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)