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from sentence_transformers import SentenceTransformer, util |
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model = SentenceTransformer('all-MiniLM-L6-v2') |
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def check_plagiarism(text1, text2, threshold=0.8): |
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""" |
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Compare two texts and return similarity score and verdict. |
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Parameters: |
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text1 (str): First text (e.g., student answer) |
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text2 (str): Second text (e.g., source material) |
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threshold (float): Similarity threshold (default = 0.8) |
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Returns: |
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result (str): Similarity percentage and plagiarism verdict. |
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""" |
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try: |
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embedding1 = model.encode(text1, convert_to_tensor=True) |
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embedding2 = model.encode(text2, convert_to_tensor=True) |
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similarity_score = util.cos_sim(embedding1, embedding2).item() |
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similarity_percent = round(similarity_score * 100, 2) |
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if similarity_score >= threshold: |
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verdict = "⚠️ Potential Plagiarism" |
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else: |
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verdict = "✅ No Significant Plagiarism Detected" |
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return f"Similarity: {similarity_percent}%\nResult: {verdict}" |
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except Exception as e: |
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return f"Error during plagiarism check: {str(e)}" |
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