back version

backend/routes/interview_api.py

@@ -187,23 +187,22 @@ def download_report(application_id: int):
         logging.error(f"Error generating report for application {application_id}: {exc}")
         return jsonify({"error": "Failed to generate report"}), 500
 
-# Modified process_answer endpoint - replace the existing one with this:
-
 @interview_api.route("/process_answer", methods=["POST"])
 @login_required
 def process_answer():
     """
-    Process a user's answer and return a conversational follow-up question 
-    along with an evaluation. Always responds with JSON.
+    Process a user's answer and return a follow‑up question along with an
+    evaluation. Always responds with JSON.
     """
     try:
         data = request.get_json() or {}
         answer = data.get("answer", "").strip()
         question_idx = data.get("questionIndex", 0)
+
+        # ``job_id`` is required to determine how many total questions are
+        # expected for this interview.  Without it we fall back to a
+        # three‑question interview.
         job_id = data.get("job_id")
-        
-        # NEW: Get conversation history if provided
-        conversation_history = data.get("conversation_history", [])
 
         if not answer:
             return jsonify({"error": "No answer provided."}), 400
@@ -211,53 +210,64 @@ def process_answer():
         # Get the current question for evaluation context
         current_question = data.get("current_question", "Tell me about yourself")
 
-        # Evaluate the answer (now includes acknowledgment)
+        # Evaluate the answer
         evaluation_result = evaluate_answer(current_question, answer)
 
-        # Add current Q&A to conversation history
-        conversation_history.append((current_question, answer))
-
         # Determine the number of questions configured for this job
         total_questions = 3
-        job_role = "Software Developer"  # Default
-        
         if job_id is not None:
             try:
                 job = Job.query.get(int(job_id))
-                if job:
-                    if job.num_questions and job.num_questions > 0:
-                        total_questions = job.num_questions
-                    job_role = job.role  # Get the actual job role
+                if job and job.num_questions and job.num_questions > 0:
+                    total_questions = job.num_questions
             except Exception:
+                # If lookup fails, keep default
                 pass
 
-        # Check if interview is complete
+        # Check completion.  ``question_idx`` is zero‑based; the last index
+        # corresponds to ``total_questions - 1``.  When the current index
+        # reaches or exceeds this value, the interview is complete.
         is_complete = question_idx >= (total_questions - 1)
 
         next_question_text = None
         audio_url = None
 
         if not is_complete:
-            # Generate dynamic follow-up question based on the conversation
-            from backend.services.interview_engine import generate_dynamic_followup
-            
-            # Combine acknowledgment with the follow-up question
-            followup = generate_dynamic_followup(
-                previous_question=current_question,
-                candidate_answer=answer,
-                job_role=job_role,
-                conversation_history=conversation_history[:-1],  # Exclude current Q&A
-                question_number=question_idx,
-                total_questions=total_questions
+            # 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?"
             )
-            
-            # If the evaluation had a good score, we might want to prepend extra praise
-            if evaluation_result.get("score") == "Excellent":
-                next_question_text = followup
-            else:
-                next_question_text = followup
 
-            # Try to generate audio for the complete response
+            # 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
+            # question (i.e. equals total_questions - 1), use the final
+            # question.  Otherwise, select a follow‑up question from the
+            # 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
+            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?"
+
+            # Try to generate audio for the next question
             try:
                 audio_dir = "/tmp/audio"
                 os.makedirs(audio_dir, exist_ok=True)
@@ -277,14 +287,14 @@ def process_answer():
             "next_question": next_question_text,
             "audio_url": audio_url,
             "evaluation": evaluation_result,
-            "is_complete": is_complete,
-            "conversation_history": conversation_history  # Return updated history
+            "is_complete": is_complete
         })
 
     except Exception as e:
         logging.error(f"Error in process_answer: {e}")
-        return jsonify({"error": "Error processing answer. Please try again."}), 500@interview_api.route("/audio/<string:filename>", methods=["GET"])
+        return jsonify({"error": "Error processing answer. Please try again."}), 500
 
