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from flask import Flask, request, jsonify
from transformers import ViTFeatureExtractor, ViTForImageClassification
from PIL import Image
import requests
import logging
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__)
log = logging.getLogger('werkzeug')
log.disabled = True
app.logger.disabled = True
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, label=None)
image = Image.open(requests.get(url, stream=True).raw)
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
return jsonify(url=url, label=model.config.id2label[predicted_class_idx]) |