add web app (incomplete)

ssswww4444 · Oct 19, 2020 · 11a92a9 · 11a92a9
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diff --git a/.env b/.env
@@ -0,0 +1,3 @@
+export HAP_DB_NAME=hap
+export HAP_DB_HOST=localhost
+export HAP_DB_PORT=27017
diff --git a/app.py b/app.py
@@ -0,0 +1,23 @@
+from config import create_app, db
+from views import public_route_bp
+from controller import api_bp_new, api_bp_old
+
+
+app = create_app()
+"""
+register blueprint
+"""
+app.register_blueprint(public_route_bp)
+app.register_blueprint(api_bp_old)
+app.register_blueprint(api_bp_new, url_prefix='/api')
+
+app.app_context().push()
+
+
+def main():
+    app.run(debug=True, host="0.0.0.0", port=5000)  # RUNNING APP MAKE debug =FALSE for Production Env
+    # app.run(debug=True, host="localhost", port=8888)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/config.py b/config.py
@@ -0,0 +1,50 @@
+from flask import Flask
+from flask_mongoengine import MongoEngine
+from deepface import DeepFaceLite
+# import tensorflow as tf
+# from tensorflow.python.keras.backend import set_session
+import os
+
+"""
+initialize the ML engine
+"""
+db = MongoEngine()
+# sess = tf.Session()
+# graph = tf.get_default_graph()
+# set_session(sess)
+deepface = DeepFaceLite()
+
+"""
+database environment variable
+"""
+DB_NAME = os.environ.get('HAP_DB_NAME')
+DB_HOST = os.environ.get('HAP_DB_HOST')
+DB_PORT = os.environ.get('HAP_DB_PORT')
+DB_USERNAME = os.environ.get('HAP_DB_USERNAME')
+DB_PASSWORD = os.environ.get('HAP_DB_PASSWORD')
+
+
+def create_app():
+    """
+    return app instance
+    :return: app instance
+    """
+    app = Flask(__name__)
+    if DB_USERNAME is not None and DB_PASSWORD is not None:
+        app.config['MONGODB_SETTINGS'] = {
+            'db': DB_NAME,
+            'host': DB_HOST,
+            'port': int(DB_PORT),
+            'username': DB_USERNAME,
+            'password': DB_PASSWORD
+        }
+    else:
+        app.config['MONGODB_SETTINGS'] = {
+            'db': DB_NAME,
+            'host': DB_HOST,
+            'port': int(DB_PORT)
+        }
+
+    db.init_app(app)
+
+    return app
diff --git a/controller/__init__.py b/controller/__init__.py
@@ -0,0 +1,24 @@
+from flask_restplus import Api
+from flask import Blueprint
+from controller.prediction_controller import api as predict_apis
+from controller.feedback_controller import api as feedback_apis
+from controller.general_controller import api as general_apis
+
+
+# old api, will be discard later
+api_bp_old = Blueprint('api_old', __name__)
+
+api = Api(api_bp_old)
+
+api.add_namespace(predict_apis)
+api.add_namespace(general_apis)
+
+"""
+new api with /api/ path
+"""
+api_bp_new = Blueprint('api_new', __name__)
+
+api_new = Api(api_bp_new)
+api_new.add_namespace(general_apis)
+api_new.add_namespace(predict_apis)
+api_new.add_namespace(feedback_apis)
diff --git a/controller/feedback_controller.py b/controller/feedback_controller.py
@@ -0,0 +1,32 @@
+from flask import request
+from extension.utilServices import send_json_response
+from flask_restplus import Namespace, Resource
+import datetime
+api = Namespace('feedback', path='/feedback', description='prediction related operations')
+
+
+@api.route('')
+class Feedback(Resource):
+    @api.doc('feedback related api')
+    def post(self):
+        """
+        store user feedbacks to the database
+        :return: operation results
+        """
+        from models.feedback import Feedback, FeedbackContent
+
+        message = request.get_json(force=True)
+
+        try:
+            new_feedback = Feedback()
+            new_feedback_content = []
+            for each_content in message['data']:
+                feedback_content = FeedbackContent(**each_content)
+                new_feedback_content.append(feedback_content)
+
+            new_feedback.content = new_feedback_content
+            new_feedback.date = datetime.datetime.now()
+            new_feedback.save()
+            return send_json_response({}, 200)
