app.py

import streamlit as st
import json
import base64
import os
import time
import uuid
from backend import VirtualInterviewer
import pandas as pd

# Set page configuration
st.set_page_config(
    page_title="Virtual Interviewer",
    page_icon="🎯",
    layout="wide",
    initial_sidebar_state="expanded"
)

# Default job description and key topics for Solution Architect
DEFAULT_JOB_DESCRIPTION = """

Job Title: Enterprise Solution Architect



Job Description:

We are seeking an experienced Enterprise Solution Architect to design and implement innovative technology solutions that address complex business challenges. The ideal candidate will have a strong background in cloud architecture, enterprise integration, and modern application development.



Responsibilities:

- Design scalable, secure, and resilient enterprise solutions using cloud-native technologies

- Create architectural blueprints and technical roadmaps aligned with business objectives

- Evaluate and recommend appropriate technologies and frameworks for various business needs

- Lead technical discussions with stakeholders and development teams

- Ensure solutions adhere to architectural standards, best practices, and compliance requirements

- Mentor junior architects and developers on architectural principles and patterns



Requirements:

- 8+ years of experience in IT with at least 5 years in solution architecture

- Strong knowledge of Azure cloud services and architecture patterns

- Experience with Java enterprise applications and microservices architecture

- Familiarity with GraphQL API design and implementation

- Experience integrating with Salesforce and other enterprise systems

- Knowledge of Generative AI technologies and their practical applications

- Excellent communication and presentation skills

- Ability to translate business requirements into technical solutions

"""

DEFAULT_KEY_TOPICS = "Azure, GraphQL, Java, Salesforce, Generative AI, Cloud Architecture, Microservices, API Design"

# Custom CSS for styling
st.markdown("""

<style>

    .main-header {

        font-size: 2.5rem;

        color: #4527A0;

        font-weight: 700;

        margin-bottom: 0;

    }

    .sub-header {

        font-size: 1.1rem;

        color: #5E35B1;

        font-style: italic;

        margin-top: 0;

    }

    .section-header {

        font-size: 1.5rem;

        color: #3949AB;

        font-weight: 600;

        border-bottom: 2px solid #3949AB;

        padding-bottom: 0.3rem;

        margin-top: 1rem;

    }

    .question-header {

        font-size: 1.3rem;

        color: #1E88E5;

        font-weight: 600;

    }

    .question-number {

        font-size: 1rem;

        color: #0D47A1;

        font-weight: 500;

    }

    .success-text {

        color: #2E7D32;

        font-weight: 500;

    }

    .warning-text {

        color: #FF6F00;

        font-weight: 500;

    }

    .score-header {

        font-size: 1.4rem;

        color: #00897B;

        font-weight: 600;

    }

    .score-value {

        font-size: 1.8rem;

        color: #00695C;

        font-weight: 700;

    }

    .feedback-text {

        color: #455A64;

        font-style: italic;

    }

    .ideal-answer-header {

        color: #7B1FA2;

        font-weight: 600;

    }

    .user-answer-header {

        color: #0277BD;

        font-weight: 600;

    }

    .footer {

        text-align: center;

        color: #78909C;

        font-size: 0.8rem;

        margin-top: 2rem;

    }

    .stButton>button {

        background-color: #3949AB;

        color: white;

        font-weight: 500;

    }

    .stButton>button:hover {

        background-color: #303F9F;

        color: white;

    }

    .submit-button>button {

        background-color: #00897B;

        color: white;

    }

    .submit-button>button:hover {

        background-color: #00796B;

    }

    .score-button>button {

        background-color: #7B1FA2;

        color: white;

    }

    .score-button>button:hover {

        background-color: #6A1B9A;

    }

    .stExpander {

        border: 1px solid #E0E0E0;

        border-radius: 5px;

        margin-bottom: 1rem;

    }

    /* Voice selection styling */

    div[data-testid="stRadio"] > div {

        background-color: #E8EAF6;

        padding: 1rem;

        border-radius: 5px;

        margin-bottom: 1rem;

    }

    div[data-testid="stRadio"] label {

        font-weight: 500;

        color: #3949AB;

