Add difference between auto scoring system and our own labels

supporting/scorer/views/stats.py

@@ -1,6 +1,9 @@
-from collections import Counter, defaultdict
+from collections import Counter
 import streamlit as st
 import pandas as pd
+import matplotlib.pyplot as plt
+
+THRESH = 0.7
 
 def page_title() -> str:
     return "Statistics"
@@ -9,22 +12,37 @@ def render():
     st.header("Statistics")
 
     word_counter = Counter()
-    doc_scores = defaultdict(list)
-    diff_scores = defaultdict(list)
+    confidence_counter = Counter()
 
     # ---- collect stats ----
     for entry in st.session_state.data:
-        doc_url = entry.get("documentUrl")
-
         for o in entry.get("output", []):
             for c in o.get("content_parsed", []):
 
                 # ---- extra_info word counts ----
                 extra = c.get("extra_info", "")
+                score = c.get("score", None)
+
                 if extra:
                     words = extra.strip().split()
                     word_counter.update(words)
 
+                # ---- confidence classification ----
+                if score is not None:
+                    extra_lower = extra.strip().lower()
+
+                    if score > THRESH and extra_lower == "perfect":
+                        confidence_counter["Correct"] += 1
+
+                    elif score > THRESH and extra_lower != "perfect":
+                        confidence_counter["Over-confident"] += 1
+
+                    elif score < THRESH and extra_lower == "perfect":
+                        confidence_counter["Under-confident"] += 1
+
+                    else:
+                        confidence_counter["Other"] += 1
+
     # --------------------------
     # Extra Info Word Counts
     # --------------------------
@@ -40,3 +58,28 @@ def render():
         st.bar_chart(df_words.set_index("Label"))
     else:
         st.info("No extra_info data available yet.")
+
+    # --------------------------
+    # Confidence vs Label Stats
+    # --------------------------
+    st.subheader("Confidence vs Label Distribution")
+
+    if confidence_counter:
+        df_conf = pd.DataFrame(
+            confidence_counter.items(),
+            columns=["Category", "Count"]
+        )
+
+        fig, ax = plt.subplots()
+        ax.pie(
+            df_conf["Count"],
+            labels=df_conf["Category"],
+            autopct="%1.1f%%",
+            startangle=90
+        )
+        ax.axis("equal")
+
+        st.pyplot(fig, width=500)
+
+    else:
+        st.info("No score data available yet.")