Update app.py

app.py

@@ -11,13 +11,8 @@ labels = learner.dls.vocab
 
 # Función para realizar predicciones
 def predict(img):
-    if isinstance(img, gr.Image):
-        img_array = np.array(img)
-    else:
-        img_array = np.array(gr.Image.from_file(img))
-
     # Realizar la predicción en la imagen proporcionada
-    pred, pred_idx, probs = learner.predict(img_array)
+    pred, pred_idx, probs = learner.predict(img)
     
     return {
         "Predictions": {labels[i]: float(probs[i]) for i in range(len(labels))},
@@ -29,9 +24,9 @@ image_dir = pathlib.Path('images')
 examples = [[path.as_posix()] for path in sorted(image_dir.glob('*.png'))]
 
 iface = gr.Interface(
-    predict,
-    gr.Image(type="pil"),
-    "image",
-    examples = examples,
+    fnpredict,
+    inputs=gr.Image(label='Input', type='numpy'),
+    outputs=gr.Image(label='Output', height=600),
+    examples=examples,
 )
 iface.launch(share=False)