def recommend():
user_data = str(request.form['user_input'].encode('utf-8'))
# --- Drive to the given URL, scrape and generate recs -- #
scraped = profile_crawl(user_data)
quora = scraped['text']
# Read and clean Quora dump recommendations
read = TextParser()
read.df = master_df
filtered = read.preprocess_quora(quora)
clean_quora = read.clean_up(filtered)
pickle.dump(clean_quora, open("data/clean_quora.pkl", "wb"))
rec = Recommender()
test = rec.vectorize()
top_ten_ind = rec.recommend()
recs = read.df.ix[top_ten_ind]
recs = recs.reset_index()
recs['img_link'] = map(get_image, recs['title'])
recs.loc[recs['type']=='course', 'img_link'] = 'http://www.michaellenox.com/wp-content/uploads/2014/07/coursera_square_logo.jpg'
recs = recs[0:20] # Get the top twenty recommendations
return render_template('testing.html', data = recs[['title', 'img_link']].values)
def recommend():
user_data = str(request.form['user_input'])
profile_crawl(user_data)
df = main()
return render_template('index.html', data = df['img_link'].values)