Update app.py

app.py

@@ -31,7 +31,7 @@ def generate_questions(category):
   )
   return json.loads(response.choices[0].message.content)
 
-# Function to calculate MetaCog score
+# Function to calculate Metaknowledge score
 def calculate_meta_cog_score(df):
     df['Correct'] = df['User Answer'] == df['Correct Answer']
     df['C'] = df['Correct'].apply(lambda x: 1 if x else -1)
@@ -71,7 +71,7 @@ def calculate_scores(df):
     underconfidence = df['Underconfidence'].sum() / n
 
     return {
-        'MetaCog Score': f"{round(meta_cog_ratio * 100, 0)}%",
+        'Metaknowledge Score': f"{round(meta_cog_ratio * 100, 0)}%",
         'Accuracy': f"{round(accuracy * 100, 0)}%",
         'Overconfidence': f"{round(overconfidence * 100, 0)}%",
         'Underconfidence': f"{round(underconfidence * 100, 0)}%"
@@ -95,7 +95,7 @@ def analyze_results(df, overall_scores, subcategory_scores):
                 "role": "user",
                 "content": f"""
                 Analyze the following quiz results:
-                - Overall MetaCog Score: {overall_scores['MetaCog Score']}
+                - Overall Metaknowledge Score: {overall_scores['Metaknowledge Score']}
                 - Overall Accuracy: {overall_scores['Accuracy']}
                 - Overall Overconfidence: {overall_scores['Overconfidence']}
                 - Overall Underconfidence: {overall_scores['Underconfidence']}
@@ -174,8 +174,8 @@ def submit_answer(category, questions, index, user_answer, confidence, user_answ
         subcategory_scores = df.groupby('Subcategory').apply(calculate_scores).to_dict()
         analysis = analyze_results(df, overall_scores, subcategory_scores)
 
-        overall_score_df = pd.DataFrame([["Overall", *overall_scores.values()]], columns=['Subcategory', 'Accuracy', 'MetaCog Score', 'Overconfidence', 'Underconfidence'])
-        subcategory_scores_df = pd.DataFrame([(subcategory, *score.values()) for subcategory, score in subcategory_scores.items()], columns=['Subcategory', 'MetaCog Score', 'Accuracy', 'Overconfidence', 'Underconfidence'])
+        overall_score_df = pd.DataFrame([["Overall", *overall_scores.values()]], columns=['Subcategory', 'Accuracy', 'Metaknowledge Score', 'Overconfidence', 'Underconfidence'])
+        subcategory_scores_df = pd.DataFrame([(subcategory, *score.values()) for subcategory, score in subcategory_scores.items()], columns=['Subcategory', 'Metaknowledge Score', 'Accuracy', 'Overconfidence', 'Underconfidence'])
         results_df = pd.concat([overall_score_df, subcategory_scores_df], ignore_index=True)
         results_df = gr.DataFrame(label="Results", value=results_df, visible=True)
         return "", index, gr.update(visible=False), user_answers, results_df, gr.update(visible=False), gr.update(visible=False), gr.update(value=analysis, visible=True)
@@ -185,7 +185,7 @@ def submit_answer(category, questions, index, user_answer, confidence, user_answ
 
 # Gradio UI setup
 with gr.Blocks(theme="soft") as app:
-    gr.Markdown("""## Metacognition Test
+    gr.Markdown("""## Metaknowledge Test
     See how well you know what you know. Enter a category and a 30-question quiz will be generated. Answer the True/False questions about it, and scores will then be calculated on your knowledge of the category, and on your ***knowledge of your knowledge*** of the category.""")
     category_input = gr.Textbox(label="Category", placeholder="general knowledge")
     submit_category = gr.Button("Generate Quiz")