Trying another pathing

app.py

@@ -1,9 +1,9 @@
 import pathlib
 import platform
-from fastai.vision.all import load_learner
 import gradio as gr
+from fastai.vision.all import load_learner
+from PIL import Image
 
-# If the operating system is not Windows, we patch pathlib.WindowsPath to be pathlib.PosixPath
 if platform.system() != 'Windows':
     pathlib.WindowsPath = pathlib.PosixPath
 
@@ -11,15 +11,27 @@ EXPORT_PATH = "export.pkl"
 learn_inf = load_learner(EXPORT_PATH)
 
 def classify_image(img):
-    img = PILImage.create(img)
-    pred,pred_idx,probs = learn_inf.predict(img)
-    return dict(zip(learn_inf.dls.vocab, map(float, probs)))
+    """Classifies an image according to three categories: dung beetle, elephant, or dolphin.
+
+    Args:
+        img (any): Any image will be converted to expected type. 
+
+    Returns:
+        _type_: Probabilies according to the three types. 
+    """
+    # Convert the image to a format the model expects
+    img = Image.fromarray(img.astype('uint8'), 'RGB')
+    # Make a prediction
+    pred_class, pred_idx, probs = learn_inf.predict(img)
+    # Return the result
+    return {learn_inf.dls.vocab[i]: float(probs[i]) for i in range(len(learn_inf.dls.vocab))}
 
 demo = gr.Interface(
-    fn=classify_image,
-    inputs=gr.inputs.Image(label='Upload your image'),
-    outputs=gr.outputs.Label(num_top_classes=3)
-)
+    title = "A dung beetle / dolphin / elephant image classifier",
+    fn=classify_image, 
+    inputs = gr.Image(
+        label = 'Upload an image of a dung beetle, a dolphin, or an elephant!'),
+    outputs="label")
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()