+@interview_api.route("/audio/<string:filename>", methods=["GET"])
 @login_required
 def get_audio(filename: str):
     """Serve previously generated TTS audio from the /tmp/audio directory."""

backend/services/interview_engine.py

@@ -9,7 +9,6 @@ import tempfile
 import shutil
 import torch
 
-# [KEEPING ALL THE INITIALIZATION CODE EXACTLY THE SAME]
 if torch.cuda.is_available():
     print("🔥 CUDA Available")
     print(torch.cuda.get_device_name(0))
@@ -21,9 +20,17 @@ print("🧠 GPU:", torch.cuda.get_device_name(0))
 print("💡 cuDNN version:", torch.backends.cudnn.version())
 print("💥 cuDNN enabled:", torch.backends.cudnn.is_available())
 
+
+
 # Initialize models
 chat_groq_api = os.getenv("GROQ_API_KEY")
 
+# Attempt to initialize the Groq LLM only if an API key is provided.  When
+# running in environments where the key is unavailable (such as local
+# development or automated testing), fall back to a simple stub that
+# generates generic responses.  This avoids raising an exception at import
+# time and allows the rest of the application to run without external
+# dependencies.  See the DummyGroq class defined below.
 if chat_groq_api:
     try:
         groq_llm = ChatGroq(
@@ -39,11 +46,31 @@ else:
 
 if groq_llm is None:
     class DummyGroq:
+        """A fallback language model used when no Groq API key is set.
+
+        The ``invoke`` method of this class returns a simple canned response
+        rather than calling an external API.  This ensures that the
+        interview functionality still produces a sensible prompt, albeit
+        without advanced LLM behaviour.
+        """
         def invoke(self, prompt: str):
+            # Provide a very generic question based on the prompt.  This
+            # implementation ignores the prompt contents entirely; in a more
+            # sophisticated fallback you could parse ``prompt`` to tailor
+            # responses.
             return "Tell me about yourself and why you're interested in this position."
+
     groq_llm = DummyGroq()
 
 # Initialize Whisper model
+#
+# Loading the Whisper model can take several seconds on first use because the
+# model weights must be downloaded from Hugging Face. This delay can cause
+# the API call to ``/api/transcribe_audio`` to appear stuck while the model
+# downloads. To mitigate this, we allow the model size to be configured via
+# the ``WHISPER_MODEL_NAME`` environment variable and preload the model when
+# this module is imported. Using a smaller model (e.g. "tiny" or "base.en")
+# reduces download size and inference time considerably.
 whisper_model = None
 
 def load_whisper_model():
@@ -52,11 +79,16 @@ def load_whisper_model():
         try:
             device = "cuda" if torch.cuda.is_available() else "cpu"
             compute_type = "float16" if device == "cuda" else "int8"
+            # Allow overriding the model size via environment. Default to a
+            # lightweight model to improve startup times. Available options
+            # include: tiny, base, base.en, small, medium, large. See
+            # https://huggingface.co/ggerganov/whisper.cpp for details.
             model_name = os.getenv("WHISPER_MODEL_NAME", "tiny")
             whisper_model = WhisperModel(model_name, device=device, compute_type=compute_type)
             logging.info(f"Whisper model '{model_name}' loaded on {device} with {compute_type}")
         except Exception as e:
             logging.error(f"Error loading Whisper model: {e}")
+            # Fallback to CPU
             whisper_model = WhisperModel(model_name if 'model_name' in locals() else "tiny", device="cpu", compute_type="int8")
     return whisper_model
 
@@ -75,11 +107,12 @@ def generate_first_question(profile, job):
         Generate an appropriate opening interview question that is professional and relevant.
         Keep it concise and clear. Respond with ONLY the question text, no additional formatting.
         If the interview is for a technical role, focus on technical skills. Make the question related
-        to the job role and the candidate's background.
+        to the job role and the candidate's background and the previous question.
         """
         
         response = groq_llm.invoke(prompt)
         
