def show_tweets():
tweet_records = Tweet.query.all()
user_records = User.query.all()
print(tweet_records)
parsed_tweets = parse_records(tweet_records)
parsed_users = parse_records(user_records)
# return jsonify(parsed_tweets)
return render_template("tweets.html",
message="List of All Tweets",
tweets=parsed_tweets,
users=parsed_users)
def get_text_search():
"""Function/Endpoint takes the parameters send in a post request and passes
them to a model to return the matching strains based on the arguments that
were passed by the user.
Arguments:
-----------
None impicit arguments
text {str} : a String that the user send from an imput feild that is passed
to a model that tried to recommend strains based on their description.
Returns:
----------
data {jsonb} : the entries in the databse that match the resulting predicted strains
"""
text = request.form['text']
#init the model
m = modelone(PICKLE_DIR + 'nn_isaac.pickle',
PICKLE_DIR + 'tfidf_isaac.pickle')
# make predictions based on the passed string
m.transform_predict([text])
pred = m.getResults()[0]
print(pred)
data = Strains.query.filter(Strains.index.in_([int(x)
for x in pred])).all()
return jsonify(parse_records(data))
def get_search():
"""Function/Endpoint takes the parameters send in a post request and passes
them to a model to return the matching strains based on the arguments that
were passed by the user.
Arguments:
-----------
None impicit arguments
flavors {str} : the string of comma seperated flavors that is passed to the POST
request from an input form
effects {str} : a string of comma seperated values that passed with the POST
request from an input form
Returns:
----------
data {jsonb} : the entries in the databse that match the resulting predicted strains
"""
flavors = request.form['flavors']
effects = request.form['effects']
#init the model
m = modelone(PICKLE_DIR + 'nn_mark.pickle',
PICKLE_DIR + 'tfidf_mark.pickle')
# make predictions based on the passed string
m.transform_predict([effects + flavors])
pred = m.getResults()[0]
print(pred)
data = Strains.query.filter(Strains.index.in_([int(x)
for x in pred])).all()
return jsonify(parse_records(data))
def list_books():
# books = [
# {"id": 1, "title": "Book 1"},
# {"id": 2, "title": "Book 2"},
# {"id": 3, "title": "Book 3"},
# ]
book_records = Book.query.all()
print(book_records)
books = parse_records(book_records)
return jsonify(books)
def list_books_for_humans():
# books = [
# {"id": 1, "title": "Book 1"},
# {"id": 2, "title": "Book 2"},
# {"id": 3, "title": "Book 3"},
# ]
book_records = Book.query.all()
print(book_records)
books = parse_records(book_records)
return render_template("books.html",
message="Here's a list of All Books",
books=books)
def return_all():
db_comments = Comment.query.all()
comments_response = parse_records(db_comments)
json_comments = jsonify(comments_response)
return json_comments
#return render_template("users.html", users=db_comments.user,
# text=db_comments.text, sentiment=db_comments.sentiment)
#json_ids = jsonify(db_comment.id)
#json_users = jsonify(db_comment.user)
#json_text = jsonify(db_comment.text)
#json_sentiment = jsonify(db_comment.sentiment)
# @stat_routes.route('/stats/comments/ids')
# def return_ids():
# db_comments = Comment.query.all()
# comments_response = parse_records(db_comments)
# db_ids = comments_response['user']
# json_ids = jsonify(db_ids)
# return json_ids
# @stat_routes.route('/stats/comments/users')
# def return_users():
# db_comments = Comment.query.all()
# db_users = db_comments.user
# comments_response = parse_records(db_users)
# json_users = jsonify(comments_response)
# return json_users
# @stat_routes.route('/stats/comments/text')
# def return_text():
# db_comments = Comment.query.all()
# db_text = db_comments.text
# comments_response = parse_records(db_text)
# json_text = jsonify(comments_response)
# return json_text
# @stat_routes.route('/stats/comments/sentiment')
# def return_sentiment():
# db_comments = Comment.query.all()
# db_sentiment = db_comments.sentiment
# comments_response = parse_records(db_sentiment)
# json_sentiment = jsonify(comments_response)
# return json_sentiment
def get_user_by_name():
if request.method == 'GET':
users = parse_records(User.query.all())
return render_template('users_list.html', users=users)
# print("FORM DATA:", dict(request.form))
else:
user = dict(request.form)
# print(user["username"])
user = User.query.filter_by(screen_name=user["username"]).one_or_none()
if not user:
flash("User not found in database", "danger")
return redirect("/list")
else:
users = []
users.append(user)
return render_template('users_list.html', users=users)
def index():
user_records = User.query.all()
print(user_records)
users = parse_records(user_records)
return render_template("prediction_form.html", users=users)
def list_users():
db_users = User.query.all()
users_response = parse_records(db_users)
return jsonify(users_response)
def cabinet():
db_cabinet = Cabinet.query.all()
cabinet_response = parse_records(db_cabinet)
return jsonify(cabinet_response)
def recommender():
"""
creates list with top n recommended strains.
Paramaters
__________
request: dictionary (json object)
list of user's desired effects listed in order of user ranking.
{
"effects":[],
"negatives":[],
"ailments":[]
}
n: int, optional
number of recommendations to return, default 10.
Returns
_______
list_strains: python list of n recommended strains.
"""
desired_dict = request.json
n = 10
effects, negatives, ailments = (desired_dict.get("effects"),
desired_dict.get("negatives"),
desired_dict.get("ailments"))
effects = [effect.lower() for effect in effects]
negatives = [negative.lower() for negative in negatives]
ailments = [ailment.lower() for ailment in ailments]
for index, effect in enumerate(effects):
if effect in columns:
effects[index] = columns.index(effect)
for index, negative in enumerate(negatives):
if negative in columns:
negatives[index] = columns.index(negative)
for index, ailment in enumerate(ailments):
if ailment in columns:
ailments[index] = columns.index(ailment)
vector = [0 for _ in range(len(columns))]
weight = 100
for index in effects:
if isinstance(index, int):
vector[index] = weight
weight *= .8
weight = int(weight)
weight = 100
for index in negatives:
if isinstance(index, int):
vector[index] = weight
weight *= .8
weight = int(weight)
weight = 100
for index in ailments:
if isinstance(index, int):
vector[index] = weight
weight *= .8
weight = int(weight)
data = numpy.array(vector)
request_series = pd.Series(data, index=columns)
distance, neighbors = nn.kneighbors([request_series])
list_strains = []
for points in neighbors:
for index in points:
list_strains.append(index)
result = [{"id": str(val)} for val in list_strains[:n]]
return_list = [str(val) for val in list_strains[:n]]
records = parse_records(
Cabinet.query.filter(Cabinet.model_id.in_(return_list)).all())
return jsonify(records)
def get_cards():
"""Function/endpoint returns all of the entries in the database
"""
db_strains = Strains.query.all()
response = parse_records(db_strains)
return jsonify(response)