    }

    .audio-player {

        margin-top: 0.5rem;

        margin-bottom: 1rem;

    }

    /* Score table styling */

    .score-table {

        width: 100%;

        border-collapse: collapse;

        margin-bottom: 2rem;

        font-size: 1.1rem;

        box-shadow: 0 4px 8px rgba(0,0,0,0.1);

        border-radius: 5px;

        overflow: hidden;

    }

    .score-table th {

        background-color: #3949AB;

        color: white;

        font-weight: 600;

        text-align: left;

        padding: 0.75rem 1rem;

        border: none;

    }

    .score-table td {

        padding: 0.75rem 1rem;

        border-bottom: 1px solid #E0E0E0;

        vertical-align: middle;

    }

    .score-table tr:last-child {

        font-weight: bold;

        background-color: #E8EAF6;

    }

    .score-table tr:last-child td {

        border-top: 2px solid #3949AB;

        border-bottom: none;

        color: #3949AB;

    }

    .score-table tr:nth-child(even):not(:last-child) {

        background-color: #F5F5F5;

    }

    .score-table tr:hover:not(:last-child) {

        background-color: #EDE7F6;

    }

    /* Make the table responsive */

    @media screen and (max-width: 600px) {

        .score-table {

            font-size: 0.9rem;

        }

        .score-table th, .score-table td {

            padding: 0.5rem;

        }

    }

</style>

""", unsafe_allow_html=True)

# Function to create an HTML audio player
def get_audio_player_html(audio_path, autoplay=True, player_id=None):
    if not audio_path or not os.path.exists(audio_path):
        return ""
    
    # Generate a unique ID for this audio player if not provided
    if player_id is None:
        player_id = str(uuid.uuid4())
    
    # Read the audio file
    with open(audio_path, 'rb') as f:
        audio_bytes = f.read()
    
    audio_base64 = base64.b64encode(audio_bytes).decode()
    
    autoplay_attr = "autoplay" if autoplay else ""
    
    # Create HTML with JavaScript to ensure autoplay works
    html = f"""

    <div class="audio-player" id="audio-container-{player_id}">

        <audio id="audio-{player_id}" controls {autoplay_attr}>

            <source src="data:audio/mp3;base64,{audio_base64}" type="audio/mp3">

            Your browser does not support the audio element.

        </audio>

    </div>

    <script>

        // Force play the audio after a short delay

        setTimeout(function() {{

            const audioElement = document.getElementById('audio-{player_id}');

            if (audioElement) {{

                audioElement.play().catch(e => console.log('Auto-play failed:', e));

            }}

        }}, 500);

    </script>

    """
    return html

# Initialize session state variables if they don't exist
if 'interviewer' not in st.session_state:
    st.session_state.interviewer = None
if 'current_question_index' not in st.session_state:
    st.session_state.current_question_index = 0
if 'current_question' not in st.session_state:
    st.session_state.current_question = ""
if 'conversation_history' not in st.session_state:
    st.session_state.conversation_history = []
if 'interview_started' not in st.session_state:
    st.session_state.interview_started = False
if 'interview_setup_done' not in st.session_state:
    st.session_state.interview_setup_done = False
if 'questions_generated' not in st.session_state:
    st.session_state.questions_generated = False
if 'interview_completed' not in st.session_state:
    st.session_state.interview_completed = False
if 'interview_scored' not in st.session_state:
    st.session_state.interview_scored = False
if 'score_results' not in st.session_state:
    st.session_state.score_results = None
if 'answer_submitted' not in st.session_state:
    st.session_state.answer_submitted = False
if 'current_answer' not in st.session_state:
    st.session_state.current_answer = ""
if 'generate_ideal_answers' not in st.session_state:
    st.session_state.generate_ideal_answers = True
if 'voice_type' not in st.session_state:
    st.session_state.voice_type = "female_casual"
if 'use_tts' not in st.session_state:
    st.session_state.use_tts = False
if 'current_audio_path' not in st.session_state:
    st.session_state.current_audio_path = ""
if 'audio_key' not in st.session_state:
    st.session_state.audio_key = str(uuid.uuid4())
if 'should_play_audio' not in st.session_state:
    st.session_state.should_play_audio = True

