred_emotion_idx, probs_emotion = learn_emotion.predict(img) pred_sentiment, pred_sentiment_idx, probs_sentiment = learn_sentiment.predict(img) emotions = {label: float(prob) for label, prob in zip(learn_emotion_labels, probs_emotion)} sentiments = {label: float(prob) for label, prob in zip(learn_sentiment_labels, probs_sentiment)} return emotions, sentiments # Interfaz de Gradio title = "Detector de emociones y sentimientos faciales " description = ( "Esta interfaz utiliza redes neuronales para detectar emociones y sentimientos a partir de imágenes faciales." ) article = "Esta herramienta proporciona una forma rápida de analizar emociones y sentimientos en imágenes." examples = [ 'PrivateTest_10131363.jpg', 'angry1.png', 'angry2.jpg', 'happy1.jpg', 'happy2.jpg', 'neutral1.jpg', 'neutral2.jpg' ] iface = gr.Interface( fn=predict, inputs=gr.Image(shape=(48, 48), image_mode='L'), outputs=[gr.Label(label='Emotion'), gr.Label(label='Sentiment')], title=title, examples=examples, description=description, article=article, allow_flagging='never' ) iface.launch(enable_queue=True)