+        except Exception as error:
+            return send_json_response({'msg': str(error)}, 500)
diff --git a/controller/general_controller.py b/controller/general_controller.py
@@ -0,0 +1,190 @@
+from extension.utilServices import (send_json_response)
+from extension.constants import CONSTANTS
+import csv
+import re
+from flask_restplus import Namespace, Resource
+import datetime
+import copy
+
+api = Namespace('general', path='/', description='general information related to app')
+
+
+# @api.route('/get-breed-info')
+# class BreedList(Resource):
+#     @api.doc('get breed information from csv')
+#     def get(self):
+#         """
+#         return the breed information, which gets from wikipedia
+#         :return: list of breed information
+#         """
+#         breed_info = {}
+#         for animal_type in CONSTANTS['ANIMAL_TYPE']:
+#             with open('wikiFile/' + animal_type + '.csv') as csv_file:
+#                 csv_reader = csv.reader(csv_file, delimiter=',')
+#                 line_count = 0
+#                 json_title = []
+#                 animal_list = []
+#                 for row in csv_reader:
+#                     if line_count == 0:
+#                         json_title = row
+#                         line_count += 1
+#                     else:
+#                         info_obj = {
+#                             json_title[0]: row[0],
+#                             json_title[1]: row[1],
+#                             json_title[2]: row[2],
+#                             json_title[3]: row[3],
+#                             json_title[4]: row[4],
+#                         }
+#                         animal_list.append(info_obj)
+#                         line_count += 1
+#                 csv_file.close()
+#                 breed_info[animal_type] = animal_list
+
+#         return send_json_response(breed_info, 200)
+
+
+@api.route('/get-app-info')
+class AppInfo(Resource):
+    @api.doc('return the app developer information')
+    def get(self):
+        """
+        return the footer information
+        :return: return the app related information
+        """
+        app_info = {
+            'developedBy': 'This app was developed by the Melbourne eResearch Group (www.eresearch.unimelb.edu.au) within the School of Computing and Information Systems (https://cis.unimelb.edu.au) at The University of Melbourne (www.unimelb.edu.au). ',
+            'description': 'The app uses artificial intelligence (convolutional neural networks) that have been trained on dog/cat images to identify whether a dog/cat is in an image, and if so the species type (breed) and it\'s emotion.',
+            'contact': 'https://eresearch.unimelb.edu.au',
+            'developedByHTML': '<p>This app was developed by the Melbourne eResearch Group (<a href=\"www.eresearch.unimelb.edu.au\" target=\"_blank\">www.eresearch.unimelb.edu.au</a>) within the School of Computing and Information Systems (<a href=\"https://cis.unimelb.edu.au\" target=\"_blank\">https://cis.unimelb.edu.au</a>) at The University of Melbourne (<a href=\"www.unimelb.edu.au\" target=\"_blank\">www.unimelb.edu.au</a>).</p>',
+            'descriptionHTML': '<p>The app uses artificial intelligence (convolutional neural networks) that have been trained on dog/cat images to identify whether a dog/cat is in an image, and if so the species type (breed) and it\'s emotion.</p>',
+            'contactHTML': '<p>Please contact us at: <a href=\"eresearch.unimelb.edu.au\" target=\"_blank\">eresearch.unimelb.edu.au</a></p>'
+        }
+
+        return send_json_response(app_info, 200)
+
+
+@api.route('/get-statistical-results')
+class StatisticalData(Resource):
+    @api.doc('return statistical data')
+    def get(self):
+        """
+        return the statistical information
+        query across feedback and prediction model
+        :return: return statistical information
+        """