# Define callback functions
def reset_answer_input():
    st.session_state.answer_submitted = False
    st.session_state.current_answer = ""
    # We don't modify st.session_state.user_answer directly

# Function to generate audio for a question
def ensure_audio_for_question(question, voice_type):
    """Ensure audio exists for the given question and return the path."""
    if not question:
        return ""
        
    # Check if we already have audio for this question
    audio_path = st.session_state.interviewer.get_question_audio_path(question)
    
    # If no audio exists, generate it
    if not audio_path:
        with st.spinner("Generating audio..."):
            audio_path = st.session_state.interviewer.generate_question_audio(question, voice_type)
    
    # Update the current audio path in session state
    st.session_state.current_audio_path = audio_path
    
    # Generate a new audio key to force refresh
    st.session_state.audio_key = str(uuid.uuid4())
    
    return audio_path

# Function to handle replay button click
def replay_audio():
    st.session_state.should_play_audio = True
    st.session_state.audio_key = str(uuid.uuid4())

# Title and description
st.markdown("<h1 class='main-header'>🎯 Virtual Interviewer</h1>", unsafe_allow_html=True)
st.markdown("<p class='sub-header'>An AI-powered interview simulator to help you prepare for your next job interview.</p>", unsafe_allow_html=True)

# Create a two-column layout
left_col, right_col = st.columns([1, 1])

with left_col:
    # Interview setup section
    if not st.session_state.interview_setup_done:
        st.markdown("<h2 class='section-header'>📋 Interview Setup</h2>", unsafe_allow_html=True)
        
        # 1a. Job requirement text box
        job_description = st.text_area(
            "💼 Job Description",
            value=DEFAULT_JOB_DESCRIPTION,
            height=200
        )
        
        # 1b. OpenAI API Key
        api_key = st.text_input(
            "🔑 OpenAI API Key",
            type="password",
            placeholder="Enter your OpenAI API key"
        )
        
        # 2. Interview type dropdown
        interview_type = st.selectbox(
            "🎭 Interview Type",
            options=["Technical", "Non-technical"]
        )
        
        # 3. Difficulty level dropdown
        difficulty_level = st.selectbox(
            "📊 Difficulty Level",
            options=["Easy", "Medium", "Hard"]
        )
        
        # 4. Key topics text box
        key_topics = st.text_input(
            "🔍 Key Topics",
            value=DEFAULT_KEY_TOPICS
        )
        
        # 5. Scoring option
        enable_scoring = st.radio(
            "📝 Enable Scoring?",
            options=["Yes", "No"],
            horizontal=True
        )
        
        # New option for generating ideal answers
        generate_ideal_answers = st.radio(
            "💡 Generate Ideal Answers?",
            options=["Yes", "No"],
            horizontal=True
        )
        
        # 6. Number of questions
        num_questions = st.number_input(
            "❓ Number of Questions",
            min_value=1,
            max_value=10,
            value=5
        )
        
        # Text-to-Speech options
        st.markdown("<h3 class='section-header'>🔊 Text-to-Speech Options</h3>", unsafe_allow_html=True)
        
        use_tts = st.checkbox("Enable Text-to-Speech for questions", value=True)
        
        if use_tts:
            # Voice type selection with a single radio button for all voices
            st.markdown("<p style='margin-top: 1rem; font-weight: 500;'>Select a voice for the interviewer:</p>", unsafe_allow_html=True)
            
            # Create a single radio button with all voice options
            voice_options = [
                "👨 Male - Casual (Guy)",
                "👨 Male - Formal (Christopher)",
                "👨 Male - British (Ryan)",
                "👩 Female - Casual (Jenny)",
                "👩 Female - Formal (Aria)",
                "👩 Female - British (Sonia)"
            ]
            
            selected_voice = st.radio(
                "Voice Selection",
                options=voice_options,
                index=3,  # Default to Female Casual
                label_visibility="collapsed"  # Hide the label since we already have a header
            )
            
