Create a0p.py

a0p.py

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+from flask import Flask, request, jsonify
+from transformers import pipeline
+from PIL import Image
+import requests
+import logging
+import torch
+from transformers import pipeline
+
+print("Loading model...")
+
+#torch.backends.cuda.matmul.allow_tf32 = True
+#torch.backends.cudnn.allow_tf32 = True
+
+pipe = pipeline("image-classification", model="./checkpoint-600")
+
+print("Starting webapp...")
+
+app = Flask(__name__)
+log = logging.getLogger('werkzeug')
+log.disabled = True
+app.logger.disabled = True
+
+print("Ready")
+
+@app.route("/")
+def classify_image():
+    global pipe
+    
+    if url is None:
+        return jsonify(error="Url is required", url=None, label=None)
+    
+    image = Image.open(requests.get(url, stream=True).raw)
+
+    output = pipe(images=[image])
+
+    return jsonify(url=url, result=output)