Spaces:
Runtime error
Runtime error
File size: 4,947 Bytes
ca4c9d6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 |
import streamlit as st
import pandas as pd
from skimage import io
import matplotlib.pyplot as plt
from wordcloud import WordCloud
import itertools
import scipy
import numpy as np
import validators
from ast import literal_eval
from sklearn.metrics.pairwise import sigmoid_kernel
from sklearn.metrics.pairwise import linear_kernel
from sklearn.feature_extraction.text import TfidfVectorizer
def show_Cover(url: str) -> None:
a = io.imread(url)
plt.imshow(a)
plt.axis('off')
plt.show()
@st.experimental_memo
def preprocess_lists(df: pd.DataFrame, column_list: list) -> pd.DataFrame:
for column in column_list:
string = column + '_treated'
df_hold = df.loc[:,column]
df_hold = df_hold.apply(lambda x: literal_eval(x) if len(x) > 2 else [])
df[string] = df_hold
df.drop(column, axis = 1, inplace = True)
return df
def gen_wordcloud(df: pd.DataFrame, column_name: str) -> None:
list_wc = df[column_name].tolist()
list_wc = list(itertools.chain(*list_wc))
strings = ' '.join(list_wc)
plt.figure(figsize=(10,10))
wordcloud = WordCloud(max_words=100,background_color="white",width=800, height=400, min_font_size = 10).generate(strings)
fig, ax = plt.subplots(figsize = (10, 10))
ax.imshow(wordcloud)
plt.axis("off")
plt.tight_layout(pad=0)
st.pyplot(fig)
@st.cache
def vect_Tfid(series: pd.Series) -> scipy.sparse.csr_matrix:
tfv = TfidfVectorizer(min_df=3, max_features=None,
analyzer='word',
ngram_range=(1, 3),
stop_words = 'english')
return tfv.fit_transform(series)
def sim_score(df: pd.DataFrame, kernel: str = 'sigmoid') -> np.ndarray:
tfv_matrix = vect_Tfid(df['synopsis'])
if kernel == 'sigmoid':
return sigmoid_kernel(tfv_matrix, tfv_matrix)
elif kernel == 'linear':
return linear_kernel(tfv_matrix, tfv_matrix)
@st.cache
def get_rec(entry: str, df: pd.DataFrame, sug_num: int, rec_type: str) -> pd.DataFrame:
idx = pd.Series(df.index, index=df['title']).drop_duplicates()[entry]
df_sim = list(enumerate(sim_score(df, rec_type)[idx]))
sim_scores = sorted(df_sim, key = lambda x: x[1], reverse = True)
sim_recs = sim_scores[1:sug_num]
anime_indices = [y[0] for y in sim_recs]
return df['title'].iloc[anime_indices]
def data_frame_demo() -> None:
@st.experimental_memo
def get_Anime_data() -> None:
df = pd.read_csv('./myanimelist/anime.csv')
return df
@st.experimental_memo
def preprocess(dataframe: pd.DataFrame) -> pd.DataFrame:
columns = ['title', 'type', 'score', 'scored_by', 'status', 'episodes', 'members',
'favorites', 'rating', 'sfw', 'genres', 'themes', 'demographics',
'studios', 'producers', 'licensors','synopsis']
return dataframe[columns]
df = get_Anime_data()
df_pred = preprocess(df)
df_pred.fillna(value = 'Not Found in MAL', inplace=True)
list_columns = ['genres','themes','demographics','studios'
,'producers','licensors']
df_pred = preprocess_lists(df_pred, list_columns)
anime_list = st.multiselect(
"Choose some anime", list(df.title)
)
#st.dataframe(df.head()) Used for testing
#st.dataframe(df_pred.head()) Used for testing
if not anime_list:
st.error("Please select an anime.")
else:
df_subset = df[df["title"].isin(anime_list)]
r_type = st.selectbox('Which kernel to be used for the recommendation?',
('sigmoid', 'linear'))
rec_num = st.slider('How many recommendations?', 10, 50, 20)
for anime, picture, url, trailer in zip(anime_list, df_subset.main_picture, df_subset.url, df_subset.trailer_url):
col1, col2, col3 = st.columns([2,4,4])
with col1:
st.write(f'Anime selected: {anime}')
#st.dataframe(df_subset) used for testing
st.image(picture, caption = picture)
st.write(f'[MAL page]({url})')
if validators.url(trailer):
st.video(trailer)
with col2:
rec_list = get_rec(anime, df_pred, rec_num, r_type)
rec_df = df_pred[df_pred["title"].isin(rec_list)]
st.dataframe(rec_df[['title','licensors_treated','sfw']],
height=550, width= 810)
with col3:
gen_wordcloud(rec_df,'genres_treated')
gen_wordcloud(rec_df,'themes_treated')
st.set_option('deprecation.showPyplotGlobalUse', False)
st.set_page_config(page_title="Recommendation", page_icon="🏮", layout="wide")
st.markdown("# Anime Suggestion")
st.sidebar.header("Anime Suggestion")
st.write(
"""In this section choose an anime or a theme that you really like. The model will take care of the rest. Enjoy!"""
)
data_frame_demo()
|