            # Map the selected voice to the backend voice type
            voice_mapping = {
                "👨 Male - Casual (Guy)": "male_casual",
                "👨 Male - Formal (Christopher)": "male_formal",
                "👨 Male - British (Ryan)": "male_british",
                "👩 Female - Casual (Jenny)": "female_casual",
                "👩 Female - Formal (Aria)": "female_formal",
                "👩 Female - British (Sonia)": "female_british"
            }
            
            voice_type = voice_mapping.get(selected_voice, "female_casual")
        else:
            voice_type = "female_casual"
        
        # Start interview button
        if st.button("🚀 Start Interview"):
            if not api_key:
                st.error("⚠️ Please enter your OpenAI API key.")
            elif not job_description:
                st.error("⚠️ Please enter a job description.")
            else:
                with st.spinner("Setting up your interview..."):
                    try:
                        # Initialize the interviewer
                        st.session_state.interviewer = VirtualInterviewer(api_key)
                        
                        # Store whether to generate ideal answers
                        should_generate_ideal_answers = (generate_ideal_answers == "Yes")
                        st.session_state.generate_ideal_answers = should_generate_ideal_answers
                        
                        # Store TTS settings
                        st.session_state.use_tts = use_tts
                        st.session_state.voice_type = voice_type
                        
                        # Generate questions
                        questions = st.session_state.interviewer.generate_interview_questions(
                            job_description=job_description,
                            interview_type=interview_type,
                            difficulty_level=difficulty_level,
                            key_topics=key_topics,
                            num_questions=int(num_questions),
                            generate_ideal_answers=should_generate_ideal_answers
                        )
                        
                        # Generate audio for the first question if TTS is enabled
                        if use_tts and questions:
                            with st.spinner("Generating audio for first question..."):
                                audio_path = st.session_state.interviewer.generate_question_audio(questions[0], voice_type)
                                st.session_state.current_audio_path = audio_path
                                st.session_state.audio_key = str(uuid.uuid4())
                                st.session_state.should_play_audio = True
                        
                        # Store interview parameters for scoring later
                        st.session_state.job_description = job_description
                        st.session_state.interview_type = interview_type
                        st.session_state.difficulty_level = difficulty_level
                        st.session_state.enable_scoring = (enable_scoring == "Yes")
                        st.session_state.num_questions = int(num_questions)
                        
                        # Set the first question
                        st.session_state.current_question = questions[0]
                        st.session_state.questions_generated = True
                        st.session_state.interview_setup_done = True
                        st.session_state.interview_started = True
                        st.session_state.answer_submitted = False
                        st.session_state.current_answer = ""
                        
                        # Rerun to update the UI
                        st.rerun()
                    except Exception as e:
                        st.error(f"⚠️ Error setting up the interview: {str(e)}")
    
    # Interview in progress section
    elif st.session_state.interview_started and not st.session_state.interview_completed:
        st.markdown("<h2 class='section-header'>🎙️ Interview in Progress</h2>", unsafe_allow_html=True)
        
        # Display current question number
        st.markdown(f"<p class='question-number'>Question {st.session_state.current_question_index + 1} of {len(st.session_state.interviewer.questions_asked)}</p>", unsafe_allow_html=True)
        
        # Display conversation history
        if st.session_state.conversation_history:
            st.markdown("<h3 class='section-header'>📜 Previous Questions and Answers</h3>", unsafe_allow_html=True)
            for i, qa in enumerate(st.session_state.conversation_history):
                if i % 2 == 0:  # Question (even index)
                    st.markdown(f"<p><strong style='color: #3949AB;'>Q{i//2 + 1}:</strong> {qa}</p>", unsafe_allow_html=True)
                else:  # Answer (odd index)
                    st.markdown(f"<p><em style='color: #455A64;'>A: {qa}</em></p>", unsafe_allow_html=True)
                    st.markdown("<hr style='margin: 0.5rem 0; border-color: #E0E0E0;'>", unsafe_allow_html=True)
    