+        # Fix: Handle AIMessage object properly
         if hasattr(response, 'content'):
             question = response.content.strip()
         elif isinstance(response, str):
@@ -87,6 +120,7 @@ def generate_first_question(profile, job):
         else:
             question = str(response).strip()
             
+        # Ensure we have a valid question
         if not question or len(question) < 10:
             question = "Tell me about yourself and why you're interested in this position."
             
@@ -97,77 +131,15 @@ 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."
 
-# NEW FUNCTION: Generate dynamic follow-up questions based on the conversation
-def generate_dynamic_followup(previous_question, candidate_answer, job_role, conversation_history=None, question_number=1, total_questions=3):
-    """Generate a dynamic follow-up question based on the candidate's answer"""
-    try:
-        # Build conversation context
-        context = ""
-        if conversation_history:
-            for q, a in conversation_history:
-                context += f"\nQ: {q}\nA: {a}\n"
-        
-        prompt = f"""
-        You are an experienced interviewer conducting an interview for a {job_role} position.
-        
-        Previous conversation:
-        {context}
-        
-        Current question: {previous_question}
-        Candidate's answer: {candidate_answer}
-        
-        This is question {question_number + 1} out of {total_questions} total questions.
-        
-        Your task is to:
-        1. First, acknowledge their answer appropriately (e.g., "That's interesting", "Great point", "I see", "Excellent experience with...", etc.)
-        2. If the answer was particularly good, give brief positive feedback
-        3. Then ask a natural follow-up question that:
-           - Builds on what they just said
-           - Digs deeper into their experience or knowledge
-           - Relates to the job requirements
-           - Feels like a natural conversation flow
-        
-        Keep your response conversational and professional. The acknowledgment should be brief (1-2 sentences max).
-        
-        If this is the last question (question {total_questions}), make it about salary expectations, work preferences (remote/onsite), and availability.
-        
-        Respond with ONLY your acknowledgment and question, no additional formatting or metadata.
-        """
-        
-        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:
-            # Fallback questions with acknowledgments
-            fallbacks = [
-                "That's a good answer. Can you tell me more about a specific challenge you faced in that situation?",
-                "Interesting perspective. How do you stay updated with the latest developments in your field?",
-                "I appreciate your detailed response. What would you say is your greatest professional achievement?",
-                "Thank you for sharing that. Where do you see yourself professionally in the next 3-5 years?"
-            ]
-            question = fallbacks[question_number % len(fallbacks)]
-            
-        logging.info(f"Generated dynamic follow-up: {question}")
-        return question
-        
-    except Exception as e:
-        logging.error(f"Error generating dynamic follow-up: {e}")
-        return "Thank you for that answer. Can you tell me more about your experience in this area?"
-
-# [KEEPING ALL OTHER FUNCTIONS EXACTLY THE SAME]
 def edge_tts_to_file_sync(text, output_path, voice="en-US-AriaNeural"):
     """Synchronous wrapper for edge-tts with better error handling"""
     try:
+        # Ensure text is not empty
         if not text or not text.strip():
             logging.error("Empty text provided for TTS")
             return None
             
+        # Ensure the directory exists and is writable
         directory = os.path.dirname(output_path)
         if not directory:
             directory = "/tmp/audio"
@@ -175,6 +147,7 @@ def edge_tts_to_file_sync(text, output_path, voice="en-US-AriaNeural"):
         
         os.makedirs(directory, exist_ok=True)
         
