Update app.py

app.py

@@ -1919,57 +1919,77 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
     
     submit_btn.click(complete_manual, [user_data, name_in, role_in, seniority_in, skills_in], [user_data, missing_section, interview_pre_section, pre_interview_greeting_md])
 
-    def start_interview_immediate(data):
-        """Start interview immediately, begin TTS generation in background"""
+    def start_interview(data):
+        # Initialize interview state
         state = {
-            "questions": [], "answers": [], "timings": [], "question_evaluations": [], "answer_evaluations": [],
-            "conversation_history": [], "difficulty_adjustment": None, "question_idx": 0, "max_questions": 3,
-            "q_start_time": time.time(), "log": []
+            "questions": [],
+            "answers": [],
+            "timings": [],
+            "question_evaluations": [],
+            "answer_evaluations": [],
+            "conversation_history": [],
+            "difficulty_adjustment": None,
+            "question_idx": 0,
+            "max_questions": 3,
+            "q_start_time": time.time(),
+            "log": []
         }
-        
-        # Generate question text first (fast)
+    
+        # Build prompt for first question
         context = ""
         prompt = build_interview_prompt(
-            conversation_history=[], user_response="", context=context, job_role=data["job_role"],
-            skills=data["skills"], seniority=data["seniority"], difficulty_adjustment=None, voice_label="neutral"
+            conversation_history=[],
+            user_response="",
+            context=context,
+            job_role=data["job_role"],
+            skills=data["skills"],
+            seniority=data["seniority"],
+            difficulty_adjustment=None,
+            voice_label="neutral"
         )
+    
+        # Generate first question
         first_q = groq_llm.predict(prompt)
-        q_eval = {"Score": "N/A", "Reasoning": "Skipped to reduce processing time", "Improvements": []}
-        
+        q_eval = {
+            "Score": "N/A",
+            "Reasoning": "Skipped to reduce processing time",
+            "Improvements": []
+        }
+    
         state["questions"].append(first_q)
         state["question_evaluations"].append(q_eval)
         state["conversation_history"].append({'role': 'Interviewer', 'content': first_q})
-        state["log"].append({"type": "question", "question": first_q, "question_eval": q_eval, "timestamp": time.time()})
-        
-        # Start TTS generation in background
-        tts_future_obj = bark_tts_async(first_q)
-        
-        # Return immediately with loading message
-        return (state, tts_future_obj, 
-                gr.update(visible=False), 
-                gr.update(visible=True), 
-                gr.update(visible=True, value="🔄 Generating audio..."),
-                gr.update(value=None), 
-                f"*Question 1:* {first_q}")
-
-    def check_tts_ready(state, tts_future_obj):
-        """Check if TTS is ready and update audio"""
-        if tts_future_obj and tts_future_obj.done():
-            try:
-                audio_path = tts_future_obj.result()
-                return gr.update(value=audio_path), gr.update(visible=False), None
-            except Exception as e:
-                print(f"TTS Error: {e}")
-                return gr.update(value=None), gr.update(value=f"Error generating audio: {e}"), None
-        else:
-            return gr.update(), gr.update(), tts_future_obj
-
+    
+        # Generate audio with Bark (wait for it)
+        start = time.perf_counter()
+        audio_future = bark_tts_async(first_q)
+        audio_path = audio_future.result()
+        print("⏱️ Bark TTS took", round(time.perf_counter() - start, 2), "seconds")
+    
+        # Log question
+        state["log"].append({
+            "type": "question",
+            "question": first_q,
+            "question_eval": q_eval,
+            "timestamp": time.time()
+        })
+    
+        return (
+            state,
+            gr.update(visible=False),  # Hide interview_pre_section
+            gr.update(visible=True),   # Show interview_section
+            audio_path,                # Set audio
+            f"*Question 1:* {first_q}" # Set question text
+        )
+    
+    # Hook into Gradio
     start_interview_final_btn.click(
-        start_interview_immediate, 
-        [user_data], 
-        [interview_state, tts_future, interview_pre_section, interview_section, loading_status, question_audio, question_text]
+        start_interview,
+        [user_data],
+        [interview_state, interview_pre_section, interview_section, question_audio, question_text]
     )
 
+
     def transcribe(audio_path):
         return whisper_stt(audio_path)