""" # MANIFESTO ANALYSIS """ ##IMPORTING LIBRARIES import random import matplotlib.pyplot as plt import nltk from nltk.tokenize import word_tokenize,sent_tokenize from nltk.corpus import stopwords from nltk.stem.porter import PorterStemmer from nltk.stem import WordNetLemmatizer from nltk.corpus import stopwords from nltk.tokenize import word_tokenize from nltk.probability import FreqDist from cleantext import clean import textract import urllib.request import nltk.corpus from nltk.text import Text import io from io import StringIO,BytesIO import sys import pandas as pd import cv2 import re from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator from textblob import TextBlob from PIL import Image import os import gradio as gr from zipfile import ZipFile import contractions import unidecode nltk.download('stopwords') nltk.download('punkt') nltk.download('wordnet') nltk.download('words') """## PARSING FILES""" #def Parsing(parsed_text): #parsed_text=parsed_text.name #raw_party =parser.from_file(parsed_text) # raw_party = raw_party['content'],cache_examples=True # return clean(raw_party) def Parsing(parsed_text): parsed_text=parsed_text.name raw_party =textract.process(parsed_text, encoding='ascii',method='pdfminer') return clean(raw_party) #Added more stopwords to avoid irrelevant terms stop_words = set(stopwords.words('english')) stop_words.update('ask','much','thank','etc.', 'e', 'We', 'In', 'ed','pa', 'This','also', 'A', 'fu','To','5','ing', 'er', '2') """## PREPROCESSING""" def clean_text(text): ''' The function which returns clean text ''' text = text.encode("ascii", errors="ignore").decode("ascii") # remove non-asciicharacters text=unidecode.unidecode(text)# diacritics remove text=contractions.fix(text) # contraction fix text = re.sub(r"\n", " ", text) text = re.sub(r"\n\n", " ", text) text = re.sub(r"\t", " ", text) text = re.sub(r"/ ", " ", text) text = text.strip(" ") text = re.sub(" +", " ", text).strip() # get rid of multiple spaces and replace with a single text = [word for word in text.split() if word not in stop_words] text = ' '.join(text) return text # text_Party=clean_text(raw_party) def Preprocess(textParty): ''' Removing special characters extra spaces ''' text1Party = re.sub('[^A-Za-z0-9]+', ' ', textParty) #Removing all stop words pattern = re.compile(r'\b(' + r'|'.join(stopwords.words('english')) + r')\b\s*') text2Party = pattern.sub('', text1Party) # fdist_cong = FreqDist(word_tokens_cong) return text2Party ''' Using Concordance, you can see each time a word is used, along with its immediate context. It can give you a peek into how a word is being used at the sentence level and what words are used with it ''' def conc(text_Party,strng): word_tokens_party = word_tokenize(text_Party) moby = Text(word_tokens_party) resultList = [] for i in range(0,1): save_stdout = sys.stdout result = StringIO() sys.stdout = result moby.concordance(strng,lines=4,width=82) sys.stdout = save_stdout s=result.getvalue().splitlines() return result.getvalue() def get_all_phases_containing_tar_wrd(target_word, tar_passage, left_margin = 10, right_margin = 10,numLins=4): """ Function to get all the phases that contain the target word in a text/passage tar_passage. Workaround to save the output given by nltk Concordance function str target_word, str tar_passage int left_margin int right_margin --> list of str left_margin and right_margin allocate the number of words/pununciation before and after target word Left margin will take note of the beginning of the text """ ## Create list of tokens using nltk function tokens = nltk.word_tokenize(tar_passage) ## Create the text of tokens text = nltk.Text(tokens) ## Collect all the index or