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keep links()
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@coopwilliams
coopwilliams committed Feb 6, 2020
2 parents 85fa577 + 7975111 commit a091148
Showing 1 changed file with 38 additions and 26 deletions.
64 changes: 38 additions & 26 deletions Flask/application.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,7 +13,7 @@
from psycopg2_blob import seventoten,query2,id_to_title,get_imdb_users,imdb_user_lookup,read_users
from w2v_inference import *
from r2v_inference import *
from functions import multi_read,multi_jsonify,multi_read_json,rec_edit,multi_dump,multi_session,multi_load,bool_func
from functions import multi_read,multi_jsonify,multi_read_json,rec_edit,multi_dump,multi_session,multi_load,bool_func,links

application = Flask(__name__)
application.secret_key = 'secret_bee'
Expand Down Expand Up @@ -244,16 +244,16 @@ def resubmit():
id_list2 = recs['Movie ID'].to_list()
difference_list = list(set(id_list2).difference(set(id_list)))



recs['New Rec?'] = bool_func(recs['Movie ID'],difference_list)
recs['Title'] = highlight_watchlist(recs['Movie ID'], recs['Title'], val_list)
cols=recs.columns.to_list()
recs=recs[cols[-1:]+cols[:-1]] #puts New Rec? column as column number 1 or number 0 if you're a computer
session['id_list'] = json.dumps(id_list2)

'''session['id_list'] = json.dumps(id_list2)
session['checked_list'] = json.dumps(checked_list)
print('checked_list saved:', checked_list)
session['rejected_list'] = json.dumps(rejected_list)
session['rejected_list'] = json.dumps(rejected_list)'''
session['id_list'],session['checked_list'],session['rejected_list']=multi_dump([id_list2,checked_list,rejected_list])

session['recs'] = recs.to_json()
recs.drop(columns='Movie ID')
return render_template('public/re_recommendations.html',
Expand Down Expand Up @@ -293,12 +293,11 @@ def imdb_submit():
ratings = pd.read_csv(file, encoding='latin1')
#strip beginning ts
ratings['Const'] = ratings['Const'].str.strip('t')
#dropping what I think to be extraneous

ratings = ratings.drop(columns=['Title Type','Num Votes','Directors','Genres',
'URL','Release Date'], errors='ignore')
session['ratings'] = ratings.to_json()
# dump ratings and reviews into database and then call model on username.
# Said username is in the zipfile name<EZ>.

return render_template('public/imdb_submission.html',
name='Watched List', data=ratings.sort_values(by=['Date Rated'],
ascending=False).head().to_html(index=False))
Expand All @@ -309,7 +308,6 @@ def imdb_recommend():
Shows recommendations from your IMDB choices
'''


ratings = pd.read_json(session['ratings'])

bad_rate = int(request.form['bad_rate'])/2
Expand Down Expand Up @@ -343,25 +341,29 @@ def imdb_recommend():
columns=['Title', 'Year', 'URL', 'Avg. Rating',
'# Votes', 'Similarity Score','Movie ID'])

recs['Liked by fans of...'] = recs['Movie ID'].apply(lambda x: s.get_most_similar_title(x, good_list))
"""recs['Liked by fans of...'] = recs['Movie ID'].apply(lambda x: s.get_most_similar_title(x, good_list))
recs['URL'] = recs['URL'].apply(links)
recs['Vote Up'] = '<input type="checkbox" name="downvote" value=' \
+ recs['Movie ID'] + '> Good idea<br>'
recs['Vote Down'] = '<input type="checkbox" name="downvote" value=' \
+ recs['Movie ID'] + '> Hard No<br>'
+ recs['Movie ID'] + '> Hard No<br>'"""
recs = rec_edit(recs,good_list)
id_list = recs['Movie ID'].to_list()

session.clear()
session['recs'] = recs.to_json()
session['id_list'] = json.dumps(id_list)
'''session['id_list'] = json.dumps(id_list)
session['good_list'] = json.dumps(good_list)
session['bad_list'] = json.dumps(bad_list)
session['hist_list'] = json.dumps(hist_list)
session['val_list'] = json.dumps(val_list)
session['ratings_dict'] = json.dumps(ratings_dict)
session['good_rate'] = good_rate
session['ratings_dict'] = json.dumps(ratings_dict)'''
session['id_list'],session['good_list'],session['bad_list'],session['hist_list'],session['val_list'],session['ratings_dict']=multi_dump([id_list,good_list,bad_list,hist_list,val_list,ratings_dict])

