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app.py

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+import os
+from fastai.vision.all import *
+import gradio as gr
+
+# Cargar los modelos
+learn_emotion = load_learner('/content/drive/MyDrive/carpeta de PIB/emotions_javier_almeida.pkl')
+learn_emotion_labels = learn_emotion.dls.vocab
+
+learn_sentiment = load_learner('/content/drive/MyDrive/carpeta de PIB/sentiment_javier_almeida.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 para parciales finales de PIB"
+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 = [
+    '/content/drive/MyDrive/carpeta de PIB/angry1.png',
+    '/content/drive/MyDrive/carpeta de PIB/neutral1.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)

emotions_jey.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:335570ccf5133bc1fc0d4cc9bb7835b72fd457a64c0cdefbbc863f1c24cc4454
+size 82965753

sentiment_jey.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e6fc0f18d5a082cd835b6b8cbd32041170be0ac20b7ca555df1209e2e4d181c3
+size 82957497