Sa-m commited on
Commit
bc86dbe
1 Parent(s): d332967

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +21 -24
app.py CHANGED
@@ -153,8 +153,13 @@ def fDistancePlot(text2Party,plotN=30):
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  fdistance = FreqDist(word_tokens_party)
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  plt.figure(figsize=(4,6))
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  fdistance.plot(plotN)
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- plt.savefig('distplot.png')
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- plt.clf()
 
 
 
 
 
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@@ -200,7 +205,7 @@ def analysis(Manifesto,Search):
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  plt.title('Sentiment Analysis')
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  plt.xlabel('Sentiment')
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  plt.ylabel('Counts')
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- plt.figure(figsize=(4,6))
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  df['Analysis on Polarity'].value_counts().plot(kind ='bar')
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  #plt.savefig('./sentimentAnalysis.png')
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  #plt.clf()
@@ -211,7 +216,7 @@ def analysis(Manifesto,Search):
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  img1 = Image.open(buf)
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  plt.clf()
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- plt.figure(figsize=(4,6))
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  df['Analysis on Subjectivity'].value_counts().plot(kind ='bar')
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  #plt.savefig('sentimentAnalysis2.png')
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  #plt.clf()
@@ -225,17 +230,21 @@ def analysis(Manifesto,Search):
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  wordcloud = WordCloud(max_words=2000, background_color="white",mode="RGB").generate(text_Party)
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  plt.figure(figsize=(4,3))
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  plt.imshow(wordcloud, interpolation="bilinear")
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- plt.axis("off")
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- plt.savefig('wordcloud.png')
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- plt.clf()
 
 
 
 
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  fdist_Party=fDistance(text_Party)
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- fDistancePlot(text_Party)
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  #img1=cv2.imread('/sentimentAnalysis.png')
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  #img2=cv2.imread('/wordcloud.png')
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- img3=cv2.imread('/wordcloud.png')
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- img4=cv2.imread('/distplot.png')
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  searchRes=concordance(text_Party,Search)
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  searChRes=clean(searchRes)
@@ -249,24 +258,12 @@ text = gr.outputs.Textbox(label='SEARCHED OUTPUT')
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  mfw=gr.outputs.Label(label="Most Relevant Topics")
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  # mfw2=gr.outputs.Image(label="Most Relevant Topics Plot")
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  plot1=gr.outputs. Image(label='Sentiment Analysis')
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- plot2=gr.outputs.Image(label='Word Cloud')
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- plot3=gr.outputs.Image(label='Subjectivity')
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  plot4=gr.outputs.Image(label='Frequency Distribution')
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  io=gr.Interface(fn=analysis, inputs=[filePdf,Search_txt], outputs=[text,mfw,plot1,plot2,plot3,plot4], title='Manifesto Analysis',examples=[['./Bjp_Manifesto_2019.pdf','india'],['./Aap_Manifesto_2019.pdf',],['./Congress_Manifesto_2019.pdf',]])
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  io.launch(debug=False,share=True)
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- #examples=[['/Bjp_Manifesto_2019.pdf',],['/Aap_Manifesto_2019.pdf',]],
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  fdistance = FreqDist(word_tokens_party)
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  plt.figure(figsize=(4,6))
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  fdistance.plot(plotN)
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+ plt.tight_layout()
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+ buf = BytesIO()
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+ plt.savefig(buf)
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+ buf.seek(0)
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+ img1 = Image.open(buf)
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+ plt.clf()
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+ return img1
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  plt.title('Sentiment Analysis')
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  plt.xlabel('Sentiment')
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  plt.ylabel('Counts')
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+ plt.figure(figsize=(4,3))
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  df['Analysis on Polarity'].value_counts().plot(kind ='bar')
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  #plt.savefig('./sentimentAnalysis.png')
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  #plt.clf()
 
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  img1 = Image.open(buf)
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  plt.clf()
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+ plt.figure(figsize=(4,3))
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  df['Analysis on Subjectivity'].value_counts().plot(kind ='bar')
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  #plt.savefig('sentimentAnalysis2.png')
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  #plt.clf()
 
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  wordcloud = WordCloud(max_words=2000, background_color="white",mode="RGB").generate(text_Party)
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  plt.figure(figsize=(4,3))
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  plt.imshow(wordcloud, interpolation="bilinear")
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+ plt.axis("off")
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+ plt.tight_layout()
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+ buf = BytesIO()
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+ plt.savefig(buf)
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+ buf.seek(0)
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+ img3 = Image.open(buf)
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+ plt.clf()
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  fdist_Party=fDistance(text_Party)
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+ img4=fDistancePlot(text_Party)
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  #img1=cv2.imread('/sentimentAnalysis.png')
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  #img2=cv2.imread('/wordcloud.png')
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+ #img3=cv2.imread('/wordcloud.png')
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+ #img4=cv2.imread('/distplot.png')
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  searchRes=concordance(text_Party,Search)
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  searChRes=clean(searchRes)
 
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  mfw=gr.outputs.Label(label="Most Relevant Topics")
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  # mfw2=gr.outputs.Image(label="Most Relevant Topics Plot")
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  plot1=gr.outputs. Image(label='Sentiment Analysis')
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+ plot2=gr.outputs.Image(label='Subjectivity Analysis')
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+ plot3=gr.outputs.Image(label='Word Cloud')
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  plot4=gr.outputs.Image(label='Frequency Distribution')
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  io=gr.Interface(fn=analysis, inputs=[filePdf,Search_txt], outputs=[text,mfw,plot1,plot2,plot3,plot4], title='Manifesto Analysis',examples=[['./Bjp_Manifesto_2019.pdf','india'],['./Aap_Manifesto_2019.pdf',],['./Congress_Manifesto_2019.pdf',]])
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  io.launch(debug=False,share=True)
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