Update app.py

changed inputs to components

app.py

@@ -2,33 +2,25 @@ import gradio as gr
 from fastai.vision.all import *
 import skimage
 
-# Load the trained model
 learn = load_learner('ripeorrotten_apple.pkl')
 
-# Define the labels for your model
-labels = ['ripe', 'rotten']
-
-# Define the prediction function
+labels = ('ripe', 'rotten')
 def predict(img):
     img = PILImage.create(img)
-    pred, pred_idx, probs = learn.predict(img)
+    pred,pred_idx,probs = learn.predict(img)
     return {labels[i]: float(probs[i]) for i in range(len(labels))}
 
-# Customize the Gradio interface
 title = "Apple Ripeness Classifier"
 description = "Is your apple ripe or rotten? Use this AQCC (apple quality control classifier) trained on web images with fastai."
-article = "<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>"
-
-# Example images (ensure these are valid paths or URLs)
+article="<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>"
 examples = ['apple-fruit-ripe.jpg']
-interpretation = 'default'
-enable_queue = True
+interpretation='default'
+enable_queue=True
 
-# Create and launch the Gradio interface
 gr.Interface(
     fn=predict,
-    inputs=gr.inputs.Image(shape=(512, 512)),
-    outputs=gr.outputs.Label(num_top_classes=2),
+    inputs=gr.components.Image(shape=(512, 512)),
+    outputs=gr.components.Label(num_top_classes=3),
     title=title,
     description=description,
     article=article,