Create app.py

app.py

@@ -0,0 +1,37 @@
+from flask import Flask, request, jsonify
+from transformers import ViTFeatureExtractor, ViTForImageClassification
+from PIL import Image
+import requests
+
+print("Loading models...")
+
+feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch32-384')
+model = ViTForImageClassification.from_pretrained('google/vit-base-patch32-384')
+
+print("Starting webapp...")
+
+app = Flask(__name__)
+
+print("Ready")
+
+@app.route("/")
+def hello_world():
+    global feature_extractor, model
+    
+    url = request.args.get('url')
+    
+    if url is None:
+        return jsonify(error="Url is required", url=None, classes=[])
+    
+    image = Image.open(requests.get(url, stream=True).raw)
+    
+    inputs = feature_extractor(images=image, return_tensors="pt")
+    outputs = model(**inputs)
+    logits = outputs.logits
+    
+    print(logits)
+
+    # model predicts one of the 1000 ImageNet classes
+    predicted_class_idx = logits.argmax(-1).item()
+
+    return jsonify(url=url, classes=model.config.id2label[predicted_class_idx])