Upload cosas.txt

cosas.txt

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+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
+
+# Diccionario de mapeo de etiquetas en inglés a etiquetas en español
+label_mapping = {
+    'angry': 'enojado',
+    'disgust': 'asco',
+    'fear': 'miedo',
+    'happy': 'feliz',
+    'sad': 'triste',
+    'surprise': 'sorpresa',
+    'neutral': 'neutral',
+    'negative': 'negativo',
+    'positive': 'positivo'
+}
+
+# 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_mapping[label]: float(prob) for label, prob in zip(learn_emotion_labels, probs_emotion)}
+    sentiments = {label_mapping[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='Emoción'), gr.Label(label='Sentimiento')],
+    title=title,
+    examples=examples,
+    description=description,
+    article=article,
+    allow_flagging='never'
+)
+
+iface.launch(enable_queue=True)
+
+
+
+
+
+
+#################
+
+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)
+
+