Update src/webApp.py

src/webApp.py

@@ -240,7 +240,6 @@ def shap_bar_chart(shap_values, feature_names):
         textposition="outside"
     ))
     fig.update_layout(
-        fig.update_layout(
         title="Feature Impact (SHAP)",
         xaxis_title="SHAP Impact (positive = pushes toward Suspicious)",
         yaxis_title="",
@@ -390,10 +389,14 @@ def interpret_shap_features(shap_df):
             strength = "strongly" if abs(impact) > 0.05 else "slightly"
 
         if feature == "review_frequency":
+            if impact > 0:
+                explanation = "Higher reviewer volume increases suspicion as it mirrors the high frequency posting patterns seen in coordinated fake review campaigns."
+            elif abs(impact) < 0.001:
+                explanation = "Reviewer volume had no meaningful impact on this prediction."
+            else:
+                explanation = "Lower reviewer volume reduces suspicion as this reviewer has not posted at high enough volume to suggest coordinated or incentivised reviewing behaviour."
             interpretations.append(
-                f"• **{feature}** ({abs(impact):.4f}): {strength} {direction}. "
-                "Higher reviewer volume increases suspicion as it mirrors the high frequency "
-                "posting patterns seen in coordinated fake review campaigns."
+                f"- **{feature}** ({abs(impact):.4f}): {strength} {direction}. {explanation}"
             )
         elif feature == "unverified_ratio":
             interpretations.append(
@@ -402,10 +405,14 @@ def interpret_shap_features(shap_df):
                 "behaviour where the reviewer may not have actually bought the product."
             )
         elif feature == "extreme_rating_ratio":
+            if impact > 0:
+                explanation = "An extreme rating was given. Extreme ratings (1 or 5 stars) appear more frequently in suspicious reviews, though they are equally common in genuine enthusiastic reviews. The model treats this as a mild signal rather than a strong flag."
+            elif abs(impact) < 0.001:
+                explanation = "Extreme rating had no meaningful impact on this prediction."
+            else:
+                explanation = "A non-extreme rating was given. Mid-range ratings are less commonly associated with suspicious behaviour and this pushed the model away from flagging this review."
             interpretations.append(
-                f"• **{feature}** ({abs(impact):.4f}): {strength} {direction}. "
-                "Extreme ratings (1 or 5 stars) are a weak signal, they appear in both "
-                "genuine and suspicious reviews, so the model gives this moderate weight."
+                f"- **{feature}** ({abs(impact):.4f}): {strength} {direction}. {explanation}"
             )
         elif feature == "very_short_review":
             interpretations.append(