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| from flask import Flask | |
| app = Flask(__name__) | |
| @app.route('/') | |
| def hello_world(): | |
| return 'Hello, World!' | |
| if __name__ == '__main__': | |
| app.run() |
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| from flask import Flask | |
| app = Flask(__name__) | |
| @app.route('/') | |
| def index(): | |
| return 'Index Page' | |
| @app.route('/hello') | |
| def hello(): | |
| return 'Hello, World' | |
| @app.route('/users/<username>') | |
| def show_user_profile(username): | |
| # show the user profile for that user | |
| return 'User %s' % username | |
| if __name__ == '__main__': | |
| app.run() |
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| from flask import Flask, jsonify | |
| app = Flask(__name__) | |
| @app.route('/users') | |
| @app.route('/users/<int:id>') | |
| def users(id=None): | |
| print(id) | |
| users = [ | |
| {'id': 1, 'name': 'Alice'}, | |
| {'id': 2, 'name': 'Bob'}, | |
| {'id': 3, 'name': 'Charlie'}] | |
| if id is None: | |
| return jsonify(users) | |
| elif isinstance(id, int) and 1 <= id <= 3: | |
| return jsonify({'id': id, 'name': users[id-1]['name']}) | |
| else: | |
| return jsonify({}) | |
| if __name__ == '__main__': | |
| app.run() |
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| import os | |
| from flask import Flask, request, redirect, url_for, jsonify | |
| from werkzeug.utils import secure_filename | |
| UPLOAD_FOLDER = 'uploaded' | |
| os.makedirs(UPLOAD_FOLDER, exist_ok=True) | |
| ALLOWED_EXTENSIONS = set(['png', 'jpg', 'jpeg', 'gif']) | |
| app = Flask(__name__) | |
| app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER | |
| def allowed_file(filename): | |
| return '.' in filename and \ | |
| filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS | |
| @app.route('/', methods=['GET', 'POST']) | |
| def upload_file(): | |
| if request.method == 'POST': | |
| # check if the post request has the file part | |
| if 'file' not in request.files: | |
| return redirect(request.url) | |
| file = request.files['file'] | |
| # if user does not select file, browser also | |
| # submit a empty part without filename | |
| if file.filename == '': | |
| return redirect(request.url) | |
| if file and allowed_file(file.filename): | |
| filename = secure_filename(file.filename) | |
| file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename)) | |
| return jsonify(filename=filename, | |
| type=filename.rsplit('.', 1)[1].lower()) | |
| return ''' | |
| <!doctype html> | |
| <title>Upload new File</title> | |
| <h1>Upload new File</h1> | |
| <form method=post enctype=multipart/form-data> | |
| <p><input type=file name=file> | |
| <input type=submit value=Upload> | |
| </form> | |
| ''' | |
| if __name__ == '__main__': | |
| app.run() |
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| import os | |
| from flask import Flask, request, redirect, url_for, jsonify, Response | |
| from werkzeug.utils import secure_filename | |
| from keras.applications.vgg16 import VGG16, preprocess_input, decode_predictions | |
| from keras.preprocessing import image | |
| import numpy as np | |
| from PIL import Image | |
| import io | |
| app = Flask(__name__) | |
| model = None | |
| def load_model(): | |
| global model | |
| model = VGG16(weights='imagenet', include_top=True) | |
| @app.route('/', methods=['GET', 'POST']) | |
| def upload_file(): | |
| response = {'success': False} | |
| if request.method == 'POST': | |
| if request.files.get('file'): | |
| img_requested = request.files['file'].read() | |
| img = Image.open(io.BytesIO(img_requested)) | |
| if img.mode != 'RGB': | |
| img = img.convert('RGB') | |
| img = img.resize((224, 224)) | |
| img = image.img_to_array(img) | |
| img = np.expand_dims(img, axis=0) | |
| inputs = preprocess_input(img) | |
| preds = model.predict(inputs) | |
| results = decode_predictions(preds) | |
| response['predictions'] = [] | |
| for (imagenetID, label, prob) in results[0]: | |
| row = {'label': label, 'probability': float(prob)} | |
| response['predictions'].append(row) | |
| response['success'] = True | |
| return jsonify(response) | |
| return ''' | |
| <!doctype html> | |
| <title>Upload new File</title> | |
| <h1>Upload new File</h1> | |
| <form method=post enctype=multipart/form-data> | |
| <p><input type=file name=file> | |
| <input type=submit value=Upload> | |
| </form> | |
| ''' | |
| if __name__ == '__main__': | |
| load_model() | |
| # no-thread: https://github.com/keras-team/keras/issues/2397#issuecomment-377914683 | |
| # avoid model.predict runs before model initiated | |
| app.run(threaded=False) |
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| from keras.applications.vgg16 import VGG16 | |
| model = VGG16(weights='imagenet', include_top=True) | |
| print(model.summary()) |
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| from keras.applications.vgg16 import VGG16, preprocess_input, decode_predictions | |
| from keras.preprocessing import image | |
| import numpy as np | |
| img_path = 'cat.jpg' # change to your file | |
| model = VGG16(weights='imagenet', include_top=True) | |
| img = image.load_img(img_path, target_size=(224, 224)) | |
| x = image.img_to_array(img) | |
| x = np.expand_dims(x, axis=0) | |
| x = preprocess_input(x) | |
| pred = model.predict(x) | |
| label = decode_predictions(pred) | |
| print(label) |
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| absl-py==0.2.1 | |
| astor==0.6.2 | |
| bleach==1.5.0 | |
| click==6.7 | |
| Flask==1.0.2 | |
| gast==0.2.0 | |
| grpcio==1.12.0 | |
| h5py==2.7.1 | |
| html5lib==0.9999999 | |
| itsdangerous==0.24 | |
| Jinja2==2.10 | |
| Keras==2.1.6 | |
| Markdown==2.6.11 | |
| MarkupSafe==1.0 | |
| numpy==1.14.3 | |
| Pillow==5.1.0 | |
| protobuf==3.5.2.post1 | |
| PyYAML==3.12 | |
| scipy==1.1.0 | |
| six==1.11.0 | |
| tensorboard==1.8.0 | |
| tensorflow==1.8.0 | |
| termcolor==1.1.0 | |
| Werkzeug==0.14.1 |
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