    # Interview completed section
    elif st.session_state.interview_completed:
        st.markdown("<h2 class='section-header'>🏁 Interview Completed</h2>", unsafe_allow_html=True)
        
        # Display conversation history
        if st.session_state.conversation_history:
            st.markdown("<h3 class='section-header'>📋 Interview Summary</h3>", unsafe_allow_html=True)
            for i, qa in enumerate(st.session_state.conversation_history):
                if i % 2 == 0:  # Question (even index)
                    st.markdown(f"<p><strong style='color: #3949AB;'>Q{i//2 + 1}:</strong> {qa}</p>", unsafe_allow_html=True)
                else:  # Answer (odd index)
                    st.markdown(f"<p><em style='color: #455A64;'>A: {qa}</em></p>", unsafe_allow_html=True)
                    st.markdown("<hr style='margin: 0.5rem 0; border-color: #E0E0E0;'>", unsafe_allow_html=True)
        
        # Option to restart
        if st.button("🔄 Start New Interview"):
            # Reset all session state variables
            for key in list(st.session_state.keys()):
                del st.session_state[key]
            st.rerun()

# Right column for displaying the current question and answer input
with right_col:
    if st.session_state.interview_started and not st.session_state.interview_completed:
        st.markdown("<h2 class='section-header'>❓ Current Question</h2>", unsafe_allow_html=True)
        
        # Display the current question
        st.markdown(f"<p class='question-header'>{st.session_state.current_question}</p>", unsafe_allow_html=True)
        
        # Display audio player if TTS is enabled
        if st.session_state.use_tts:
            # Ensure we have audio for the current question
            current_question = st.session_state.current_question
            audio_path = ensure_audio_for_question(current_question, st.session_state.voice_type)
            
            # Display audio player with unique key to force refresh
            if audio_path and os.path.exists(audio_path):
                # Create a container for the audio player
                audio_container = st.empty()
                
                # Display the audio player with the current audio key
                audio_container.markdown(
                    get_audio_player_html(
                        audio_path, 
                        autoplay=st.session_state.should_play_audio,
                        player_id=st.session_state.audio_key
                    ), 
                    unsafe_allow_html=True
                )
                
                # Reset the should_play_audio flag after displaying
                if st.session_state.should_play_audio:
                    st.session_state.should_play_audio = False
                
                # Add a button to replay the audio
                if st.button("🔊 Replay Question Audio"):
                    # Set flag to play audio and generate new key
                    replay_audio()
                    st.rerun()
        
        # Text area for the user's answer
        user_answer = st.text_area(
            "Your Answer",
            key="user_answer",
            height=300,
            placeholder="Type your answer here..."
        )
        
        # Submit button for the answer
        submit_button_col1, submit_button_col2, submit_button_col3 = st.columns([1, 1, 1])
        with submit_button_col2:
            if st.button("✅ Submit Answer", key="submit_answer"):
                if user_answer:  # Use the local variable, not session_state
                    st.session_state.answer_submitted = True
                    st.session_state.current_answer = user_answer  # Store the answer in a different session state variable
                    st.markdown("<p class='success-text'>✅ Answer submitted! Click 'Next Question' to continue.</p>", unsafe_allow_html=True)
                else:
                    st.markdown("<p class='warning-text'>⚠️ Please provide an answer before submitting.</p>", unsafe_allow_html=True)
    
    # Display score results if interview is scored
    elif st.session_state.interview_scored and st.session_state.score_results:
        st.markdown("<h2 class='section-header'>📊 Interview Results</h2>", unsafe_allow_html=True)
        
        score_results = st.session_state.score_results
        
        # Display overall score with a visual indicator
        if "overall_score" in score_results:
            overall_score = score_results['overall_score']
            
            # Create a visual score indicator
            score_color = "#4CAF50" if overall_score >= 4 else "#FF9800" if overall_score >= 3 else "#F44336"
            
            st.markdown(f"""

            <div style="background-color: #F3F4F6; padding: 1.5rem; border-radius: 10px; margin: 1rem 0; box-shadow: 0 2px 5px rgba(0,0,0,0.1);">

