Update my_model/tabs/results.py

my_model/tabs/results.py

@@ -49,14 +49,16 @@ class ResultDemonstrator(KBVQAEvaluator):
         st.pyplot(fig)
 
 ############
-
+        
         # Load data from Excel
         d = pd.read_excel('my_model/results/evaluation_results.xlsx', sheet_name="Main Data")
+  
+        token_counts = d['trimmed_tokens_count_caption_detic']
         
         # Assume 'accuracies' and 'token_counts' need to be computed or are columns in the DataFrame
         # Compute colors and labels for the plot (assuming these columns are already in the DataFrame)
-        d['color'] = d['accuracy'].apply(lambda x: 'green' if x == 1 else 'orange' if round(x, 2) == 0.67 else 'red')
-        d['label'] = d['accuracy'].apply(lambda x: 'Correct' if x == 1 else 'Partially Correct' if x == 0.67 else 'Incorrect')
+        d['color'] = d['vqa_score_13b_caption+detic'].apply(lambda x: 'green' if x == 1 else 'orange' if round(x, 2) == 0.67 else 'red')
+        d['label'] = d['vqa_score_13b_caption+detic'].apply(lambda x: 'Correct' if x == 1 else 'Partially Correct' if x == 0.67 else 'Incorrect')
         
         # Creating the scatter plot
         scatter_chart = alt.Chart(d).mark_circle(size=20).encode(
@@ -72,11 +74,9 @@ class ResultDemonstrator(KBVQAEvaluator):
 
 ####################
         scores = d['vqa_score_13b_caption+detic']
-        token_counts = d['trimmed_tokens_count_caption_detic']
 
         # Define colors and labels for the legend
-        colors = ['green' if score == 1 else 'orange' if round(score,2) == 0.67 else 'red' for score in scores]
-        labels = ['Correct' if score == 1 else 'Partially Correct' if score == 0.67 else 'Incorrect' for score in scores]
+
         plt.figure(figsize=(10, 6))
         # Create a scatter plot with smaller dots using the 's' parameter
         scatter = plt.scatter(range(len(token_counts)), token_counts, c=colors, s=20, label=labels)