updated

backend/routes/interview_api.py

@@ -7,11 +7,13 @@ from flask_login import login_required, current_user
 from backend.models.database import db, Job, Application
 from backend.services.interview_engine import (
     generate_first_question,
+    generate_next_question, 
     edge_tts_to_file_sync,
     whisper_stt,
     evaluate_answer
 )
 
+
 # Additional imports for report generation
 from backend.models.database import Application
 from backend.services.report_generator import generate_llm_interview_report, create_pdf_report
@@ -233,23 +235,6 @@ def process_answer():
         audio_url = None
 
         if not is_complete:
-            # Follow‑up question bank.  These are used for indices 1 .. n‑2.
-            # The final question (last index) probes salary expectations and
-            # working preferences.  If the recruiter has configured fewer
-            # questions than the number of entries here, only the first
-            # appropriate number will be used.
-            follow_up_questions = [
-                "Can you describe a challenging project you've worked on and how you overcame the difficulties?",
-                "What is your favorite machine learning algorithm and why?",
-                "How do you stay up-to-date with advancements in AI?",
-                "Describe a time you had to learn a new technology quickly. How did you approach it?"
-            ]
-            final_question = (
-                "What are your salary expectations? Are you looking for a full-time or part-time role, "
-                "and do you prefer remote or on-site work?"
-            )
-
-            # Compute the next index (zero‑based) for the upcoming question
             next_idx = question_idx + 1
 
             # Determine which question to ask next.  If next_idx is the last
@@ -258,14 +243,35 @@ def process_answer():
             # bank based on ``next_idx - 1`` (because index 0 is for the
             # first follow‑up).  If out of range, cycle through the list.
             if next_idx == (total_questions - 1):
-                next_question_text = final_question
+                next_question_text = (
+                    "What are your salary expectations? Are you looking for a full-time or part-time role, "
+                    "and do you prefer remote or on-site work?"
+            )
             else:
-                if follow_up_questions:
-                    idx_in_bank = (next_idx - 1) % len(follow_up_questions)
-                    next_question_text = follow_up_questions[idx_in_bank]
-                else:
-                    # Fallback if no follow‑ups are defined
-                    next_question_text = "Do you have any questions about the role or our company?"
+                # 🔥 Use Qdrant-powered next question
+                try:
+                # You need profile + job for Qdrant context
+                    job = Job.query.get(int(job_id)) if job_id else None
+                    application = Application.query.filter_by(
+                        user_id=current_user.id,
+                        job_id=job_id
+                    ).first()
+
+                    profile = {}
+                    if application and application.extracted_features:
+                        profile = json.loads(application.extracted_features)
+
+                    conversation_history = data.get("conversation_history", [])
+                    next_question_text = generate_next_question(
+                        profile,
+                        job,
+                        conversation_history,
+                        answer
+                    )
+                except Exception as e:
+                    logging.error(f"Error generating next question from Qdrant: {e}")
+                    next_question_text = "Could you elaborate more on your last point?"
+
 
             # Try to generate audio for the next question
             try:

backend/services/interview_engine.py

@@ -129,7 +129,7 @@ def generate_first_question(profile, job):
         logging.warning("[QDRANT DEBUG] No questions retrieved, falling back to defaults")
 
     context_data = random_context_chunks(retrieved_data, k=4) if retrieved_data else ""
-    
+
     try:
         prompt = f"""
         You are conducting an interview for a {job.role} position at {job.company}.
@@ -168,6 +168,62 @@ def generate_first_question(profile, job):
         logging.error(f"Error generating first question: {e}")
         return "Tell me about yourself and why you're interested in this position."
 
