Added full model

app.py

@@ -1,23 +1,16 @@
 from fastai.vision.all import *
 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]
+learn=load_learner("model.pkl")
 
+labels=learn.dls.vocab
 def sample_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))}
 
 intf=gr.Interface(fn=sample_predict, 
-                  inputs=gr.inputs.Image(shape=(512, 512)), 
-                  outputs=gr.outputs.Label(num_top_classes=3))
+                  inputs=gr.components.Image(shape=(256, 256)), 
+                  outputs=gr.components.Label(num_top_classes=3))
 
 intf.launch()