+        from models.prediction import Prediction
+        from models.feedback import Feedback
+
+        total_cat_breed = []
+        total_dog_breed = []
+        prediction_collection = Prediction._get_collection()
+        feedback_collection = Feedback._get_collection()
+        statistical_data = copy.deepcopy(CONSTANTS['STATISTICAL_DATA'])
+
+        # initial statistical_data based on breed and alpha order
+        f = open("model_data/pet_classes.txt", "r")
+        for each_line in f:
+            first_char = each_line[0]
+            if first_char.isupper():
+                total_cat_breed.append(re.sub(r"_", " ", each_line.rstrip(), flags=re.IGNORECASE).title())
+            else:
+                total_dog_breed.append(re.sub(r"_", " ", each_line.rstrip(), flags=re.IGNORECASE).title())
+        f.close()
+        total_cat_breed.sort()
+        total_dog_breed.sort()
+
+        for each_breed in total_cat_breed:
+            statistical_data['Cat']['prediction_data']['breed'][each_breed] = 0
+            statistical_data['Cat']['feedback_data']['breed']['wrong'][each_breed] = 0
+            statistical_data['Cat']['feedback_data']['breed']['correct'][each_breed] = 0
+
+        for each_breed in total_dog_breed:
+            statistical_data['Dog']['prediction_data']['breed'][each_breed] = 0
+            statistical_data['Dog']['feedback_data']['breed']['wrong'][each_breed] = 0
+            statistical_data['Dog']['feedback_data']['breed']['correct'][each_breed] = 0
+
+        # photo by date
+        today = datetime.datetime.today().replace(hour=0, minute=0, second=0, microsecond=0)
+        total_number_of_photo_within_one_week = {
+            (today - datetime.timedelta(days=6)).strftime('%d/%m/%Y'): prediction_collection.count_documents(
+                {'date': {'$lt': today - datetime.timedelta(days=5), '$gte': today - datetime.timedelta(days=6)}}),
+            (today - datetime.timedelta(days=5)).strftime('%d/%m/%Y'): prediction_collection.count_documents(
+                {'date': {'$lt': today - datetime.timedelta(days=4), '$gte': today - datetime.timedelta(days=5)}}),
+            (today - datetime.timedelta(days=4)).strftime('%d/%m/%Y'): prediction_collection.count_documents(
+                {'date': {'$lt': today - datetime.timedelta(days=3), '$gte': today - datetime.timedelta(days=4)}}),
+            (today - datetime.timedelta(days=3)).strftime('%d/%m/%Y'): prediction_collection.count_documents(
+                {'date': {'$lt': today - datetime.timedelta(days=2), '$gte': today - datetime.timedelta(days=3)}}),
+            (today - datetime.timedelta(days=2)).strftime('%d/%m/%Y'): prediction_collection.count_documents(
+                {'date': {'$lt': today - datetime.timedelta(days=1), '$gte': today - datetime.timedelta(days=2)}}),
+            (today - datetime.timedelta(days=1)).strftime('%d/%m/%Y'): prediction_collection.count_documents(
+                {'date': {'$lt': today, '$gte': today - datetime.timedelta(days=1)}}),
+            today.strftime('%d/%m/%Y'): prediction_collection.count_documents(
+                {'date': {'$gte': today}}),
+        }
+
+        # prediction
+        total_prediction = prediction_collection.find({})
+        for each_prediction in total_prediction:
+            prediction_results = each_prediction['predictionResults']
+            for each_result in prediction_results:
+                statistical_data[each_result['type']]['prediction_number'] = statistical_data[each_result['type']].get(
+                    'prediction_number', 0) + 1
+                statistical_data[each_result['type']]['prediction_data']['breed'][each_result['breed']] = \
+                    statistical_data[each_result['type']]['prediction_data']['breed'].get(each_result['breed'], 0) + 1
+                statistical_data[each_result['type']]['prediction_data']['emotion'][each_result['emotion']] = \
+                    statistical_data[each_result['type']]['prediction_data']['emotion'].get(each_result['emotion'],
