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Main.py
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Main.py
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import streamlit as st
import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics.pairwise import cosine_similarity
with st.sidebar:
st.image('https://dodo.ac/np/images/5/52/NH_Logo_English.png', use_column_width='auto')
st.audio('https://dodo.ac/np/images/4/48/NH_Main_Theme.flac', format='audio/flac', start_time=0)
st.write('---')
data = pd.read_csv('acnh_villagers.csv')
st.title('Villagers Recommendation')
random_rows = data.sample(n=1)
st.caption(random_rows['nh_details.quote'].values[0] + ' - ' + random_rows['Name'].values[0])
st.write('---')
st.write('Select the filters that you wish to apply:')
filtcol1, filtcol2 = st.columns(2)
with filtcol1:
filter1 = st.selectbox('Species filter',
[None] + list(data['Species'].unique()))
with filtcol2:
filter2 = st.selectbox('Gender filter',
[None, "Male", "Female"])
# data multiple filter
if filter1 is not None:
data = data[data['Species'] == filter1]
if filter2 is not None:
data = data[data['Gender'] == filter2]
data['PersonalitySub'] = data['Personality'] + data['nh_details.sub-personality']
data['Birthday'] = data['birthday_month'].astype(str) + ' ' + data['birthday_day'].astype(str)
df = data.copy()
main_out = ['Name', 'Species', 'Gender', 'Hobby', 'Birthday',
'Personality', 'Catchphrase', 'Favorite Song',
'Style 1', 'Style 2', 'Color 1', 'Color 2']
# st.write(data[main_out])
st.write('---')
option = st.selectbox(
'Select a villager',
data['Name'].unique())
st.write("---")
st.write('You selected:', option)
# st.write(data[data['Name'] == option][main_out])
st.caption(data[data['Name'] == option]['nh_details.quote'].values[0])
vil1, vil2 = st.columns(2)
with vil1:
st.image(data[data['Name'] == option]['nh_details.image_url'].values[0], width=150)
with vil2:
st.write('Species: ', data[data['Name'] == option]['Species'].values[0])
st.write('Gender: ', data[data['Name'] == option]['Gender'].values[0])
st.write('Hobby: ', data[data['Name'] == option]['Hobby'].values[0])
st.write('Birthday: ', data[data['Name'] == option]['Birthday'].values[0])
st.write('Personality: ', data[data['Name'] == option]['Personality'].values[0])
st.write('Catchphrase: ', data[data['Name'] == option]['Catchphrase'].values[0])
st.write('Favorite Song: ', data[data['Name'] == option]['Favorite Song'].values[0])
st.write('Style: ', data[data['Name'] == option]['Style 1'].values[0] + ', ' + data[data['Name'] == option]['Style 2'].values[0])
st.write('Color: ', data[data['Name'] == option]['Color 1'].values[0] + ', ' + data[data['Name'] == option]['Color 2'].values[0])
features = ['Species', 'Gender', 'Hobby', 'PersonalitySub', 'Catchphrase',
'Favorite Song', 'Style 1', 'Style 2', 'Color 1', 'Color 2',
'birthday_month', 'sign', 'Birthday', 'nh_details.quote']
def clean_data(x):
if isinstance(x, list):
return [str.lower(i.replace(" ", "")) for i in x]
else:
if isinstance(x, str):
return str.lower(x.replace(" ", ""))
else:
return ''
# Apply clean_data function to your features.
for feature in features:
df[feature] = df[feature].apply(clean_data)
df['soup'] = df[features].apply(lambda row: ' '.join(row.values.astype(str)), axis=1)
count = CountVectorizer(stop_words='english')
count_matrix = count.fit_transform(df['soup'])
cosine_sim = cosine_similarity(count_matrix, count_matrix)
# Reset index of your main DataFrame and construct reverse mapping as before
df = df.reset_index()
indices = pd.Series(df.index, index=df['Name'])
def get_recommendations(name, cosine_sim):
idx = indices[name]
sim_scores = list(enumerate(cosine_sim[idx]))
sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)
sim_scores = sim_scores[1:11]
vill_indices = [i[0] for i in sim_scores]
return df['Name'].iloc[vill_indices]
villagers = get_recommendations(option, cosine_sim)
st.write("---")
st.write('Here are the villagers that you might like:')
st.write(data[data['Name'].isin(villagers)][main_out])
lst_vill = list(villagers)
tablst = st.tabs(lst_vill)
for i in range(len(tablst)):
with tablst[i]:
st.caption(data[data['Name'] == lst_vill[i]]['nh_details.quote'].values[0])
col1, col2, col3, col4, col5 = st.tabs(['Image', 'Photo', 'Icon', 'House Interior', 'House Exterior'])
with col1:
st.image(data[data['Name'] == lst_vill[i]]['nh_details.image_url'].values[0], width=150)
with col2:
st.image(data[data['Name'] == lst_vill[i]]['nh_details.photo_url'].values[0], width=150)
with col3:
st.image(data[data['Name'] == lst_vill[i]]['nh_details.icon_url'].values[0], width=150)
with col4:
st.image(data[data['Name'] == lst_vill[i]]['nh_details.house_interior_url'].values[0], width=300)
with col5:
st.image(data[data['Name'] == lst_vill[i]]['nh_details.house_exterior_url'].values[0], width=300)