Update app.py

app.py

@@ -5,16 +5,31 @@ import skimage
 learn = load_learner('model.pkl')
 
 labels = learn.dls.vocab
+
 def predict(img):
     img = PILImage.create(img)
-    pred,pred_idx,probs = learn.predict(img)
+    pred, pred_idx, probs = learn.predict(img)
+    
+    # Check if the predicted label is 'Other'
+    if pred == 'Other':
+        return 'The model can\'t recognize the image, adjust your camera angle and take a close picture of a leaf'
+    
     return {labels[i]: float(probs[i]) for i in range(len(labels))}
 
-title = "Common beans diseases classfier"
-description = "An app for Common beans diseases Classisfication"
-article="<p style='text-align: center'>The app identifies and classifies common beans diseases: Anthracnose and Bean rust.</p>"
+title = "Common beans diseases classifier"
+description = "An app for Common beans diseases Classification"
+article = "<p style='text-align: center'>The app identifies and classifies common beans diseases: Anthracnose and Bean rust.</p>"
 examples = ['image.jpeg']
-interpretation='default'
-enable_queue=True
+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,article=article,examples=examples,interpretation=interpretation,enable_queue=enable_queue).launch()
+# Modify the output to Textbox to handle string messages as well
+gr.Interface(fn=predict,
+             inputs=gr.inputs.Image(shape=(512, 512)),
+             outputs=gr.outputs.Textbox(),
+             title=title,
+             description=description,
+             article=article,
+             examples=examples,
+             interpretation=interpretation,
+             enable_queue=enable_queue).launch()