+        # Test write permissions with a temporary file
         test_file = os.path.join(directory, f"test_{os.getpid()}.tmp")
         try:
             with open(test_file, 'w') as f:
@@ -183,6 +156,7 @@ def edge_tts_to_file_sync(text, output_path, voice="en-US-AriaNeural"):
             logging.info(f"Directory {directory} is writable")
         except (PermissionError, OSError) as e:
             logging.error(f"Directory {directory} is not writable: {e}")
+            # Fallback to /tmp
             directory = "/tmp/audio"
             output_path = os.path.join(directory, os.path.basename(output_path))
             os.makedirs(directory, exist_ok=True)
@@ -196,9 +170,11 @@ def edge_tts_to_file_sync(text, output_path, voice="en-US-AriaNeural"):
                 logging.error(f"Error in async TTS generation: {e}")
                 raise
         
+        # Run async function in sync context
         try:
             loop = asyncio.get_event_loop()
             if loop.is_running():
+                # If loop is already running, create a new one in a thread
                 import threading
                 import concurrent.futures
                 
@@ -212,10 +188,11 @@ def edge_tts_to_file_sync(text, output_path, voice="en-US-AriaNeural"):
                 
                 with concurrent.futures.ThreadPoolExecutor() as executor:
                     future = executor.submit(run_in_thread)
-                    future.result(timeout=30)
+                    future.result(timeout=30)  # 30 second timeout
             else:
                 loop.run_until_complete(generate_audio())
         except RuntimeError:
+            # No event loop exists
             loop = asyncio.new_event_loop()
             asyncio.set_event_loop(loop)
             try:
@@ -223,9 +200,10 @@ def edge_tts_to_file_sync(text, output_path, voice="en-US-AriaNeural"):
             finally:
                 loop.close()
         
+        # Verify file was created and has content
         if os.path.exists(output_path):
             file_size = os.path.getsize(output_path)
-            if file_size > 1000:
+            if file_size > 1000:  # At least 1KB for a valid audio file
                 logging.info(f"TTS file created successfully: {output_path} ({file_size} bytes)")
                 return output_path
             else:
@@ -257,7 +235,7 @@ def convert_webm_to_wav(webm_path, wav_path):
         logging.error(f"Error converting audio: {e}")
         return None
 
-import subprocess
+import subprocess  # top of the file if not already imported
 
 def whisper_stt(audio_path):
     """Speech-to-text using Faster-Whisper"""
@@ -266,10 +244,11 @@ def whisper_stt(audio_path):
             logging.error(f"Audio file is empty or missing: {audio_path}")
             return ""
 
+        # Convert webm to wav using ffmpeg
         wav_path = audio_path.replace(".webm", ".wav")
         cmd = [
             "ffmpeg",
-            "-y",
+            "-y",  # overwrite
             "-i", audio_path,
             "-ar", "16000",
             "-ac", "1",
@@ -290,15 +269,13 @@ def whisper_stt(audio_path):
         logging.error(f"Error in STT: {e}")
         return ""
 
-# ENHANCED EVALUATION FUNCTION with more conversational feedback
 def evaluate_answer(question, answer, job_role="Software Developer", seniority="Mid-level"):
-    """Evaluate candidate's answer with conversational feedback"""
+    """Evaluate candidate's answer with better error handling"""
     try:
         if not answer or not answer.strip():
             return {
                 "score": "Poor",
-                "feedback": "No answer provided.",
-                "acknowledgment": "I didn't catch your response. Could you please elaborate?"
+                "feedback": "No answer provided."
             }
             
         prompt = f"""
@@ -308,19 +285,18 @@ def evaluate_answer(question, answer, job_role="Software Developer", seniority="
         Candidate Answer: {answer}
         
         Evaluate based on technical correctness, clarity, and relevance.
-        Provide:
-        1. A brief, conversational acknowledgment (e.g., "Great example!", "Interesting approach", "Good point")
-        2. A score: Poor, Medium, Good, or Excellent
-        3. Brief constructive feedback (1-2 sentences)
+        Provide a brief evaluation in 1-2 sentences.
+        
+        Rate the answer as one of: Poor, Medium, Good, Excellent
         
