from sentence_transformers import SentenceTransformer, util

# Load pre-trained model once
model = SentenceTransformer('all-MiniLM-L6-v2')

def check_plagiarism(text1, text2, threshold=0.8):
    """
    Compare two texts and return similarity score and verdict.
    
    Parameters:
        text1 (str): First text (e.g., student answer)
        text2 (str): Second text (e.g., source material)
        threshold (float): Similarity threshold (default = 0.8)
    
    Returns:
        result (str): Similarity percentage and plagiarism verdict.
    """
    try:
        embedding1 = model.encode(text1, convert_to_tensor=True)
        embedding2 = model.encode(text2, convert_to_tensor=True)

        similarity_score = util.cos_sim(embedding1, embedding2).item()
        similarity_percent = round(similarity_score * 100, 2)

        if similarity_score >= threshold:
            verdict = "⚠️ Potential Plagiarism"
        else:
            verdict = "✅ No Significant Plagiarism Detected"

        return f"Similarity: {similarity_percent}%\nResult: {verdict}"
    except Exception as e:
        return f"Error during plagiarism check: {str(e)}"