Update app.py

app.py

@@ -173,7 +173,7 @@ class FaceAnalyzer:
             try:
                 face_landmarks = results.multi_face_landmarks[0]
                 landmarks = [(int(p.x * frame.shape[1]), int(p.y * frame.shape[0])) 
-                            for p in face_landmarks.landmark]
+                             for p in face_landmarks.landmark]
 
                 # Calculate EAR and MAR
                 left_ear = self.calculate_ear(LEFT_EYE, landmarks)
@@ -230,7 +230,7 @@ class FaceAnalyzer:
         processed_frame = cv2.addWeighted(overlay, alpha, frame, 1 - alpha, 0)
         
         cv2.putText(processed_frame, timer_text, (text_x, text_y),
-                   cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)
+                    cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)
 
         return (cv2.cvtColor(processed_frame, cv2.COLOR_BGR2RGB),
                f"{ear:.2f}" if ear is not None else "---",
@@ -361,8 +361,14 @@ answer_mapping = {
     "The symptoms of burnout that I’m experiencing won’t go away. I think about frustration at work a lot": 3,
     "I feel completely burned out and often wonder if I can go on. I am at the point where I may need some changes or may need to seek some sort of help": 4,
 }
-scaler=joblib.load('scaler.joblib')
-model = joblib.load('model.joblib')
+try:
+    scaler=joblib.load('scaler.joblib')
+    model = joblib.load('model.joblib')
+except FileNotFoundError:
+    gr.Warning("Model files not found. The app will run, but prediction will not work.")
+    scaler = None
+    model = None
+
 def preprocess_input(raw_dict, scaler):
     # Map questionnaire answers to ints
     q_encoded = [answer_mapping.get(ans, -1) for ans in raw_dict["Questionnaire Answers"]]
@@ -409,13 +415,15 @@ def preprocess_input(raw_dict, scaler):
     return X_scaled
 
 def predict_burnout(raw_dict):
+    if not scaler or not model:
+        raise RuntimeError("Model files not loaded. Cannot predict.")
+
     X_processed = preprocess_input(raw_dict, scaler)
     pred = model.predict(X_processed)
     # Assuming regression or float prediction; if classifier, adapt accordingly
     output_score = float(pred[0])
     return output_score
 
-
 # --- PDF Download Function ---
 def download_info_pdf():
     pdf_path = "info.pdf"
@@ -480,12 +488,12 @@ with gr.Blocks() as demo:
                 text_output = gr.Textbox(label="Transcribed Text")
                 sentiment_output = gr.Textbox(label="Sentiment Analysis (Pos, Neu, Neg, Comp)")
                 pitch_output = gr.Textbox(label="Pitch (Hz) ± Std Dev")
-                proceed_to_results_btn = gr.Button("Proceed to Results")
+                proceed_to_results_btn = gr.Button("Proceed to Suggestions")
+
+            # Suggestions
+            with gr.TabItem("Suggestions", id=3):
+                suggestion_output = gr.Textbox(label="Suggestion based on Burnout Score", interactive=False)
 
-            # Results
-            with gr.TabItem("Results", id=3):
-                results_json = gr.JSON(label="Full Results (Questionnaire, EAR/MAR, Emotion, Voice, Subtypes)")
-                burnout_score_output = gr.Textbox(label="Burnout Score", interactive=False)
 
     # Questionnaire submit → store answers + go to Face Analysis tab
     def questionnaire_to_face(*answers):
@@ -561,19 +569,19 @@ with gr.Blocks() as demo:
         outputs=[text_output, sentiment_output, pitch_output],
     )
 
-    # Proceed to Results
-    def proceed_to_results():
+    # Proceed to Suggestions tab
+    def proceed_to_suggestions():
         required_keys = ["questionnaire", "face", "emotion", "voice", "subtypes"]
         missing_keys = [key for key in required_keys if key not in stored_data]
 
         if missing_keys:
-            return {"Error": f"Missing data: {', '.join(missing_keys)}. Please complete all sections."}, "", gr.Tabs(selected=2)
+            return f"Error: Missing data: {', '.join(missing_keys)}. Please complete all sections.", gr.Tabs(selected=2)
 
