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diff --git a/data/my_results/evaluate_gemini.py b/data/my_results/evaluate_gemini.py
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+++ b/data/my_results/evaluate_gemini.py
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+import pandas as pd
+import json
+
+# Define file paths for the CSV files
+file_path1 = 'data/my_results/gemini_2.0_flash_kg_rag_based_mcq_0.csv'
+
+
+# Load the CSV files into DataFrames
+df1 = pd.read_csv(file_path1)
+
+# Define a function to check if the correct answer is present in the LLM answer
+def contains_correct_answer(row):
+    try: 
+        return row['correct_answer'] == json.loads(row['llm_answer'].replace('```', '').replace('\n', '').replace('json', '').replace('{{', '{').replace('}}', '}').split('}')[0] + '}')['answer']
+    except:
+        return False
+
+# Apply the function to each row of the DataFrames
+df1['is_correct'] = df1.apply(contains_correct_answer, axis=1)
+
+# Calculate the percentage of correct answers
+correct_rate1 = df1['is_correct'].mean() * 100
+print(f"Correct Answer Rate for {file_path1}: {correct_rate1:.2f}%")
+