Create app.py

app.py

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+import nltk
+from nltk.tokenize import word_tokenize, sent_tokenize
+from nltk.corpus import stopwords
+nltk.download('punkt')
+nltk.download('stopwords')
+
+import gradio as gr
+
+#longest common subsequence
+#dynamic programming algorithm for finding lcs
+def lcs(l1,l2):
+  s1=word_tokenize(l1)
+  s2=word_tokenize(l2)
+  # storing the dp values 
+  dp = [[None]*(len(s1)+1) for i in range(len(s2)+1)] 
+  
+  for i in range(len(s2)+1): 
+      for j in range(len(s1)+1): 
+          if i == 0 or j == 0: 
+              dp[i][j] = 0
+          elif s2[i-1] == s1[j-1]: 
+              dp[i][j] = dp[i-1][j-1]+1
+          else: 
+              dp[i][j] = max(dp[i-1][j] , dp[i][j-1]) 
+  return dp[len(s2)][len(s1)]
+    
+ def plagiarismChecker(orig, plag):
+  sent_o=sent_tokenize(orig)
+  sent_p=sent_tokenize(plag)
+  
+  #maximum length of LCS for a sentence in suspicious text
+  max_lcs=0
+  sum_lcs=0
+  
+  for i in sent_p:
+      for j in sent_o:
+          l=lcs(i,j)
+          max_lcs=max(max_lcs,l)
+      sum_lcs+=max_lcs
+      max_lcs=0
+  
+  score=sum_lcs/len(tokens_p)
+  return score*100
+  
+plagiarismUI = gr.Interface(fn=plagiarismChecker, inputs=[gr.inputs.Textbox(lines=10, label='Original'), gr.inputs.Textbox(lines=10, label='Plagiarised')], outputs=gr.outputs.Textbox(label='Plagiarism Level'), title="Plagiarism Checker", theme='dark-peach')
+plagiarismUI.launch(inbrowser=False)