         try:
-            # Defensive cleaning as you had
+            # Defensive cleaning
             subtype_counts = stored_data.get("subtypes", {})
             cleaned_subtypes = {str(emotion): {str(k): int(v) for k, v in subs.items()} 
-                                for emotion, subs in subtype_counts.items()}
+                                 for emotion, subs in subtype_counts.items()}
             
             results = aggregate_results(
                 stored_data["questionnaire"],
@@ -582,23 +590,66 @@ with gr.Blocks() as demo:
                 stored_data["voice"],
                 cleaned_subtypes,
             )
-            # Store results dict to later use for model prediction
-            stored_data["results"] = results
-
+            
             # Get burnout score from model prediction
-            burnout_score = predict_burnout(results)  # Your integration function
+            burnout_score = predict_burnout(results)
+
+            if burnout_score==0.0:
+                suggestion_text = """
+                Your burnout score lies between 0 to 20.
+                You're feeling good.
+                You're active and enjoying your work. The goal is to build habits to maintain that feeling and prevent stress from building up.
+                Maintain healthy work-life habits. This includes taking proper lunch breaks and not checking work emails after hours.
+                Practice gratitude journaling. Write down three things you are grateful for daily to build resilience.
+                Promote regular physical activity. Even a 20-minute walk can help reduce stress and improve your mood.
+                """
+            elif burnout_score==1.0:
+                suggestion_text = """
+                Your burnout score lies between 20 to 40.
+                You're feeling a bit tired.
+                You're under a little stress, but not completely burned out. Now is the time to build in small, intentional breaks to prevent burnout.
+                Use the Pomodoro Technique: Work for 25 minutes, then take a 5-minute break. This prevents stress from building up.
+                Try Box Breathing: Inhale (4s), Hold (4s), Exhale (4s), Hold (4s). This calms your nervous system quickly.
+                Schedule "disconnect" periods. Set specific times to turn off work notifications.
+                """
+            elif burnout_score==2.0:
+                suggestion_text = """
+                Your burnout score lies between 40 to 60.
+                You're starting to burn out.
+                You're feeling physical and emotional exhaustion. This is a critical stage. Set firm boundaries and actively manage your stress.
+                Implement a Digital Detox. Designate tech-free hours in the evening to mentally separate work from personal life.
+                Prioritize Physical Activity: Aim for 20-30 minutes of aerobic exercise, 3 times a week, to reduce stress hormones.
+                Focus on Sleep Hygiene: Ensure you get 7-9 hours of quality sleep to improve your body's ability to cope with stress.
+                """
+            elif burnout_score==3.0:
+                suggestion_text = """
+                Your burnout score lies between 60 to 80.
+                Your burnout symptoms are persistent.
+                They won't go away, and you're thinking about work constantly. You need a more structured approach and should consider professional help.
+                Take Mandatory Time Off. Use your vacation days to fully disconnect and recharge.
+                Consider a Mindfulness-Based Stress Reduction (MBSR) course. This is a more intensive program that provides long-term stress management skills.
+                Seek Professional Help. Contact your company's Employee Assistance Program (EAP) or a therapist for confidential support.
+                """
+            else:
+                suggestion_text = """
+                Your burnout score lies between 80 to 100.
+                You feel completely burned out.
+                You feel like you can't go on. Your health and safety are the top priority. You need immediate professional help.
+                Access Crisis Resources: If you are having thoughts of self-harm, immediately call a crisis hotline like the National Suicide Prevention Lifeline at 988.
+                Consult a Doctor Immediately. Seek urgent help from a doctor, psychologist, or psychiatrist.
+                Prioritize Health Over Work. Your health and safety are the most important thing. Work can wait.
+                """
             
-            return results, f"{burnout_score:.2f}", gr.Tabs(selected=3)
+            return suggestion_text, gr.Tabs(selected=3)
         except Exception as e:
-            return {"Error": f"Failed to generate results: {str(e)}"}, "", gr.Tabs(selected=2)
-
+            return f"Error: Failed to generate results: {str(e)}", gr.Tabs(selected=2)
 
 
     proceed_to_results_btn.click(
-        fn=proceed_to_results,
-        outputs=[results_json,burnout_score_output, tabs],
+        fn=proceed_to_suggestions,
+        outputs=[suggestion_output, tabs],
     )
 
     
 if __name__ == "__main__":
-    demo.launch(share=True)
+    demo.launch()