Update - Level

app.py

@@ -45,9 +45,9 @@ def load_feedback_stats():
     except pd.errors.EmptyDataError:
         return {}
 
-def save_feedback(action, rating):
+def save_feedback(action, rating, display_df):
     if not action:
-        return "Please select a recommendation from the table first."
+        return "Please select a recommendation from the table first.", display_df
     norm_action = normalize_action(action)
     new_feedback = pd.DataFrame([{'action': norm_action, 'rating': int(rating)}])
     if not os.path.exists(FEEDBACK_FILE):
@@ -55,7 +55,20 @@ def save_feedback(action, rating):
     else:
         new_feedback.to_csv(FEEDBACK_FILE, mode='a', header=False, index=False)
     build_feedback_db()
-    return f"✅ Rating of {rating}/10 saved for: {action}"
+    
+    msg = f"✅ Rating of {rating}/10 saved for: {action}"
+    
+    # Update the displayed table dynamically
+    if display_df is not None and not display_df.empty:
+        try:
+            feedback_stats = load_feedback_stats()
+            default_stat = {'mean': 0, 'count': 0}
+            stats_list = [feedback_stats.get(normalize_action(act), default_stat) for act in display_df['Recommended Action']]
+            display_df['Avg. Feedback'] = [f"{stat['mean']:.2f}/10 ({int(stat['count'])})" for stat in stats_list]
+        except Exception as e:
+            print(f"Error updating display_df: {e}")
+
+    return msg, display_df
 
 def build_feedback_db():
     global feedback_vector_store
@@ -203,6 +216,14 @@ def fmea_rag_interface(mode, effect, cause, severity, occurrence, detection):
 
     return rpn_text, display_df, output_df
 
+def get_level_info(val):
+    levels = {
+        10: "Hazardous", 9: "Serious", 8: "Extreme", 7: "Major", 
+        6: "Significant", 5: "Moderate", 4: "Minor", 3: "Slight", 
+        2: "Very Slight", 1: "No Effect"
+    }
+    return gr.update(info=f"Level: {levels.get(val, '')}")
+
 # --- 6. Main Application Execution ---
 if build_rag_chain():
     print("\n🚀 Launching Gradio Interface...")
@@ -217,9 +238,13 @@ if build_rag_chain():
                     f_effect = gr.Textbox(label="Effect", placeholder="e.g., Reduced vehicle performance")
                     f_cause = gr.Textbox(label="Cause", placeholder="e.g., Coolant leak")
                 with gr.Column(scale=1):
-                    f_sev = gr.Slider(1, 10, value=5, step=1, label="Severity")
-                    f_occ = gr.Slider(1, 10, value=5, step=1, label="Occurrence")
-                    f_det = gr.Slider(1, 10, value=5, step=1, label="Detection")
+                    f_sev = gr.Slider(1, 10, value=5, step=1, label="Severity", info="Level: Moderate")
+                    f_occ = gr.Slider(1, 10, value=5, step=1, label="Occurrence", info="Level: Moderate")
+                    f_det = gr.Slider(1, 10, value=5, step=1, label="Detection", info="Level: Moderate")
+
+        f_sev.change(fn=get_level_info, inputs=f_sev, outputs=f_sev)
+        f_occ.change(fn=get_level_info, inputs=f_occ, outputs=f_occ)
+        f_det.change(fn=get_level_info, inputs=f_det, outputs=f_det)
 
         submit_btn = gr.Button("Get AI Recommendations", variant="primary")
 
@@ -268,8 +293,8 @@ if build_rag_chain():
 
         submit_feedback_btn.click(
             fn=save_feedback,
-            inputs=[selected_action_text, rating_slider],
-            outputs=[feedback_status]
+            inputs=[selected_action_text, rating_slider, recommendations_output],
+            outputs=[feedback_status, recommendations_output]
         )
 
     # Simplified launch command for Hugging Face