Update app.py

app.py

@@ -1,4 +1,93 @@
 from transformers import AutoFeatureExtractor, AutoModelForImageClassification
 import gradio
+import torch
+from einops import rearrange
+import numpy
 
-gradio.Interface.load(name='huggingface/vincentclaes/mit-indoor-scenes').launch()
+extractor = AutoFeatureExtractor.from_pretrained("vincentclaes/mit-indoor-scenes")
+model = AutoModelForImageClassification.from_pretrained("vincentclaes/mit-indoor-scenes")
+
+labels = {
+    "0": "airport_inside",
+    "1": "artstudio",
+    "2": "auditorium",
+    "3": "bakery",
+    "4": "bar",
+    "5": "bathroom",
+    "6": "bedroom",
+    "7": "bookstore",
+    "8": "bowling",
+    "9": "buffet",
+    "10": "casino",
+    "11": "children_room",
+    "12": "church_inside",
+    "13": "classroom",
+    "14": "cloister",
+    "15": "closet",
+    "16": "clothingstore",
+    "17": "computerroom",
+    "18": "concert_hall",
+    "19": "corridor",
+    "20": "deli",
+    "21": "dentaloffice",
+    "22": "dining_room",
+    "23": "elevator",
+    "24": "fastfood_restaurant",
+    "25": "florist",
+    "26": "gameroom",
+    "27": "garage",
+    "28": "greenhouse",
+    "29": "grocerystore",
+    "30": "gym",
+    "31": "hairsalon",
+    "32": "hospitalroom",
+    "33": "inside_bus",
+    "34": "inside_subway",
+    "35": "jewelleryshop",
+    "36": "kindergarden",
+    "37": "kitchen",
+    "38": "laboratorywet",
+    "39": "laundromat",
+    "40": "library",
+    "41": "livingroom",
+    "42": "lobby",
+    "43": "locker_room",
+    "44": "mall",
+    "45": "meeting_room",
+    "46": "movietheater",
+    "47": "museum",
+    "48": "nursery",
+    "49": "office",
+    "50": "operating_room",
+    "51": "pantry",
+    "52": "poolinside",
+    "53": "prisoncell",
+    "54": "restaurant",
+    "55": "restaurant_kitchen",
+    "56": "shoeshop",
+    "57": "stairscase",
+    "58": "studiomusic",
+    "59": "subway",
+    "60": "toystore",
+    "61": "trainstation",
+    "62": "tv_studio",
+    "63": "videostore",
+    "64": "waitingroom",
+    "65": "warehouse",
+    "66": "winecellar"
+  }
+
+def classify(image):
+  model.eval()
+  with torch.no_grad():
+    inputs = extractor(images=image, return_tensors='pt')
+    outputs = model(**inputs).logits
+    outputs = rearrange(outputs, '1 j->j')
+    outputs = outputs.cpu().numpy()
+    outputs = (numpy.exp(outputs)) / (numpy.sum(numpy.exp(outputs)))
+    return {labels[str(i)]: float(outputs[i]) for i in range(len(labels))}
+
+gradio.Interface(fn=classify,
+                 inputs=gradio.inputs.Image(shape=(224,224), image_mode='RGB', source='upload', tool='editor', type='pil', label=None, optional=False),
+                 outputs=gradio.outputs.Label(num_top_classes=5, type='auto'),
+                 allow_flagging='never').launch(inbrowser=True, debug=True)