import streamlit as st import pandas as pd import streamlit.components.v1 as stc import docx2txt # NLP Package-used for text analysis import nltk from nltk.tokenize import word_tokenize from nltk.tag import pos_tag from nltk.stem import WordNetLemmatizer from nltk.corpus import stopwords # from nltk import ne_chunk from nltk.tag import StanfordNERTagger from collections import Counter from textblob import TextBlob import seaborn as sns import matplotlib.pyplot as plt from wordcloud import WordCloud import base64 import time from app_utils import * HTML_BANNER = """ <div style="background-color:green;padding:10px;border-radius:10px"> <h1 style="color:white;text-align:center;">Text Analysis App </h1> </div> """ def text_analysis(): stc.html(HTML_BANNER) menu=['Text-analysis','Upload_Files'] choice=st.sidebar.selectbox('Menu',menu) if choice=='Text-analysis': st.subheader('Analyse Text') text=st.text_area("Enter the text to anlayze") if (st.button("Analyze")): st.success("Success") with st.expander('Original Text'): st.write(text) with st.expander('Text Analysis'): token_analysis=nlp_analysis(text) st.dataframe(token_analysis) with st.expander('Entitites'): entity_result=find_entities(text) stc.html(entity_result, height=100, scrolling=True) col1,col2=st.columns(2) with col1: with st.expander("Word Stats"): st.info("Word Statistics") docx = nt.TextFrame(text) st.write(docx.word_stats()) with st.expander("Top keywords"): keywords=get_most_common_tokens(text) st.write(keywords) with st.expander('Tagged Keywords'): data= pos_tag(text) st.dataframe(data) visualize_tags=tag_visualize(data) stc.html(visualize_tags,scrolling=True) with st.expander("Sentiment"): sent_result=get_semantics(text) st.write(sent_result) with col2: with st.expander("Plot word freq"): try: fig, ax = plt.subplots() most_common_tokens = dict(token_analysis["Token"].value_counts()) sns.countplot(data=token_analysis[token_analysis["Token"].isin(most_common_tokens)], x="Token", ax=ax) ax.set_xlabel('PoS') ax.set_ylabel('Frequency') ax.tick_params(axis='x' , rotation=45) st.pyplot(fig) except: st.warning('Insufficient data') with st.expander("Plot part of speech"): try: fig, ax = plt.subplots() most_common_tokens = dict(token_analysis["Position"].value_counts()) sns.countplot(data=token_analysis[token_analysis["Position"].isin(most_common_tokens)], x="Position", ax=ax) ax.set_xlabel('PoS') ax.set_ylabel('Frequency') ax.tick_params(axis='x' , rotation=45) st.pyplot(fig) except: st.warning('Insufficient data') with st.expander("Plot word cloud"): try: plot_wordcloud(text) except: st.warning('Insufficient data') with st.expander('Download Results'): file_download(token_analysis) elif choice == 'Upload_Files': text_file = st.file_uploader('Upload Files', type=['docx']) if text_file is not None: if text_file.type == 'text/plain': text = str(text_file.read(), "utf-8") else: text = docx2txt.process(text_file) if (st.button("Analyze")): with st.expander('Original Text'): st.write(text) with st.expander('Text Analysis'): token_analysis = nlp_analysis(text) st.dataframe(token_analysis) with st.expander('Entities'): entity_result = find_entities(text) stc.html(entity_result, height=100, scrolling=True) col1, col2 = st.columns(2) with col1: with st.expander("Word Stats"): st.info("Word Statistics") docx = nt.TextFrame(text) st.write(docx.word_stats()) with st.expander("Top keywords"): keywords = get_most_common_tokens(text) st.write(keywords) with st.expander("Sentiment"): sent_result = get_semantics(text) st.write(sent_result) with col2: with st.expander("Plot word freq"): fig, ax = plt.subplots() num_tokens = 10 # Adjust the number of tokens to display as desired most_common_tokens = dict(token_analysis["Token"].value_counts().head(num_tokens)) sns.countplot(data=token_analysis[token_analysis["Token"].isin(most_common_tokens)], x="Token", ax=ax) ax.set_xlabel('Token') ax.set_ylabel('Frequency') ax.tick_params(axis='x', rotation=45) st.pyplot(fig) with st.expander("Plot part of speech"): fig, ax = plt.subplots() most_common_tokens = dict(token_analysis["Position"].value_counts()) sns.countplot(data=token_analysis[token_analysis["Position"].isin(most_common_tokens)], x="Position", ax=ax) ax.set_xlabel('PoS') ax.set_ylabel('Frequency') ax.tick_params(axis='x', rotation=45) st.pyplot(fig) with st.expander("Plot word cloud"): plot_wordcloud(text) with st.expander('Download Results'): file_download(token_analysis)