add more details in results page

app.py

@@ -183,19 +183,18 @@ def render_results():
     )
     st.markdown("##  User Performance Breakdown")
 
-    categories = set(session_state.is_classifier_correct.values())
+    categories = [
+        "Correct",
+        "Wrong",
+    ]  # set(session_state.is_classifier_correct.values())
     breakdown_stats_correct = {c: 0 for c in categories}
     breakdown_stats_wrong = {c: 0 for c in categories}
 
     experiment_summary = []
 
     for q in session_state.user_feedback.keys():
-        category = session_state.is_classifier_correct[q]
-        user_feedback_boolean = (
-            True if session_state.user_feedback[q] == "Correct" else False
-        )
-
-        is_user_correct = category == user_feedback_boolean
+        category = "Correct" if session_state.is_classifier_correct[q] else "Wrong"
+        is_user_correct = category == session_state.user_feedback[q]
 
         if is_user_correct:
             breakdown_stats_correct[category] += 1
@@ -214,7 +213,7 @@ def render_results():
                 is_user_correct,
             ]
         )
-
+    ################################### Summary Table
     experiment_summary_df = pd.DataFrame.from_records(
         experiment_summary,
         columns=[
@@ -232,6 +231,44 @@ def render_results():
     st.download_button(
         "Press to Download", csv, "summary.csv", "text/csv", key="download-records"
     )
+    ################################### SHOW BREAKDOWN
+    user_pf_by_model_pred = experiment_summary_df.groupby("Category").agg(
+        {"Is User Prediction Correct": ["count", "sum", "mean"]}
+    )
+    # rename columns
+    user_pf_by_model_pred.columns = user_pf_by_model_pred.columns.droplevel(0)
+    user_pf_by_model_pred.columns = [
+        "Count",
+        "Correct User Guess",
+        "Mean User Performance",
+    ]
+    user_pf_by_model_pred.index.name = "Model Prediction"
+    st.write("User performance break down by Model prediction:", user_pf_by_model_pred)
+    csv = convert_df(user_pf_by_model_pred)
+    st.download_button(
+        "Press to Download",
+        csv,
+        "user-performance-by-model-prediction.csv",
+        "text/csv",
+        key="download-performance-by-model-prediction",
+    )
+    ################################### CONFUSION MATRIX
+
+    confusion_matrix = pd.crosstab(
+        experiment_summary_df["Category"],
+        experiment_summary_df["User Prediction"],
+        rownames=["Actual"],
+        colnames=["Predicted"],
+    )
+    st.write("Confusion Matrix", confusion_matrix)
+    csv = convert_df(confusion_matrix)
+    st.download_button(
+        "Press to Download",
+        csv,
+        "confusion-matrix.csv",
+        "text/csv",
+        key="download-confusiion-matrix",
+    )
 
 
 def render_menu():