Create app.py

app.py

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+from fastai.vision.all import load_learner
+import gradio as gr
+
+#import pathlib
+#temp=pathlib.PosixPath
+#pathlib.PosixPath=pathlib.WindowsPath
+
+
+food_labels = [
+    'Baked Potato',
+    'Crispy Chicken',
+    'Donut',
+    'Fries',
+    'Hot Dog',
+    'Sandwich',
+    'Taco',
+    'Taquito',
+    'Apple Pie',
+    'Burger',
+    'Butter Naan',
+    'Chai',
+    'Chapati',
+    'Cheesecake',
+    'Chicken Curry',
+    'Chole Bhature',
+    'Dal Makhani',
+    'Dhokla',
+    'Fried Rice',
+    'Ice Cream',
+    'Idli',
+    'Jalebi',
+    'Kaathi Rolls',
+    'Kadai Paneer',
+    'Kulfi',
+    'Masala Dosa',
+    'Momos',
+    'Omelette',
+    'Paani Puri',
+    'Pakode',
+    'Pancakes',
+    'Quesadilla',
+    'Ratatouille',
+    'Samosa',
+    'Spring Rolls',
+    'Stuffed Bell Peppers',
+    'Tandoori Chicken',
+    'Uttapam',
+    'Veggie Burger',
+    'Waffles',
+    'Zucchini Bread']
+
+model=load_learner("food_model_v2.pkl")
+
+def recognize_image(image):
+  #import pdb;pdb.set_trace()
+  pred,idx,probs=model.predict(image)
+  print(pred)
+  return dict(zip(jersey_labels, map(float,probs)))
+
+image = gr.Image()
+label = gr.Label()
+examples = [
+   "food1.jpg",
+   "food2.jpg",
+   "food3.jpg",
+   "food4.jpg"]
+
+iface = gr.Interface(fn=recognize_image, inputs=image, outputs=label,examples=examples)
+iface.launch(inline=False,share=True)