                <h3 style="color: #3949AB; margin-top: 0;">Overall Performance</h3>

                <div style="display: flex; align-items: center; margin-bottom: 1rem;">

                    <div style="background-color: {score_color}; color: white; font-size: 2rem; font-weight: bold; width: 80px; height: 80px; border-radius: 50%; display: flex; align-items: center; justify-content: center; margin-right: 1.5rem;">

                        {overall_score}/5

                    </div>

                    <div>

                        <p style="font-size: 1.1rem; margin: 0; color: #333;">{score_results['overall_feedback']}</p>

                    </div>

                </div>

            </div>

            """, unsafe_allow_html=True)
        
        # Display score table
        if "individual_scores" in score_results:
            st.markdown("<h3 class='section-header'>📈 Score Summary</h3>", unsafe_allow_html=True)
            
            # Extract data for the table
            questions = []
            scores = []
            
            for i, score_item in enumerate(score_results["individual_scores"]):
                # Truncate long questions for better display
                question_text = score_item['question']
                if len(question_text) > 80:
                    question_text = question_text[:77] + "..."
                
                questions.append(f"Q{i+1}: {question_text}")
                scores.append(score_item['score'])
            
            # Calculate average score
            if scores:
                avg_score = sum(scores) / len(scores)
                questions.append("**Average Score**")
                scores.append(f"**{avg_score:.2f}**")
            
            # Create DataFrame
            df = pd.DataFrame({
                "Question": questions,
                "Score (out of 5)": scores
            })
            
            # Convert DataFrame to HTML and display it
            table_html = df.to_html(classes='score-table', escape=False, index=False)
            st.markdown(f"""

            <div style="overflow-x: auto;">

                {table_html}

            </div>

            """, unsafe_allow_html=True)
        
        # Display individual scores with ideal answers
        if "individual_scores" in score_results:
            st.markdown("<h3 class='section-header'>📝 Detailed Feedback</h3>", unsafe_allow_html=True)
            
            st.markdown("""

            <p style="margin-bottom: 1rem;">Click on each question below to see detailed feedback and ideal answers.</p>

            """, unsafe_allow_html=True)
            
            for i, score_item in enumerate(score_results["individual_scores"]):
                # Determine score color
                score_value = score_item['score']
                score_color = "#4CAF50" if score_value >= 4 else "#FF9800" if score_value >= 3 else "#F44336"
                
                with st.expander(f"Question {i+1}: {score_item['question']}"):
                    st.markdown(f"""

                    <div style="display: flex; align-items: center; margin-bottom: 1rem;">

                        <div style="background-color: {score_color}; color: white; font-size: 1.2rem; font-weight: bold; width: 50px; height: 50px; border-radius: 50%; display: flex; align-items: center; justify-content: center; margin-right: 1rem;">

                            {score_value}/5

                        </div>

                        <div>

                            <p style="font-size: 1.1rem; margin: 0; color: #333; font-weight: 500;">{score_item['feedback']}</p>

                        </div>

                    </div>

                    """, unsafe_allow_html=True)
                    
                    st.markdown("<h4 class='user-answer-header'>🧑‍💼 Your Answer:</h4>", unsafe_allow_html=True)
                    st.markdown(f"{score_item['answer']}")
                    
                    # Only show ideal answers if they were generated
                    if st.session_state.generate_ideal_answers:
                        st.markdown("<h4 class='ideal-answer-header'>💡 Ideal Answer:</h4>", unsafe_allow_html=True)
                        if "ideal_answer" in score_item:
                            st.markdown(f"{score_item['ideal_answer']}")
                        else:
                            st.markdown("No ideal answer available.")
    