offset position of the target word c = nltk.ConcordanceIndex(text.tokens, key = lambda s: s.lower()) ## Collect the range of the words that is within the target word by using text.tokens[start;end]. ## The map function is use so that when the offset position - the target range < 0, it will be default to zero concordance_txt = ([text.tokens[list(map(lambda x: x-5 if (x-left_margin)>0 else 0,[offset]))[0]:offset+right_margin] for offset in c.offsets(target_word)]) ## join the sentences for each of the target phrase and return it result = [''.join([x.replace("Y","")+' ' for x in con_sub]) for con_sub in concordance_txt][:-1] result=result[:numLins+1] res='\n\n'.join(result) return res def normalize(d, target=1.0): raw = sum(d.values()) factor = target/raw return {key:value*factor for key,value in d.items()} def fDistance(text2Party): ''' Most frequent words search ''' word_tokens_party = word_tokenize(text2Party) #Tokenizing fdistance = FreqDist(word_tokens_party).most_common(10) mem={} for x in fdistance: mem[x[0]]=x[1] return normalize(mem) def fDistancePlot(text2Party,plotN=15): ''' Most Frequent Words Visualization ''' word_tokens_party = word_tokenize(text2Party) #Tokenizing fdistance = FreqDist(word_tokens_party) plt.title('Frequency Distribution') plt.axis('off') plt.figure(figsize=(4,3)) fdistance.plot(plotN) plt.tight_layout() buf = BytesIO() plt.savefig(buf) buf.seek(0) img1 = Image.open(buf) plt.clf() return img1 def DispersionPlot(textParty): ''' Dispersion PLot ''' word_tokens_party = word_tokenize(textParty) #Tokenizing moby = Text(word_tokens_party) fdistance = FreqDist(word_tokens_party) word_Lst=[] for x in range(5): word_Lst.append(fdistance.most_common(6)[x][0]) plt.axis('off') plt.figure(figsize=(4,3)) plt.title('Dispersion Plot') moby.dispersion_plot(word_Lst) plt.plot(color="#EF6D6D") plt.tight_layout() buf = BytesIO() plt.savefig(buf) buf.seek(0) img = Image.open(buf) plt.clf() return img def getSubjectivity(text): ''' Create a function to get the polarity ''' return TextBlob(text).sentiment.subjectivity def getPolarity(text): ''' Create a function to get the polarity ''' return TextBlob(text).sentiment.polarity def getAnalysis(score): if score < 0: return 'Negative' elif score == 0: return 'Neutral' else: return 'Positive' def Original_Image(path): img= cv2.imread(path) img= cv2.cvtColor(img, cv2.COLOR_BGR2RGB) return img def Image_Processed(path): ''' Reading the image file ''' img= cv2.imread(path) img= cv2.cvtColor(img, cv2.COLOR_BGR2RGB) #Thresholding ret, bw_img = cv2.threshold(img, 124, 255, cv2.THRESH_BINARY) return bw_img def word_cloud(orgIm,mask_img,text_Party_pr,maxWord=2000,colorGener=True, contCol='white',bckColor='white'): ''' #Generating word cloud ''' mask =mask_img # Create and generate a word cloud image: wordcloud = WordCloud(max_words=maxWord, background_color=bckColor, mask=mask, colormap='nipy_spectral_r', contour_color=contCol, width=800, height=800, margin=2, contour_width=3).generate(text_Party_pr) # create coloring from image plt.axis("off") if colorGener==True: image_colors = ImageColorGenerator(orgIm) plt.imshow(wordcloud.recolor(color_func= image_colors),interpolation="bilinear") else: plt.imshow(wordcloud) def word_cloud_generator(parsed_text_name,text_Party): parsed=parsed_text_name.lower() if 'bjp' in parsed: orgImg=Original_Image('bjpImg2.jpeg') bwImg=Image_Processed('bjpImg2.jpeg') plt.figure(figsize=(6,5)) word_cloud(orgImg,bwImg,text_Party,maxWord=3000,colorGener=True, contCol='white', bckColor='black') plt.tight_layout() buf = BytesIO() plt.savefig(buf) buf.seek(0) img1 = Image.open(buf) plt.clf() return img1 elif 'congress' in parsed: