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| import streamlit as st | |
| import pandas as pd | |
| import re | |
| import nltk | |
| from PIL import Image | |
| import os | |
| import numpy as np | |
| import seaborn as sns | |
| from wordcloud import WordCloud, STOPWORDS | |
| from nltk.corpus import stopwords | |
| import datasets | |
| from datasets import load_dataset | |
| import matplotlib.pyplot as plt | |
| import sklearn | |
| from sklearn.preprocessing import LabelEncoder | |
| sns.set_palette('RdBu') | |
| #load dataset | |
| dataset = load_dataset('merve/poetry', streaming=True) | |
| df = pd.DataFrame.from_dict(dataset['train']) | |
| d = os.path.dirname(__file__) if '__file__' in locals() else os.getcwd() | |
| nltk.download('stopwords') | |
| stop = stopwords.words('english') | |
| def standardize(text, remove_digits=True): | |
| text = re.sub('[^a-zA-Z\d\s]', '', text) | |
| text = text.lower() | |
| return text | |
| st.set_option('deprecation.showPyplotGlobalUse', False) | |
| st.write('Poetry dataset, content character cleaned from special characters and lower cased') | |
| df.content = df.content.apply(lambda x: ' '.join(word for word in x.split() if word not in stop)) | |
| df.content = df.content.apply(standardize) | |
| st.dataframe(df) | |
| st.subheader('Visualization on dataset statistics') | |
| st.write('Number of peoms written in each type') | |
| sns.catplot(x='type', data=df, kind='count') | |
| plt.xticks(rotation=0) | |
| st.pyplot() | |
| st.write('Number of poems for each age') | |
| sns.catplot(x='age', data=df, kind='count') | |
| plt.xticks(rotation=0) | |
| st.pyplot() | |
| st.write("Number of poems for each author") | |
| sns.catplot(x="author", data=df, kind="count", aspect = 4) | |
| plt.xticks(rotation=90) | |
| st.pyplot() | |
| st.write('Distributions of poem types according to ages and authors, seems that folks in renaissance loved the love themed poems and nature themed poems became popular later') | |
| le = LabelEncoder() | |
| df.author = le.fit_transform(df.author) | |
| sns.catplot(x='age', y='author', hue='type', data=df) | |
| st.pyplot() | |
| words = df.content.str.split(expand=True).unstack().value_counts() | |
| renaissance = df.content.loc[df.age == 'Renaissance'].str.split(expand=True).unstack().value_counts() | |
| modern = df.content.loc[df.age == 'modern'].str.split(expand=True).unstack().value_counts() | |
| st.subheader('Visualizing content') | |
| mask = np.array(Image.open(os.path.join(d, 'poet.png'))) | |
| import matplotlib.pyplot as plt | |
| def word_cloud(content, title): | |
| wc = WordCloud(background_color='white', | |
| max_words=200, | |
| contour_width=3, | |
| stopwords=STOPWORDS, | |
| max_font_size=50) | |
| wc.generate(' '.join(content.index.values)) | |
| fig = plt.figure(figsize=(10, 10)) | |
| plt.title(title, fontsize=20) | |
| plt.imshow(wc.recolor(colormap='magma', random_state=42), cmap=plt.cm.gray, interpolation = "bilinear", alpha=0.98) | |
| plt.axis('off') | |
| st.pyplot() | |
| st.subheader('Most appearing words excluding stopwords n poems according to ages') | |
| word_cloud(modern, 'word cloud Renaissance poems') | |
| st.write('Most appearing words including stopwords') | |
| st.bar_chart(words[:50]) |