Update my_model/tabs/results.py

my_model/tabs/results.py

@@ -47,19 +47,48 @@ class ResultDemonstrator(KBVQAEvaluator):
         # Display the plot in Streamlit
         st.pyplot(fig)
 
-        import plotly.express as px
-      
+############
+
+        # Load data from Excel
+        d = pd.read_excel('my_model/results/evaluation_results.xlsx', sheet_name="Main Data")
         
-        # Data
-        df = pd.DataFrame({
-            "x": range(10),
-            "y": [2, 1, 4, 3, 5, 6, 9, 7, 10, 8],
-            "color": ["red"] * 5 + ["blue"] * 5
-        })
+        # 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')
         
-        # Create an interactive scatter plot
-        fig = px.scatter(df, x='x', y='y', color='color')
+        # Creating the scatter plot
+        scatter_chart = alt.Chart(d).mark_circle(size=20).encode(
+            x=alt.X('index:Q', title='Index'),  # Assuming 'index' is a column or can be created
+            y=alt.Y('token_counts:Q', title='Number of Tokens'),
+            color=alt.Color('color:N', legend=alt.Legend(title="VQA Score")),
+            tooltip=['index', 'token_counts', 'label']
+        ).interactive()
         
-        # Display the plot in Streamlit
-        st.plotly_chart(fig)
-            
+        # Display the chart
+        st.altair_chart(scatter_chart, use_container_width=True)
+
+
+####################
+
+        # Define colors and labels for the legend
+        colors = ['green' if accuracy == 1 else 'orange' if round(accuracy,2) == 0.67 else 'red' for accuracy in accuracies]
+        labels = ['Correct' if accuracy == 1 else 'Partially Correct' if accuracy == 0.67 else 'Incorrect' for accuracy in accuracies]
+        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)
+        
+        # Create a custom legend
+        from matplotlib.lines import Line2D
+        legend_elements = [Line2D([0], [0], marker='o', color='w', label='Full VQA Score', markerfacecolor='green', markersize=10),
+                           Line2D([0], [0], marker='o', color='w', label='Partial VQA Score', markerfacecolor='orange', markersize=10),
+                           Line2D([0], [0], marker='o', color='w', label='Zero VQA Score', markerfacecolor='red', markersize=10)]
+        #plt.legend(handles=legend_elements, loc='upper right')
+        plt.legend(handles=legend_elements, loc='best', bbox_to_anchor=(1, 1))
+        # Set the title and labels
+        plt.title('Token Counts VS VQA Score')
+        plt.xlabel('Index')
+        plt.ylabel('Number of Tokens')
+        
+        # Display the plot
+        plt.show()