Enhance Plagiarism Checker with Heatmap Visualization

app.py

@@ -1,21 +1,25 @@
 import os
+import matplotlib.pyplot as plt
+import seaborn as sns
 from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.metrics.pairwise import cosine_similarity
 
+# Load the student files
 student_files = [doc for doc in os.listdir() if doc.endswith('.txt')]
-student_notes = [open(_file, encoding='utf-8').read()
-                 for _file in student_files]
+student_notes = [open(_file, encoding='utf-8').read() for _file in student_files]
 
+# Vectorize the text
+def vectorize(Text): 
+    return TfidfVectorizer().fit_transform(Text).toarray()
 
-def vectorize(Text): return TfidfVectorizer().fit_transform(Text).toarray()
-def similarity(doc1, doc2): return cosine_similarity([doc1, doc2])
-
+def similarity(doc1, doc2): 
+    return cosine_similarity([doc1, doc2])
 
 vectors = vectorize(student_notes)
 s_vectors = list(zip(student_files, vectors))
 plagiarism_results = set()
 
-
+# Check plagiarism
 def check_plagiarism():
     global s_vectors
     for student_a, text_vector_a in s_vectors:
@@ -29,6 +33,27 @@ def check_plagiarism():
             plagiarism_results.add(score)
     return plagiarism_results
 
+# Visualize plagiarism results
+def visualize_results(plagiarism_results):
+    # Create a matrix for the heatmap
+    files = sorted(list(set([pair[0] for pair in plagiarism_results] + [pair[1] for pair in plagiarism_results])))
+    matrix = [[0 for _ in files] for _ in files]
+
+    file_index = {file: idx for idx, file in enumerate(files)}
+
+    for file_a, file_b, score in plagiarism_results:
+        i, j = file_index[file_a], file_index[file_b]
+        matrix[i][j] = score
+        matrix[j][i] = score
+
+    # Plot the heatmap
+    plt.figure(figsize=(10, 8))
+    sns.heatmap(matrix, xticklabels=files, yticklabels=files, cmap='coolwarm', annot=True)
+    plt.title('Plagiarism Heatmap')
+    plt.show()
+
+# Check for plagiarism and visualize the results
+plagiarism_results = check_plagiarism()
+visualize_results(plagiarism_results)
+
 
-for data in check_plagiarism():
-    print(data)

requirements.txt

@@ -1 +1,4 @@
-scikit_learn==0.24.2
+scikit-learn==0.24.2
+matplotlib==3.4.2
+seaborn==0.11.1
+