Update app.py

app.py

@@ -1,20 +1,21 @@
+from huggingface_hub import from_pretrained_fastai
 import gradio as gr
 from fastai.vision.all import *
-import skimage
 
-learn = load_learner('export.pkl')
 
-labels = learn.dls.vocab
+
+# repo_id = "YOUR_USERNAME/YOUR_LEARNER_NAME"
+repo_id = "magarcd/intel-image-classification"
+
+learner = from_pretrained_fastai(repo_id)
+labels = learner.dls.vocab
+
+# Definimos una función que se encarga de llevar a cabo las predicciones
 def predict(img):
     img = PILImage.create(img)
-    pred,pred_idx,probs = learn.predict(img)
+    pred,pred_idx,probs = learner.predict(img)
     return {labels[i]: float(probs[i]) for i in range(len(labels))}
-
-title = "Clasificación de imágenes"
-description = "Demo para el clasificador construido en la práctica 1"
-article="<p style='text-align: center'><a href='https://ap2122.github.io/aprendizajeprofundo2122/practica/2022/01/10/practica1-clasificacion-imagenes.html' target='_blank'>Blog post</a></p>"
-examples = ['building.jpg','forest.jpg']
-interpretation='default'
-enable_queue=True
-
-gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(num_top_classes=3),title=title,description=description,article=article,examples=examples,interpretation=interpretation,enable_queue=enable_queue).launch(share=False)
+    
+# Creamos la interfaz y la lanzamos. 
+gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(128, 128)), outputs=gr.outputs.Label(num_top_classes=3),examples=['buildings.jpg','forest.jpg']).launch(share=False)
+