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app.py

@@ -1,2 +1,23 @@
 from fastai.vision.all import *
-import gradio as gr
+import gradio as gr
+
+labels = ['apple', 'banana', 'beetroot', 'bell pepper', 'cabbage', 'capsicum', 'carrot', 'cauliflower', 'chilli pepper', 'corn', 'cucumber', 'eggplant', 'garlic', 'ginger', 'grapes', 'jalepeno', 'kiwi', 'lemon', 'lettuce', 'mango', 'onion', 'orange', 'paprika', 'pear', 'peas', 'pineapple', 'pomegranate', 'potato', 'raddish', 'soy beans', 'spinach', 'sweetcorn', 'sweetpotato', 'tomato', 'turnip', 'watermelon'] #learn.dls.vocab
+
+probs=[9.9996e-01, 1.1584e-08, 1.8556e-07, 1.6986e-06, 3.5755e-08,
+1.3057e-06, 2.7230e-08, 5.8373e-08, 1.3209e-08, 1.8147e-10,
+2.8197e-08, 9.4939e-09, 8.8776e-09, 7.7736e-08, 3.0117e-08,
+2.8444e-09, 9.8802e-09, 5.9252e-08, 1.9288e-08, 1.2375e-07,
+8.2446e-08, 1.6437e-06, 2.4753e-08, 1.1622e-05, 6.6616e-09,
+1.8746e-08, 1.6764e-05, 9.2635e-07, 2.6931e-09, 1.1366e-08,
+1.7266e-09, 1.7419e-08, 3.6652e-08, 2.4264e-07, 8.6940e-08,
+1.0518e-07]
+
+def sample_predict(img):
+    img = PILImage.create(img)
+    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+
+intf=gr.Interface(fn=predict, 
+                  inputs=gr.inputs.Image(shape=(512, 512)), 
+                  outputs=gr.outputs.Label(num_top_classes=3))
+
+intf.launch(share=True)