+                                                                                            0) + 1
+        # feedback
+        total_feedback = feedback_collection.find({})
+
+        for each_feedback in total_feedback:
+            feedback_content = each_feedback['content']
+            for each_content in feedback_content:
+                statistical_data[each_content['type']]['feedback_number'] = statistical_data[each_content['type']].get(
+                    'feedback_number', 0) + 1
+
+                if not each_content['breedCorrectness']:
+                    statistical_data[each_content['type']]['feedback_data']['breed']['wrong'][
+                        each_content['breedFeedback']] = \
+                        statistical_data[each_content['type']]['feedback_data']['breed'][
+                            'wrong'].get(each_content['breedFeedback'], 0) + 1
+                else:
+                    statistical_data[each_content['type']]['feedback_data']['breed']['correct'][
+                        each_content['breedFeedback']] = \
+                        statistical_data[each_content['type']]['feedback_data']['breed']['correct'].get(
+                            each_content['breedFeedback'], 0) + 1
+
+                if not each_content['emotionCorrectness']:
+                    statistical_data[each_content['type']]['feedback_data']['emotion']['wrong'][
+                        each_content['emotionFeedback']] = \
+                        statistical_data[each_content['type']]['feedback_data']['emotion']['wrong'].get(
+                            each_content['emotionFeedback'], 0) + 1
+                else:
+                    statistical_data[each_content['type']]['feedback_data']['emotion']['correct'][
+                        each_content['emotionFeedback']] = \
+                        statistical_data[each_content['type']]['feedback_data']['emotion']['correct'].get(
+                            each_content['emotionFeedback'], 0) + 1
+
+        result = {
+            'totalNumberOfPhotoUploaded': prediction_collection.count_documents({}),
+            'totalNumberOfCatPrediction': statistical_data['Cat']['prediction_number'],
+            'totalNumberOfDogPrediction': statistical_data['Dog']['prediction_number'],
+            'totalNumberOfCatFeedback': statistical_data['Cat']['feedback_number'],
+            'totalNumberOfDogFeedback': statistical_data['Dog']['feedback_number'],
+            'totalNumberOfDogBreedPrediction': statistical_data['Dog']['prediction_data']['breed'],
+            'totalNumberOfDogBreedCorrectFeedback': statistical_data['Dog']['feedback_data']['breed']['correct'],
+            'totalNumberOfDogBreedWrongFeedback': statistical_data['Dog']['feedback_data']['breed']['wrong'],
+            'totalNumberOfDogEmotionPrediction': statistical_data['Dog']['prediction_data']['emotion'],
+            'totalNumberOfDogEmotionCorrectFeedback': statistical_data['Dog']['feedback_data']['emotion']['correct'],
+            'totalNumberOfDogEmotionWrongFeedback': statistical_data['Dog']['feedback_data']['emotion']['wrong'],
+            'totalNumberOfCatBreedPrediction': statistical_data['Cat']['prediction_data']['breed'],
+            'totalNumberOfCatBreedCorrectFeedback': statistical_data['Cat']['feedback_data']['breed']['correct'],
+            'totalNumberOfCatBreedWrongFeedback': statistical_data['Cat']['feedback_data']['breed']['wrong'],
+            'totalNumberOfCatEmotionPrediction': statistical_data['Cat']['prediction_data']['emotion'],
+            'totalNumberOfCatEmotionCorrectFeedback': statistical_data['Cat']['feedback_data']['emotion']['correct'],
+            'totalNumberOfCatEmotionWrongFeedback': statistical_data['Cat']['feedback_data']['emotion']['wrong'],
+            'numberOfPhotoByDate': total_number_of_photo_within_one_week
+        }
+
+        return send_json_response(result, 200)