         Respond in this exact format:
-        Acknowledgment: [Your brief acknowledgment]
         Score: [Poor/Medium/Good/Excellent]
         Feedback: [Your brief feedback here]
         """
         
         response = groq_llm.invoke(prompt)
         
+        # Handle AIMessage object properly
         if hasattr(response, 'content'):
             response_text = response.content.strip()
         elif isinstance(response, str):
@@ -328,34 +304,31 @@ def evaluate_answer(question, answer, job_role="Software Developer", seniority="
         else:
             response_text = str(response).strip()
         
+        # Parse the response
         lines = response_text.split('\n')
-        score = "Medium"
-        feedback = "Good answer, but could be more detailed."
-        acknowledgment = "Thank you for your response."
+        score = "Medium"  # default
+        feedback = "Good answer, but could be more detailed."  # default
         
         for line in lines:
             line = line.strip()
-            if line.startswith('Acknowledgment:'):
-                acknowledgment = line.replace('Acknowledgment:', '').strip()
-            elif line.startswith('Score:'):
+            if line.startswith('Score:'):
                 score = line.replace('Score:', '').strip()
             elif line.startswith('Feedback:'):
                 feedback = line.replace('Feedback:', '').strip()
         
+        # Ensure score is valid
         valid_scores = ["Poor", "Medium", "Good", "Excellent"]
         if score not in valid_scores:
             score = "Medium"
         
         return {
             "score": score,
-            "feedback": feedback,
-            "acknowledgment": acknowledgment
+            "feedback": feedback
         }
         
     except Exception as e:
         logging.error(f"Error evaluating answer: {e}")
         return {
             "score": "Medium",
-            "feedback": "Unable to evaluate answer at this time.",
-            "acknowledgment": "Thank you for your response."
+            "feedback": "Unable to evaluate answer at this time."
         }

backend/templates/interview.html

@@ -516,7 +516,6 @@
                     answers: [],
                     evaluations: []
                 };
-                this.conversationHistory = [];
                 this.initializeElements();
                 this.initializeInterview();
             }
@@ -840,98 +839,72 @@
                 this.recordingStatus.style.color = '#666';
             }
 
