Update test.py

test.py

@@ -1,6 +1,26 @@
 import pandas as pd
 import re
 
+data['match'] = data['Column_B'] == df_excel['Column_A']
+
+# Map the boolean result to 'Match' or 'No Match'
+data['match'] = data['match'].map({True: 'Match', False: 'No Match'})
+
+# Calculate performance metrics
+tp = data['match'].value_counts().get('Match', 0)
+fn = data['match'].value_counts().get('No Match', 0)
+tot = len(data)  # You can replace len(data) with len(df_excel) if both are the same
+
+accuracy = tp / tot
+precision = tp / (tp + 0)  # Assuming all true positives
+recall = tp / (tp + fn)
+f1_score = 2 * (precision * recall) / (precision + recall)
+
+print(f"Accuracy: {accuracy:.2%}")
+print(f"Precision: {precision:.2%}")
+print(f"Recall: {recall:.2%}")
+print(f"F1 Score: {f1_score:.2%}")
+
 You are a healthcare professional with specialized expertise in oncology and a comprehensive understanding of biomarker data from patient medical records. Your primary task is to extract and identify the table layouts present in the provided data, focusing solely on these layouts. Present your findings in a clear and structured table format. Ensure that only the relevant table layouts are included, without incorporating any additional findings or headers unrelated to the identified tables.