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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) | |
| tokens_p = word_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='Text 1'), gr.inputs.Textbox(lines=10, label='Text 2')], outputs=gr.outputs.Textbox(label='Plagiarism Level'), title="Plagiarism Checker", theme='dark-peach') | |
| plagiarismUI.launch(inbrowser=False) |