InHUMAN/Game-Recommendation-System

1import streamlit as st2import pickle3import pandas  as pd4from PIL import Image5import requests6from bs4 import BeautifulSoup7 8st.title('Game Recommendation System')9 10game_list=pickle.load(open('games.pkl','rb'))11similarity_list=pickle.load(open('similarities.pkl','rb'))12games=pd.DataFrame(game_list)13 14def game_recommender(game):15    game_index=games[games['name']==game].index[0]16    distances=sorted(list(enumerate(similarity_list[game_index])),reverse=True, key=lambda x:x[1])[1:10]17    game_recs=[]18    for i in distances:19        game_recs.append(games.iloc[i[0]]['name'])20    return game_recs21    22 23game_selected=st.selectbox('',(games['name']))24 25 26if st.button('Recommend'):27   28    game_recs=game_recommender(game_selected)29    for i in game_recs:30        word = f'latest {i} video game cover photo'31        url = 'https://www.google.com/search?q={0}&tbm=isch'.format(word)32        content = requests.get(url).content33        soup = BeautifulSoup(content,'lxml')34        images = soup.findAll('img')35        c=036        img_link=''37        for image in images:38            if c==1:39                img_link=image.get('src')40                break41            c+=142 43        im = Image.open(requests.get(img_link, stream=True).raw)44        st.header(i)45        st.image(im)46        47 48 49