# Path: deepanimebot/classifiers.py
# def fetch_cvimage_from_url(url, maxsize=10 * 1024 * 1024):
#     def classify(self, *args, **kwargs):
#     def __init__(self, dataset_path, input_shape, model_name='model'):
#     def classify(self, cvimage):
#     def __init__(self, image_classifier):
#     def classify(self, url=None):
#     def __init__(self, base_url):
#     def classify(self, **params):
# class MockClassifier(object):
# class ImageClassifier(object):
# class URLClassifier(object):
# class RemoteClassifier(object):
# 
# Path: deepanimebot/exceptions.py
# class NotImage(Exception): pass
# class RemoteError(Exception): pass
# 
# Path: deepanimebot/messages.py
# class Messages(object):
#     '''Each method is expected to return a message of length under TWEET_MAX_LENGTH.
#     '''
#     @staticmethod
#     def took_too_long():
#         return at_random(
#             "It took too long to get the image. Try again?",
#         )
# 
#     @staticmethod
#     def something_went_wrong():
#         return at_random(
#             "Something went wrong. Try again later?",
#         )
# 
#     @staticmethod
#     def not_an_image():
#         return at_random(
#             "That doesn't look like an image",
#             "Are you sure it's an image?",
#         )
# 
#     @staticmethod
#     def unknown_image():
#         return at_random(
#             'I have no clue!',
#             'Unknown',
#         )
# 
#     @classmethod
#     def my_guess(cls, y, top_n=3, max_length=None, preface="Probable Anime:"):
#         if not len(y):
#             return cls.unknown_image()
# 
#         pred_lines = []
#         max_category_length = 0
#         max_category_length_index = 0
# 
#         for i, pred in enumerate(y[:top_n]):
#             pred_lines.append(deploy.Prediction(
#                 "{}.".format(pred.rank),
#                 pred.category,
#                 "{:.2%}".format(pred.probability),
#             ))
#             if max_category_length < len(pred.category):
#                 max_category_length_index = i
#                 max_category_length = len(pred.category)
# 
#         newline_count = len(pred_lines)
#         pred_length = sum(sum(map(len, pred)) + len(pred) - 1 for pred in pred_lines)
#         current_length = len(preface) + newline_count + pred_length
# 
#         # truncate category name(s) if needed
#         if max_length is not None and current_length > max_length:
#             lengthy_pred = pred_lines[max_category_length_index]
#             excess_length = current_length - max_length
#             # don't penalize the longest category if it's going to be truncated too much
#             if len(lengthy_pred.category) * 0.5 < excess_length:
#                 subtract_from_everyone_length = int(math.ceil(excess_length / len(pred_lines)))
#                 pred_lines = [
#                     deploy.Prediction(
#                         pred.rank, pred.category[:-subtract_from_everyone_length], pred.probability)
#                     for pred in pred_lines]
#             else:
#                 shortened_pred = deploy.Prediction(
#                     lengthy_pred.rank, lengthy_pred.category[:-excess_length], lengthy_pred.probability)
#                 pred_lines[max_category_length_index] = shortened_pred
# 
#         reply = "{}\n{}".format(preface, "\n".join(" ".join(pred) for pred in pred_lines))
#         return reply[:max_length] if max_length is not None else reply


from flask import Flask, Blueprint, current_app, request, render_template, jsonify
from deepanimebot import classifiers
from deepanimebot import exceptions as exc
from deepanimebot.messages import Messages
@bp.route('/api/v1/deepanimebot/classify_by_url')
def api_v1_classify():
    maybe_image_url = request.args.get('url')
    if maybe_image_url is None:
        return jsonify(error='provide url of image as `url` query param'), 400

    message = None
    try:
        y = current_app.extensions['classifier'].classify(url=maybe_image_url)
        return jsonify(y=y)
    except exc.TimeoutError:
        current_app.logger.debug("timed out while classifying {}".format(maybe_image_url))
        message = messages.took_too_long()
    except exc.NotImage:
        current_app.logger.debug("no image found at {}".format(maybe_image_url))
        message = messages.not_an_image()
    except Exception as e:
        current_app.logger.error("error while classifying {}: {}".format(maybe_image_url, e))
        message = messages.something_went_wrong()
    return jsonify(error=message), 500


def create_app():
    app = Flask(__name__)
    app.config.setdefault('DATASET_PATH', 'data/data.h5py')
    app.config.setdefault('INPUT_SHAPE', 128)
    app.config.setdefault('MODEL_NAME', 'deep_anime_model')
    app.register_blueprint(bp)

