# """ # # 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('punkt_tab') # 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): # 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() # 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)) # plt.close('all') # return searChRes,fdist_Party,img1,img2,img3,img4,img5 # Search_txt= "text" # filePdf = "file" # text = gr.Textbox(label='Context Based Search') # mfw=gr.Label(label="Most Relevant Topics") # plot1=gr.Image(label='Sentiment Analysis') # plot2=gr.Image(label='Subjectivity Analysis') # plot3=gr.Image(label='Word Cloud') # plot4=gr.Image(label='Frequency Distribution') # plot5=gr.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) # #allow_screenshot=False,allow_flagging="never", # #examples=[['manifestos/Bjp_Manifesto_2019.pdf','modi'],['AAP_Manifesto_2019.pdf','delhi'],['manifestos/Congress_Manifesto_2019.pdf','safety']]) """ # 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 import groq import json from dotenv import load_dotenv from sklearn.feature_extraction.text import TfidfVectorizer from collections import Counter # Load environment variables from .env file load_dotenv() nltk.download('punkt_tab') nltk.download('stopwords') nltk.download('punkt') nltk.download('wordnet') nltk.download('words') # Initialize Groq client for LLM capabilities try: groq_api_key = os.getenv("GROQ_API_KEY") if groq_api_key: groq_client = groq.Groq(api_key=groq_api_key) else: print("Warning: GROQ_API_KEY not found in environment variables. Summarization will be disabled.") groq_client = None except Exception as e: print(f"Error initializing Groq client: {e}") groq_client = None """## 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 generate_summary(text, max_length=1000): """ Generate a summary of the manifesto text using Groq LLM """ if not groq_client: return "Summarization is not available. Please set up your GROQ_API_KEY in the .env file." # Truncate text if it's too long to fit in context window if len(text) > 10000: text = text[:10000] try: # Use Groq's LLaMA 3 model for summarization completion = groq_client.chat.completions.create( model="llama3-8b-8192", # Using LLaMA 3 8B model messages=[ {"role": "system", "content": "You are a helpful assistant that summarizes political manifestos. Provide a concise, objective summary that captures the key policy proposals, themes, and promises in the manifesto."}, {"role": "user", "content": f"Please summarize the following political manifesto text in about 300-500 words, focusing on the main policy areas, promises, and themes:\n\n{text}"} ], temperature=0.3, # Lower temperature for more focused output max_tokens=800, # Limit response length ) return completion.choices[0].message.content except Exception as e: return f"Error generating summary: {str(e)}. Please check your API key and connection." def fDistance(text2Party): ''' Most frequent words search using TF-IDF to find more relevant words ''' # Traditional frequency distribution 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] # Enhanced with TF-IDF for better relevance sentences = sent_tokenize(text2Party) # Use TF-IDF to find more relevant words vectorizer = TfidfVectorizer(max_features=15, stop_words='english') tfidf_matrix = vectorizer.fit_transform(sentences) # Get feature names (words) feature_names = vectorizer.get_feature_names_out() # Calculate average TF-IDF score for each word across all sentences tfidf_scores = {} for i, word in enumerate(feature_names): scores = [tfidf_matrix[j, i] for j in range(len(sentences)) if i < tfidf_matrix[j].shape[1]] if scores: tfidf_scores[word] = sum(scores) / len(scores) # Sort by score and get top words sorted_tfidf = dict(sorted(tfidf_scores.items(), key=lambda x: x[1], reverse=True)[:10]) # Combine traditional frequency with TF-IDF for better results combined_scores = {} for word in set(list(mem.keys()) + list(sorted_tfidf.keys())): # Normalize and combine both scores (with more weight to TF-IDF) freq_score = mem.get(word, 0) / max(mem.values()) if mem else 0 tfidf_score = sorted_tfidf.get(word, 0) / max(sorted_tfidf.values()) if sorted_tfidf else 0 combined_scores[word] = (freq_score * 0.3) + (tfidf_score * 0.7) # Weight TF-IDF higher # Get top 10 words by combined score top_words = dict(sorted(combined_scores.items(), key=lambda x: x[1], reverse=True)[:10]) return normalize(top_words) 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): ''' Main analysis function that processes the manifesto and generates all outputs Manifesto: PDF file uploaded by the user Search: Search term entered by the user ''' try: # Process the uploaded PDF raw_party = Parsing(Manifesto) text_Party = clean_text(raw_party) text_Party_processed = Preprocess(text_Party) # Generate summary using LLM summary = generate_summary(raw_party) # Sentiment analysis 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) # Generate sentiment analysis plot 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() # Generate subjectivity analysis plot 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() # Generate word cloud img3 = word_cloud_generator(Manifesto.name, text_Party_processed) # Generate frequency distribution and dispersion plots fdist_Party = fDistance(text_Party_processed) img4 = fDistancePlot(text_Party_processed) img5 = DispersionPlot(text_Party_processed) # Search for the term in the text searChRes = get_all_phases_containing_tar_wrd(Search, text_Party_processed) searChRes = searChRes.replace(Search, "\u0332".join(Search)) plt.close('all') return searChRes, fdist_Party, img1, img2, img3, img4, img5, summary except Exception as e: error_message = f"Error analyzing manifesto: {str(e)}" print(error_message) # Return placeholder values in case of error return error_message, {}, None, None, None, None, None, "Error generating summary. Please check the console for details." Search_txt= "text" filePdf = "file" text = gr.Textbox(label='Context Based Search') mfw=gr.Label(label="Most Relevant Topics (LLM Enhanced)") plot1=gr.Image(label='Sentiment Analysis') plot2=gr.Image(label='Subjectivity Analysis') plot3=gr.Image(label='Word Cloud') plot4=gr.Image(label='Frequency Distribution') plot5=gr.Image(label='Dispersion Plot') summary_output = gr.Textbox(label='AI-Generated Summary', lines=10) with gr.Blocks(title='Manifesto Analysis') as demo: gr.Markdown("# Manifesto Analysis with LLM Enhancement") gr.Markdown("### Analyze political manifestos with advanced NLP and LLM techniques") with gr.Row(): with gr.Column(scale=1): file_input = gr.File(label="Upload Manifesto PDF", file_types=[".pdf"]) search_input = gr.Textbox(label="Search Term", placeholder="Enter a term to search in the manifesto") submit_btn = gr.Button("Analyze Manifesto") with gr.Tabs(): with gr.TabItem("Summary"): summary_output with gr.TabItem("Search Results"): text with gr.TabItem("Key Topics"): mfw with gr.TabItem("Visualizations"): with gr.Row(): with gr.Column(scale=1): plot3 with gr.Column(scale=1): plot4 with gr.Row(): with gr.Column(scale=1): plot1 with gr.Column(scale=1): plot2 with gr.Row(): plot5 submit_btn.click( fn=analysis, inputs=[file_input, search_input], outputs=[text, mfw, plot1, plot2, plot3, plot4, plot5, summary_output] ) gr.Examples( examples=[ ['Example/AAP_Manifesto_2019.pdf', 'government'], ['Example/Bjp_Manifesto_2019.pdf', 'environment'], ['Example/Congress_Manifesto_2019.pdf', 'safety'] ], inputs=[file_input, search_input] ) demo.launch(debug=True, share=False) # Old interface code replaced by the Blocks implementation above # 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']]) # io.launch(debug=True,share=False)