-            // Replace the existing submitAnswer method in your AIInterviewer class with this:
-
-async submitAnswer() {
-    const answerText = this.transcriptArea.textContent.trim();
-    
-    if (!answerText || answerText === this.transcriptArea.getAttribute('placeholder')) {
-        this.showError('Please provide an answer before submitting.');
-        return;
-    }
-    
-    // Show loading state
-    this.confirmButton.disabled = true;
-    this.confirmLoading.style.display = 'inline-block';
-    this.confirmButton.querySelector('span:first-child').textContent = 'Processing...';
-    
-    try {
-        const response = await fetch('/api/interview/process_answer', {
-            method: 'POST',
-            headers: {
-                'Content-Type': 'application/json',
-            },
-            body: JSON.stringify({
-                answer: answerText,
-                questionIndex: this.currentQuestionIndex,
-                job_id: JOB_ID,
-                current_question: this.currentQuestion,
-                conversation_history: this.conversationHistory || []  // Include conversation history
-            })
-        });
-        
-        const data = await response.json();
-        
-        if (data.success) {
-            // Update conversation history from response
-            if (data.conversation_history) {
-                this.conversationHistory = data.conversation_history;
-            }
-            
-            // Store answer and evaluation
-            this.interviewData.answers.push(answerText);
-            this.interviewData.evaluations.push(data.evaluation);
-            
-            // Display user's answer
-            this.addUserMessage(answerText);
-            
-            // Display evaluation with acknowledgment
-            const evalDiv = document.createElement('div');
-            evalDiv.className = 'ai-message';
-            evalDiv.innerHTML = `
-                <div class="ai-avatar">AI</div>
-                <div class="message-bubble" style="background: #e8f5e9;">
-                    <p><strong>${data.evaluation.acknowledgment || 'Thank you for your response.'}</strong></p>
-                    <p style="margin-top: 10px;">Score: <span class="evaluation-score">${data.evaluation.score}</span></p>
-                    <p style="margin-top: 5px; font-size: 0.9rem; color: #666;">${data.evaluation.feedback}</p>
-                </div>
-            `;
-            this.chatArea.appendChild(evalDiv);
-            this.chatArea.scrollTop = this.chatArea.scrollHeight;
-            
-            // Reset input for next question
-            this.resetForNextQuestion();
-            
-            if (!data.is_complete) {
-                // Move to next question after a short delay
-                setTimeout(() => {
-                    this.currentQuestionIndex++;
-                    this.currentQuestion = data.next_question;
-                    this.displayQuestion(data.next_question, data.audio_url);
-                    this.interviewData.questions.push(data.next_question);
-                }, 3000);  // 3 second delay to read evaluation
-            } else {
-                // Interview complete
-                setTimeout(() => {
-                    this.showInterviewSummary();
-                }, 2000);
-            }
-        } else {
-            this.showError(data.error || 'Error processing answer');
-        }
-    } catch (error) {
-        console.error('Error submitting answer:', error);
-        this.showError('Error submitting answer. Please try again.');
-    } finally {
-        this.confirmButton.disabled = false;
-        this.confirmLoading.style.display = 'none';
-        this.confirmButton.querySelector('span:first-child').textContent = 'Confirm Answer';
-    }
-}
-
-// Also add this property to the constructor of AIInterviewer class:
-// (Add this line in the constructor after this.interviewData)
+            async submitAnswer() {
+                const answer = this.transcriptArea.textContent.trim();
+                if (!answer) return;
+
+                console.log('Submitting answer:', answer);
 
+                // Show loading state
+                this.confirmButton.disabled = true;
+                this.confirmLoading.style.display = 'inline-block';
+                this.confirmButton.querySelector('span').style.display = 'none';
+
+                // Add user message to chat
+                this.addUserMessage(answer);
+
+                try {
+                    const response = await fetch('/api/process_answer', {
+                        method: 'POST',
+                        headers: {
+                            'Content-Type': 'application/json'
+                        },
+                        body: JSON.stringify({
+                            answer: answer,
+                            questionIndex: this.currentQuestionIndex,
+                            current_question: this.currentQuestion,
+                            job_id: JOB_ID
+                        })
+                    });
+
+                    if (!response.ok) {
+                        const errorText = await response.text();
+                        console.error('Process answer error:', response.status, errorText);
+                        throw new Error(`HTTP error! status: ${response.status}`);
+                    }
+
+                    const data = await response.json();
+                    console.log('Process answer response:', data);
+
+                    if (!data.success) {
+                        this.showError(data.error || 'Failed to process answer. Please try again.');
+                        return;
+                    }
+
+                    // Record the user's answer and its evaluation
+                    this.interviewData.answers.push(answer);
+                    this.interviewData.evaluations.push(data.evaluation || {});
+
+                    if (data.is_complete) {
+                        console.log('Interview completed');
+                        this.showInterviewSummary();
+                    } else {
+                        console.log('Moving to next question');
+                        this.currentQuestionIndex++;
+                        this.currentQuestion = data.next_question;
+                        this.displayQuestion(data.next_question, data.audio_url);
+                        this.interviewData.questions.push(data.next_question);
+                        this.resetForNextQuestion();
+                    }
+                } catch (error) {
+                    console.error('Error submitting answer:', error);
+                    this.showError('Connection error. Please try again.');
+                } finally {
+                    // Reset button state
+                    this.confirmLoading.style.display = 'none';
+                    this.confirmButton.querySelector('span').style.display = 'inline';
+                }
+            }
 
             addUserMessage(message) {
                 const messageDiv = document.createElement('div');