orgImg=Original_Image('congress3.jpeg') bwImg=Image_Processed('congress3.jpeg') plt.figure(figsize=(5,4)) word_cloud(orgImg,bwImg,text_Party,maxWord=3000,colorGener=True) plt.tight_layout() buf = BytesIO() plt.savefig(buf) buf.seek(0) img2 = Image.open(buf) plt.clf() return img2 #congrsMain.jpg elif 'aap' in parsed: orgImg=Original_Image('aapMain2.jpg') bwImg=Image_Processed('aapMain2.jpg') plt.figure(figsize=(5,4)) word_cloud(orgImg,bwImg,text_Party,maxWord=3000,colorGener=False,contCol='black') plt.tight_layout() buf = BytesIO() plt.savefig(buf) buf.seek(0) img3 = Image.open(buf) plt.clf() return img3 else : wordcloud = WordCloud(max_words=2000, background_color="white",mode="RGB").generate(text_Party) plt.figure(figsize=(5,5)) plt.imshow(wordcloud, interpolation="bilinear") plt.axis("off") plt.tight_layout() buf = BytesIO() plt.savefig(buf) buf.seek(0) img4 = Image.open(buf) plt.clf() return img4 ''' url = "http://library.bjp.org/jspui/bitstream/123456789/2988/1/BJP-Election-english-2019.pdf" path_input = "./Bjp_Manifesto_2019.pdf" urllib.request.urlretrieve(url, filename=path_input) url="https://drive.google.com/uc?id=1BLCiy_BWilfVdrUH8kbO-44DJevwO5CG&export=download" path_input = "./Aap_Manifesto_2019.pdf" urllib.request.urlretrieve(url, filename=path_input) url="https://drive.google.com/uc?id=1HVZvTtYntl0YKLnE0cwu0CvAIRhXOv60&export=download" path_input = "./Congress_Manifesto_2019.pdf" urllib.request.urlretrieve(url, filename=path_input) ''' def analysis(Manifesto,Search): plt.close('all') raw_party = Parsing(Manifesto) text_Party=clean_text(raw_party) text_Party= Preprocess(text_Party) df = pd.DataFrame(raw_party.split('\n'), columns=['Content']) df['Subjectivity'] = df['Content'].apply(getSubjectivity) df['Polarity'] = df['Content'].apply(getPolarity) df['Analysis on Polarity'] = df['Polarity'].apply(getAnalysis) df['Analysis on Subjectivity'] = df['Subjectivity'].apply(getAnalysis) plt.title('Sentiment Analysis') plt.xlabel('Sentiment') plt.ylabel('Counts') plt.figure(figsize=(4,3)) df['Analysis on Polarity'].value_counts().plot(kind ='bar',color="#FF9F45") plt.tight_layout() buf = BytesIO() plt.savefig(buf) buf.seek(0) img1 = Image.open(buf) plt.clf() plt.figure(figsize=(4,3)) df['Analysis on Subjectivity'].value_counts().plot(kind ='bar',color="#B667F1") plt.tight_layout() buf = BytesIO() plt.savefig(buf) buf.seek(0) img2 = Image.open(buf) plt.clf() plt.close() img3 = word_cloud_generator(Manifesto.name,text_Party) fdist_Party=fDistance(text_Party) img4=fDistancePlot(text_Party) img5=DispersionPlot(text_Party) #concordance(text_Party,Search) searChRes=get_all_phases_containing_tar_wrd(Search,text_Party) searChRes=searChRes.replace(Search,"\u0332".join(Search)) return searChRes,fdist_Party,img1,img2,img3,img4,img5 Search_txt=gr.inputs.Textbox() filePdf = gr.inputs.File() text = gr.outputs.Textbox(label='Context Based Search') mfw=gr.outputs.Label(label="Most Relevant Topics") plot1=gr.outputs. Image(label='Sentiment Analysis') plot2=gr.outputs.Image(label='Subjectivity Analysis') plot3=gr.outputs.Image(label='Word Cloud') plot4=gr.outputs.Image(label='Frequency Distribution') plot5=gr.outputs.Image(label='Dispersion Plot') io=gr.Interface(fn=analysis, inputs=[filePdf,Search_txt], outputs=[text,mfw,plot1,plot2,plot3,plot4,plot5], title='Manifesto Analysis',examples=[['Example/AAP_Manifesto_2019.pdf','government'],['Example/Bjp_Manifesto_2019.pdf','environment'],['Example/Congress_Manifesto_2019.pdf','safety']],theme='peach') io.launch(debug=True,share=False,enable_queue=True) #allow_screenshot=False,allow_flagging="never", #examples=[['manifestos/Bjp_Manifesto_2019.pdf','modi'],['AAP_Manifesto_2019.pdf','delhi'],['manifestos/Congress_Manifesto_2019.pdf','safety']])