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app.py

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+from huggingface_hub import from_pretrained_fastai
+import gradio as gr
+from fastai.vision.all import *
+
+
+repo_id = "nlmaldonadog/pract1-firstmodel-chest-xray"
+
+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 = learner.predict(img)
+    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+
+# Creamos la interfaz y la lanzamos.
+lista = [
+  'NORMAL2-IM-0035-0001.jpeg',
+  'NORMAL2-IM-0349-0001.jpeg',
+  'NORMAL2-IM-0374-0001.jpeg',
+  'person78_bacteria_386.jpeg',
+  'person83_bacteria_407.jpeg',
+  'person91_bacteria_447.jpeg'
+]
+gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(128, 128)), outputs=gr.outputs.Label(num_top_classes=2),examples=lista).launch(share=False)

requirements.txt

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+fastai
+toml