Update app.py

app.py

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