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Commit 9d278ac

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Merge pull request avinashkranjan#707 from Anupreetadas/branch_name2
Plagiarism-Checker
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‎Plagiarism-Checker/README.md‎

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<h3 align="center">Plagiarism Checker</h3>
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## How it works <br><br>
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- In order to compute the simlilarity between two text documents, the textual raw data is transformed into vectors.
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- Then it is transformed into arrays of numbers and then from that by using a basic knowledge vector to compute the the similarity between them.
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## Dependencies<br>
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- Install scikit-learn by:
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$ pip install scikit-learn
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## Running the app <br>
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- There are four text documents in the repository.
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- Basically the code will compare all the .txt files and check for any similarity.
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$ python plagiarism.py
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## Screenshots
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<img src="https://raw.githubusercontent.com/Anupreetadas/plagiarism_checker/main/assets/Capture1.PNG" width="100%" height="100%" align="left" >
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‎Plagiarism-Checker/file1.txt‎

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The present moment is filled with happiness and joy.
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If you are attentive enough you will see it.

‎Plagiarism-Checker/file2.txt‎

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Life is in the joy of achievement and thrill of creative effort.
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If you are attentive enough you will see it.

‎Plagiarism-Checker/file3.txt‎

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Life is in joy of achievement.Do hark work and live up to your goals.

‎Plagiarism-Checker/file4.txt‎

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Welcome to new brave world!!

‎Plagiarism-Checker/plagiarism.py‎

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#OS Module for loading paths of textfiles. TfidfVectorizer to perform word embedding on the textual data and cosine similarity to compute the plagiarism.
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import os
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from sklearn.feature_extraction.text import TfidfVectorizer
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from sklearn.metrics.pairwise import cosine_similarity
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student_files = [doc for doc in os.listdir() if doc.endswith('.txt')]
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student_notes =[open(File).read() for File in student_files]
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#Two lambda functions, one to convert the text to arrays of numbers and the other one to compute the similarity between them.
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vectorize = lambda Text: TfidfVectorizer().fit_transform(Text).toarray()
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similarity=lambda doc1,doc2:cosine_similarity([doc1,doc2])
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#Vectorize the Textual Data
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vectors = vectorize(student_notes)
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s_vectors=list(zip(student_files,vectors))
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#computing the similarity among students
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def check_plagiarism():
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plagiarism_results = set()
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global s_vectors
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for student_a, text_vector_a in s_vectors:
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new_vectors =s_vectors.copy()
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current_index = new_vectors.index((student_a, text_vector_a))
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del new_vectors[current_index]
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for student_b , text_vector_b in new_vectors:
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sim_score = similarity(text_vector_a, text_vector_b)[0][1]
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student_pair = sorted((student_a, student_b))
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score = (student_pair[0], student_pair[1],sim_score)
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plagiarism_results.add(score)
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return plagiarism_results
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for data in check_plagiarism():
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print(data)

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