+def generate_next_question(profile, job, conversation_history, last_answer):
+    """Generate the next interview question based on profile, job, and conversation so far"""
+    all_roles = extract_all_roles_from_qdrant()
+    logging.info(f"[QDRANT DEBUG] Available Roles: {all_roles}")
+
+    retrieved_data = retrieve_interview_data(job.role.lower(), all_roles)
+    logging.info(f"[QDRANT DEBUG] Role requested: {job.role.lower()}")
+    logging.info(f"[QDRANT DEBUG] Questions retrieved: {len(retrieved_data)}")
+    if retrieved_data:
+        logging.info(f"[QDRANT DEBUG] Sample Next Q: {retrieved_data[0]['question']}")
+    else:
+        logging.warning("[QDRANT DEBUG] No questions retrieved, falling back to defaults")
+
+    context_data = random_context_chunks(retrieved_data, k=4) if retrieved_data else ""
+
+    try:
+        prompt = f"""
+        You are continuing an interview for a {job.role} position at {job.company}.
+        Candidate's profile:
+        - Skills: {profile.get('skills', [])}
+        - Experience: {profile.get('experience', [])}
+        - Education: {profile.get('education', [])}
+
+        Conversation so far:
+        {conversation_history}
+
+        Candidate's last answer:
+        {last_answer}
+
+        Use the following context to generate the next question:
+        {context_data}
+
+        Generate an appropriate follow-up interview question that is professional and relevant.
+        Keep it concise and clear. If the interview is for a technical role, focus on technical skills.
+        """
+        
+        response = groq_llm.invoke(prompt)
+        
+        if hasattr(response, 'content'):
+            question = response.content.strip()
+        elif isinstance(response, str):
+            question = response.strip()
+        else:
+            question = str(response).strip()
+
+        if not question or len(question) < 10:
+            question = "Could you elaborate more on your last point?"
+            
+        logging.info(f"Generated next question: {question}")
+        return question
+
+    except Exception as e:
+        logging.error(f"Error generating next question: {e}")
+        return "Could you elaborate more on your last point?"
+
+
 def edge_tts_to_file_sync(text, output_path, voice="en-US-AriaNeural"):
     """Synchronous wrapper for edge-tts with better error handling"""
     try:
@@ -271,6 +327,61 @@ def convert_webm_to_wav(webm_path, wav_path):
     except (subprocess.TimeoutExpired, FileNotFoundError, Exception) as e:
         logging.error(f"Error converting audio: {e}")
         return None
+    
+def generate_next_question(profile, job, conversation_history, last_answer):
+    """Generate the next interview question based on profile, job, and conversation so far"""
+    all_roles = extract_all_roles_from_qdrant()
+    logging.info(f"[QDRANT DEBUG] Available Roles: {all_roles}")
+
+    retrieved_data = retrieve_interview_data(job.role.lower(), all_roles)
+    logging.info(f"[QDRANT DEBUG] Role requested: {job.role.lower()}")
+    logging.info(f"[QDRANT DEBUG] Questions retrieved: {len(retrieved_data)}")
+    if retrieved_data:
+        logging.info(f"[QDRANT DEBUG] Sample Next Q: {retrieved_data[0]['question']}")
+    else:
+        logging.warning("[QDRANT DEBUG] No questions retrieved, falling back to defaults")
+
+    context_data = random_context_chunks(retrieved_data, k=4) if retrieved_data else ""
+
+    try:
+        prompt = f"""
+        You are continuing an interview for a {job.role} position at {job.company}.
+        Candidate's profile:
+        - Skills: {profile.get('skills', [])}
+        - Experience: {profile.get('experience', [])}
+        - Education: {profile.get('education', [])}
+
+        Conversation so far:
+        {conversation_history}
+
+        Candidate's last answer:
+        {last_answer}
+
+        Use the following context to generate the next question:
+        {context_data}
+
+        Generate an appropriate follow-up interview question that is professional and relevant.
+        Keep it concise and clear. If the interview is for a technical role, focus on technical skills.
+        """
+        
+        response = groq_llm.invoke(prompt)
+        
+        if hasattr(response, 'content'):
+            question = response.content.strip()
+        elif isinstance(response, str):
+            question = response.strip()
+        else:
+            question = str(response).strip()
+
+        if not question or len(question) < 10:
+            question = "Could you elaborate more on your last point?"
+            
+        logging.info(f"Generated next question: {question}")
+        return question
+
+    except Exception as e:
+        logging.error(f"Error generating next question: {e}")
+        return "Could you elaborate more on your last point?"
 
 import subprocess  # top of the file if not already imported