'''session['good_rate'] = good_rate
session['bad_rate'] = bad_rate
session['extra_weight'] = extra_weight
session['extra_weight'] = extra_weight'''
session['good_rate'],session['bad_rate'],session['extra_weight']=multi_session([good_rate,bad_rate,extra_weight])

recs = recs.drop(columns = 'Movie ID')

Expand Down Expand Up @@ -430,8 +432,9 @@ def user_reviews():
name = request.form['Username']
df,ratings,reviews = imdb_user_lookup(name)

session['ratings']=ratings.to_json()
session['reviews']=reviews.to_json()
'''session['ratings']=ratings.to_json()
session['reviews']=reviews.to_json()'''
session['ratings'],session['reviews']=multi_jsonify([ratings,reviews])

return render_template('public/user_reviews.html', data=df.head(10).to_html(index=False), name=name)

Expand All @@ -440,8 +443,11 @@ def user_search_recommend():
'''
Shows recommendations from your Letterboxd choices
'''
ratings = pd.read_json(session['ratings'])
reviews = pd.read_json(session['reviews'])

'''ratings = pd.read_json(session['ratings'])
reviews = pd.read_json(session['reviews'])'''
ratings,reviews = multi_read_json(['ratings','reviews'])

watched = pd.DataFrame()
watchlist = pd.DataFrame()
bad_rate = int(request.form['bad_rate'])/2
Expand Down Expand Up @@ -504,12 +510,14 @@ def user_search_recommend():
hidden_df['Vote Down'] = '<input type="checkbox" name="downvote" value=' \
+ hidden_df['Movie ID'] + '> Hard No<br>'

recs['Liked by fans of...'] = recs['Movie ID'].apply(lambda x: s.get_most_similar_title(x, good_list))
"""recs['Liked by fans of...'] = recs['Movie ID'].apply(lambda x: s.get_most_similar_title(x, good_list))
recs['URL'] = recs['URL'].apply(links)
recs['Vote Up'] = '<input type="checkbox" name="upvote" value=' \
+ recs['Movie ID'] + '> Good idea<br>'
recs['Vote Down'] = '<input type="checkbox" name="downvote" value=' \
+ recs['Movie ID'] + '> Hard No<br>'
+ recs['Movie ID'] + '> Hard No<br>'"""
recs = rec_edit(recs,good_list)

id_list = recs['Movie ID'].to_list()

session.clear()
Expand All @@ -518,17 +526,21 @@ def user_search_recommend():
if cult:
session['cult_df'] = cult_df.to_json()
session['recs'] = recs.to_json()
session['id_list'] = json.dumps(id_list)

'''session['id_list'] = json.dumps(id_list)
session['good_list'] = json.dumps(good_list)
session['bad_list'] = json.dumps(bad_list)
session['hist_list'] = json.dumps(hist_list)
session['val_list'] = json.dumps(val_list)
session['ratings_dict'] = json.dumps(ratings_dict)
session['good_rate'] = good_rate
session['ratings_dict'] = json.dumps(ratings_dict)'''
session['id_list'],session['good_list'],session['bad_list'],session['hist_list'],session['val_list'],session['ratings_dict']=multi_dump([id_list,good_list,bad_list,hist_list,val_list,ratings_dict])

'''session['good_rate'] = good_rate
session['bad_rate'] = bad_rate
session['hidden'] = hidden
session['cult'] = cult
session['extra_weight'] = extra_weight
session['extra_weight'] = extra_weight'''
session['good_rate'],session['bad_rate'],session['hidden'],session['cult'],session['extra_weight']=multi_session([good_rate,bad_rate,hidden,cult,extra_weight])

recs = recs.drop(columns='Movie ID')

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