    # Display a welcome message if interview hasn't started
    elif not st.session_state.interview_started:
        st.markdown("<h2 class='section-header'>👋 Welcome to Virtual Interviewer</h2>", unsafe_allow_html=True)
        st.markdown("""

        <div style="background-color: #E8EAF6; padding: 1.5rem; border-radius: 10px; margin-top: 1rem;">

            <h3 style="color: #3949AB; font-size: 1.3rem; margin-bottom: 1rem;">This tool will help you practice for your upcoming interviews by:</h3>

            <ol style="color: #303F9F; font-size: 1.1rem;">

                <li>Generating relevant interview questions based on a job description</li>

                <li>Simulating a real interview experience</li>

                <li>Providing feedback on your answers</li>

            </ol>

            <p style="color: #3949AB; font-size: 1.1rem; margin-top: 1rem;">To get started, fill out the interview setup form on the left and click "Start Interview".</p>

        </div>

        """, unsafe_allow_html=True)

# Bottom section for control buttons
if st.session_state.interview_started and not st.session_state.interview_completed:
    st.markdown("<hr style='margin: 2rem 0 1rem 0;'>", unsafe_allow_html=True)
    
    # Create a container for the buttons at the bottom
    button_container = st.container()
    
    with button_container:
        col1, col2, col3 = st.columns([1, 1, 1])
        
        with col1:
            pass  # Empty column for spacing
        
        with col2:
            next_button_disabled = not st.session_state.answer_submitted
            if st.button("⏭️ Next Question", disabled=next_button_disabled, use_container_width=True):
                # Store the current question and answer
                if st.session_state.current_answer:  # Use current_answer instead of user_answer
                    # Add to conversation history
                    st.session_state.conversation_history.append(st.session_state.current_question)
                    st.session_state.conversation_history.append(st.session_state.current_answer)
                    
                    # Store in the interviewer object
                    st.session_state.interviewer.store_user_answer(
                        st.session_state.current_question,
                        st.session_state.current_answer
                    )
                    
                    # Move to the next question
                    st.session_state.current_question_index += 1
                    
                    # Check if we've reached the end of the questions
                    if st.session_state.current_question_index >= len(st.session_state.interviewer.questions_asked):
                        st.session_state.interview_completed = True
                        
                        # Automatically score the interview if scoring is enabled
                        if st.session_state.enable_scoring:
                            with st.spinner("Scoring your interview..."):
                                try:
                                    # Score the interview
                                    score_results = st.session_state.interviewer.score_interview(
                                        job_description=st.session_state.job_description,
                                        interview_type=st.session_state.interview_type,
                                        difficulty_level=st.session_state.difficulty_level
                                    )
                                    
                                    st.session_state.score_results = score_results
                                    st.session_state.interview_scored = True
                                except Exception as e:
                                    st.error(f"⚠️ Error scoring the interview: {str(e)}")
                        
                        st.rerun()
                    else:
                        # Get the next question
                        next_question = st.session_state.interviewer.get_next_question(
                            st.session_state.current_question_index
                        )
                        st.session_state.current_question = next_question
                        
                        # Reset submission status and current answer
                        reset_answer_input()
                        
                        # Set flag to play audio for the new question
                        st.session_state.should_play_audio = True
                        st.session_state.audio_key = str(uuid.uuid4())
                        
                        st.rerun()
                else:
                    st.warning("⚠️ Please provide an answer before moving to the next question.")
        
        with col3:
            if st.session_state.enable_scoring:
                if st.button("📊 Score Interview", use_container_width=True, key="score_button"):
                    if len(st.session_state.interviewer.user_answers) > 0:
                        with st.spinner("Scoring your interview..."):
                            try:
                                # Score the interview
                                score_results = st.session_state.interviewer.score_interview(
                                    job_description=st.session_state.job_description,
                                    interview_type=st.session_state.interview_type,
                                    difficulty_level=st.session_state.difficulty_level
                                )
                                
                                st.session_state.score_results = score_results
                                st.session_state.interview_scored = True
                                st.session_state.interview_completed = True
                                st.rerun()
                            except Exception as e:
                                st.error(f"⚠️ Error scoring the interview: {str(e)}")
                    else:
                        st.warning("⚠️ Please answer at least one question before scoring.")

# Footer
st.markdown("<hr style='margin: 2rem 0 1rem 0;'>", unsafe_allow_html=True)
st.markdown("<p class='footer'>🎯 Powered by OpenAI GPT-4o | Created with Streamlit</p>", unsafe_allow_html=True)