    app.extensions['classifier'] = classifiers.URLClassifier(

# Path: deepanimebot/classifiers.py
# def fetch_cvimage_from_url(url, maxsize=10 * 1024 * 1024):
#     def classify(self, *args, **kwargs):
#     def __init__(self, dataset_path, input_shape, model_name='model'):
#     def classify(self, cvimage):
#     def __init__(self, image_classifier):
#     def classify(self, url=None):
#     def __init__(self, base_url):
#     def classify(self, **params):
# class MockClassifier(object):
# class ImageClassifier(object):
# class URLClassifier(object):
# class RemoteClassifier(object):
# 
# Path: deepanimebot/exceptions.py
# class NotImage(Exception): pass
# class RemoteError(Exception): pass
# 
# Path: deepanimebot/messages.py
# class Messages(object):
#     '''Each method is expected to return a message of length under TWEET_MAX_LENGTH.
#     '''
#     @staticmethod
#     def took_too_long():
#         return at_random(
#             "It took too long to get the image. Try again?",
#         )
# 
#     @staticmethod
#     def something_went_wrong():
#         return at_random(
#             "Something went wrong. Try again later?",
#         )
# 
#     @staticmethod
#     def not_an_image():
#         return at_random(
#             "That doesn't look like an image",
#             "Are you sure it's an image?",
#         )
# 
#     @staticmethod
#     def unknown_image():
#         return at_random(
#             'I have no clue!',
#             'Unknown',
#         )
# 
#     @classmethod
#     def my_guess(cls, y, top_n=3, max_length=None, preface="Probable Anime:"):
#         if not len(y):
#             return cls.unknown_image()
# 
#         pred_lines = []
#         max_category_length = 0
#         max_category_length_index = 0
# 
#         for i, pred in enumerate(y[:top_n]):
#             pred_lines.append(deploy.Prediction(
#                 "{}.".format(pred.rank),
#                 pred.category,
#                 "{:.2%}".format(pred.probability),
#             ))
#             if max_category_length < len(pred.category):
#                 max_category_length_index = i
#                 max_category_length = len(pred.category)
# 
#         newline_count = len(pred_lines)
#         pred_length = sum(sum(map(len, pred)) + len(pred) - 1 for pred in pred_lines)
#         current_length = len(preface) + newline_count + pred_length
# 
#         # truncate category name(s) if needed
#         if max_length is not None and current_length > max_length:
#             lengthy_pred = pred_lines[max_category_length_index]
#             excess_length = current_length - max_length
#             # don't penalize the longest category if it's going to be truncated too much
#             if len(lengthy_pred.category) * 0.5 < excess_length:
#                 subtract_from_everyone_length = int(math.ceil(excess_length / len(pred_lines)))
#                 pred_lines = [
#                     deploy.Prediction(
#                         pred.rank, pred.category[:-subtract_from_everyone_length], pred.probability)
#                     for pred in pred_lines]
#             else:
#                 shortened_pred = deploy.Prediction(
#                     lengthy_pred.rank, lengthy_pred.category[:-excess_length], lengthy_pred.probability)
#                 pred_lines[max_category_length_index] = shortened_pred
# 
#         reply = "{}\n{}".format(preface, "\n".join(" ".join(pred) for pred in pred_lines))
#         return reply[:max_length] if max_length is not None else reply


# Path: deepanimebot/webapp.py
from flask import Flask, Blueprint, current_app, request, render_template, jsonify
from deepanimebot import classifiers
from deepanimebot import exceptions as exc
from deepanimebot.messages import Messages

@bp.route('/api/v1/deepanimebot/classify_by_url')
def api_v1_classify():
    maybe_image_url = request.args.get('url')
    if maybe_image_url is None:
        return jsonify(error='provide url of image as `url` query param'), 400

    message = None
    try:
        y = current_app.extensions['classifier'].classify(url=maybe_image_url)
        return jsonify(y=y)
    except exc.TimeoutError:
        current_app.logger.debug("timed out while classifying {}".format(maybe_image_url))
        message = messages.took_too_long()
    except exc.NotImage:
        current_app.logger.debug("no image found at {}".format(maybe_image_url))
        message = messages.not_an_image()
    except Exception as e:
        current_app.logger.error("error while classifying {}: {}".format(maybe_image_url, e))
        message = messages.something_went_wrong()
    return jsonify(error=message), 500


def create_app():
    app = Flask(__name__)
    app.config.setdefault('DATASET_PATH', 'data/data.h5py')
    app.config.setdefault('INPUT_SHAPE', 128)
    app.config.setdefault('MODEL_NAME', 'deep_anime_model')
    app.register_blueprint(bp)

    app.extensions['classifier'] = classifiers.URLClassifier(

        classifiers.ImageClassifier(