Upload webApp.py

src/webApp.py

@@ -131,10 +131,12 @@ def engineer_features(review, rating, verified, review_frequency):
     }
 
 
-def gauge_chart(value, title):
+def gauge_chart(value, title, threshold=0.5, bar_color=None):
     """
-    Renders a gauge chart showing model confidence.
+    Renders a gauge chart showing a model score.
+    The bar turns red above the decision threshold, which is also marked on the dial.
     """
+    t = threshold * 100
     fig = go.Figure(go.Indicator(
         mode="gauge+number",
         value=round(value * 100, 1),
@@ -142,12 +144,13 @@ def gauge_chart(value, title):
         title={"text": title},
         gauge={
             "axis": {"range": [0, 100]},
-            "bar": {"color": "#e63946" if value > 0.5 else "#2a9d8f"},
+            "bar": {"color": bar_color or ("#e63946" if value > threshold else "#2a9d8f")},
             "steps": [
-                {"range": [0, 40],   "color": "#d4edda"},
-                {"range": [40, 70],  "color": "#fff3cd"},
+                {"range": [0, t],    "color": "#d4edda"},
+                {"range": [t, 70],   "color": "#fff3cd"},
                 {"range": [70, 100], "color": "#f8d7da"},
             ],
+            "threshold": {"line": {"color": "#333", "width": 3}, "thickness": 0.8, "value": t},
         }
     ))
     fig.update_layout(height=250, margin=dict(t=40, b=0, l=20, r=20))
@@ -582,7 +585,7 @@ if analyse_clicked:
         pred = 1 if prob > threshold else 0
         label = "Suspicious" if pred == 1 else "Not Suspicious"
         prob_pct = round(prob * 100, 1)
-        likely = "likely" if prob > 0.4 else "likely not"
+        likely = "likely" if pred == 1 else "likely not"
 
         # Show reviewer frequency note
         if not freq_found:
@@ -601,19 +604,21 @@ if analyse_clicked:
         with sum_col2:
             if pred == 1:
                 st.error(
-                    f"Review: **Suspicious**  \nConfidence: **{prob_pct}%**")
+                    f"Review: **Suspicious**  \nSuspicion score: **{prob_pct}%**")
             else:
                 st.success(
-                    f"Review: **Not Suspicious**  \nConfidence: **{prob_pct}%**")
+                    f"Review: **Not Suspicious**  \nSuspicion score: **{prob_pct}%**")
 
         # side by side gauge charts
         gauge_col1, gauge_col2 = st.columns(2)
         with gauge_col1:
             st.plotly_chart(gauge_chart(
-                s_score, "Sentiment Confidence"), use_container_width=True)
+                s_score, "Sentiment Confidence",
+                bar_color="#2a9d8f" if s_label == "LABEL_1" else "#e63946"),
+                use_container_width=True)
         with gauge_col2:
             st.plotly_chart(gauge_chart(
-                prob, "Suspicion Confidence"), use_container_width=True)
+                prob, "Suspicion Score", threshold=threshold), use_container_width=True)
 
         # sentiment detailed breakdown
         with st.expander("Sentiment Analysis: Detailed Breakdown", expanded=True):
@@ -702,7 +707,8 @@ if analyse_clicked:
 
             # Interpretation sentence
             st.markdown(
-                f"The model is **{prob_pct}%** confident that this review is "
+                f"The model gives this review a suspicion score of **{prob_pct}%** "
+                f"(reviews above {int(threshold * 100)}% are flagged), so it is "
                 f"**{likely} suspicious** as a result of the following features:"
             )