Update app.py

app.py

@@ -1,28 +1,28 @@
-import gradio as gr
-from fastai.vision.all import *
-
-learn = load_learner('model.pkl')
-labels = ['brown_spots', 'green', 'spoiled', 'yellow']
-
-
-def predict(img):
-    img = PILImage.create(img)
-    pred,pred_idx,probs = learn.predict(img)
-    return {labels[i]: float(probs[i]) for i in range(len(labels))}
-
-title = "Banana Quality Classifier"
-description = "A banana classifier that can classify bananas into 4 categories based on their quality"
-examples = ['yellow_banana.jpg', 'green_banana.jpg', 'banana_with_brown_spots.jpg', 'black_banana.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,
-    examples=examples,
-    interpretation=interpretation,
-    enable_queue=enable_queue
+import gradio as gr
+from fastai.vision.all import *
+
+learn = load_learner('model.pkl')
+labels = ['brown_spots', 'green', 'spoiled', 'yellow']
+
+
+def predict(img):
+    img = PILImage.create(img)
+    pred,pred_idx,probs = learn.predict(img)
+    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+
+title = "Banana Quality Classifier"
+description = "A banana classifier that can classify bananas into 4 categories based on their quality"
+examples = ['yellow_banana.jpg', 'green_banana.jpg', 'banana_with_brown_spots.jpg', 'black_banana.jpg', 'banana.jpg']
+interpretation='default'
+enable_queue=True
+
+gr.Interface(
+    fn=predict,
+    inputs=gr.inputs.Image(shape=(512, 512)),
+    outputs=gr.outputs.Label(num_top_classes=4),
+    title=title,
+    description=description,
+    examples=examples,
+    interpretation=interpretation,
+    enable_queue=enable_queue
 ).launch()