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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") | |
| # loading 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') | |
| # standardizing dataset by removing special characters and lowercasing | |
| 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 column cleaned from special characters and lowercased") | |
| 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 poems 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() | |
| # distributions of poem types according to ages and authors | |
| 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() | |
| # most appearing words other than stop words | |
| 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 in poems according to ages") | |
| word_cloud(modern, "Word Cloud of Modern Poems") | |
| word_cloud(renaissance, "Word Cloud Renaissance Poems") | |
| # most appearing words including stopwords | |
| st.write("Most appearing words including stopwords") | |
| st.bar_chart(words[0:50]) |