Update app.py

app.py

@@ -16,7 +16,7 @@ df['Scenario'] = df['Scenario'].astype(str)
 def estimate_pass_at_k(num_samples, num_correct, k):
     def estimator(n, c, k):
         if n - c < k:
-            return 1.0
+            return 'N/A'
         return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1, n + 1))
 
     return np.array([estimator(n, c, k) for n, c in zip(num_samples, num_correct)])
@@ -65,9 +65,9 @@ duo_complete_pass_at_k = duo_df.groupby('Model')[['Runs', 'Successes']].apply(la
 }, index=['pass@1'])).reset_index()
 
 complete_pass_at_k = df.groupby('Model')[['Runs', 'Successes']].apply(lambda x: pd.Series({
-    'pass@1': estimate_pass_at_k(x['Runs'].values, x['Successes'].values, 1).mean()
-    #'pass@5': estimate_pass_at_k(x['Runs'].values, x['Successes'].values, 5).mean(),
-    #'pass@10': estimate_pass_at_k(x['Runs'].values, x['Successes'].values, 10).mean()
+    'pass@1': estimate_pass_at_k(x['Runs'].values, x['Successes'].values, 1).mean(),
+    'pass@5': estimate_pass_at_k(x['Runs'].values, x['Successes'].values, 5).mean(),
+    'pass@10': estimate_pass_at_k(x['Runs'].values, x['Successes'].values, 10).mean()
 }, index=['pass@1', 'pass@5', 'pass@10'])).reset_index()
     
 with gr.Blocks() as demo: