Update app.py

app.py

@@ -1,747 +1,747 @@
-
-import os
-import re
-import time
-from dotenv import load_dotenv
-import streamlit as st
-from gtts import gTTS
-import PyPDF2
-import google.generativeai as genai
-import speech_recognition as sr
-from random import sample
-import random
-from html import escape
-
-# ✅ MUST be the first Streamlit command
-st.set_page_config(page_title="GrillMaster", layout="wide")
-
-# Load API key
-load_dotenv()
-genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
-
-# Initialize session state
-for key, default in {
-    "generated_questions": [],
-    "current_question_index": 0,
-    "answers": [],
-    "evaluation_feedback": "",
-    "overall_score": 0,
-    "percentage_score": 0,
-    "is_recording": False,
-    "question_played": False,
-    "selected_domain": "",
-    "response_captured": False,
-    "timer_start": None,
-    "show_summary": False,
-    "recorded_text": "",
-    "recording_complete": False,
-    "recording_started": False,
-    "audio_played": False,
-    "question_start_time": 0.0,
-    "record_phase": ""
-}.items():
-    if key not in st.session_state:
-        st.session_state[key] = default
-
-# Utility functions
-def extract_pdf_text(uploaded_file):
-    pdf_reader = PyPDF2.PdfReader(uploaded_file)
-    return "".join(page.extract_text() or "" for page in pdf_reader.pages).strip()
-
-def get_questions(prompt, input_text, num_questions=3, max_retries=10):
-    model = genai.GenerativeModel('gemini-1.5-pro-latest')
-
-    if "previous_questions" not in st.session_state:
-        st.session_state["previous_questions"] = set()
-
-    new_questions = []
-    retries = 0
-
-    while len(new_questions) < num_questions and retries < max_retries:
-        # Add artificial noise/randomness to input
-        noise = f" [session: {random.randint(1000,9999)} time: {time.time()}]"
-        modified_input = input_text + noise
-
-        response = model.generate_content([prompt, modified_input])
-        questions = [q.strip("*•- ") for q in response.text.strip().split("") if q.strip() and "question" not in q.lower()]
-
-        for q in questions:
-            if q not in st.session_state["previous_questions"]:
-                st.session_state["previous_questions"].add(q)
-                new_questions.append(q)
-                if len(new_questions) == num_questions:
-                    break
-
-        retries += 1
-
-    return new_questions
-
-
-# def evaluate_answers():
-#     model = genai.GenerativeModel('gemini-1.5-pro-latest')
-#     prompt = """
-#     You are an expert interview evaluator. Assess responses based on:
-#     - Conceptual Understanding
-#     - Communication Skills
-#     - Clarity & Depth of Explanation
-#     - Use of Real-World Examples
-#     - Logical Flow
-
-#     Provide a score (out of 10) and an evaluation summary.
-
-#     **Format:**
-#     **Overall Score:** x/10
-#     **Evaluation Summary:**
-#     - Concept Understanding: .
-#     - Communication: .
-#     - Depth of Explanation: .
-#     - Examples: .
-#     - Logical Flow: .
-#     """
-#     candidate_responses = "\n\n".join(
-#         [f"Q: {entry['question']}\nA: {entry['response']}" for entry in st.session_state["answers"]]
-#     )
-#     full_prompt = f"{prompt}\n\nCandidate Responses:\n{candidate_responses}"
-#     response = model.generate_content(full_prompt)
-#     st.session_state["evaluation_feedback"] = response.text.strip()
-#     match = re.search(r"\*\*Overall Score:\*\* (\d+)/10", response.text)
-#     st.session_state["overall_score"] = int(match.group(1)) if match else 0
-#     st.session_state["percentage_score"] = st.session_state["overall_score"] * 10
-
-import asyncio
-import edge_tts
-
-import re
-import tempfile
-import asyncio
-import edge_tts
-
-async def generate_question_audio(question, voice="en-IE-EmilyNeural"):
-    clean_question = re.sub(r'[^A-Za-z0-9.,?! ]+', '', question)
-    tts = edge_tts.Communicate(text=clean_question, voice=voice)
-    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp_file:
-        await tts.save(tmp_file.name)
-        return tmp_file.name
-
-
-# === KEYWORD-BASED SCORING LOGIC ===
-
-KEYWORDS = {
-    "Analytics": {
-        "Python": ["loops", "list", "dictionary", "function", "pandas", "numpy"],
-        "SQL": ["join", "group by", "select", "where", "index", "foreign key"],
-        "Machine Learning": ["model", "features", "training", "accuracy", "regression"]
-    },
-    "Finance": {
-        "Fund Accounting": ["NAV", "mutual fund", "reconciliation", "journal entry"],
-        "AML/KYC": ["customer", "verification", "risk", "compliance", "money laundering"]
-    },
-    "Soft Skills": {
-        "default": ["communication", "teamwork", "problem solving", "motivation", "adaptability"]
-    }
-}
-
-def score_answer(answer_text, domain, skill):
-    answer = answer_text.strip().lower()
-    if answer in ["", "[no response]", "no response", "skipped"]:
-        return 0.0
-    if any(phrase in answer for phrase in ["don't know", "not sure", "unaware"]):
-        return 0.0
-
-    keywords = KEYWORDS.get(domain, {}).get(skill, KEYWORDS["Soft Skills"]["default"])
-    match_count = sum(1 for kw in keywords if kw in answer)
-    match_ratio = match_count / len(keywords) if keywords else 0
-
-    if match_ratio == 0:
-        return 1.5
-    elif match_ratio <= 0.5:
-        return 3.0
-    else:
-        return 5.0
-
-# Evaluate candidate answers - YOUR FUNCTION
- 
-
- 
-def evaluate_answers():
-    model = genai.GenerativeModel('gemini-1.5-pro-latest')
-    difficulty_level = st.session_state.get("difficulty_level_select", "Beginner")
-    level_string = difficulty_level.lower()
-
-    # --- Start: Check for all no-responses ---
-    all_no_response = True
-    if not st.session_state.get("answers"):
-        all_no_response = True
-    else:
-        for entry in st.session_state["answers"]:
-            response_text = str(entry.get('response', '')).strip().lower()
-            no_response_placeholders = [
-                "", "[no response provided]", "[no response - timed out]",
-                "[no response]", "no response", "[could not understand audio]",
-                "[no clear response recorded]"
-            ]
-            if response_text not in no_response_placeholders:
-                all_no_response = False
-                break
-    
-    if all_no_response:
-        st.session_state["evaluation_feedback"] = (
-            "**Overall Score:** 0/10\n"
-            "**Evaluation Summary:**\n"
-            "- Concept Understanding: N/A - No response provided.\n"
-            "- Communication: N/A - No response provided.\n"
-            "- Depth of Explanation: N/A - No response provided.\n"
-            "- Examples: N/A - No response provided.\n"
-            "- Logical Flow: N/A - No response provided.\n\n"
-            "The candidate did not provide any meaningful answers."
-        )
-        st.session_state["overall_score"] = 0
-        st.session_state["percentage_score"] = 0
-        return
-    # --- End: Check for all no-responses ---
-
-    base_assessment_criteria = """
-    Assess responses based on:
-    - Conceptual Understanding (effort and relevance more than perfect accuracy)
-    - Communication Clarity (can the core idea be understood?)
-    - Depth of Explanation (relative to expected level)
-    - Use of Examples (if any, and if appropriate for the level)
-    - Logical Flow (is there a basic structure or train of thought?)
-    """
-
-    if level_string == "beginner":
-        level_specific_instructions = """
-        You are an extremely understanding and encouraging interview evaluator for a **BEGINNER/FRESHER**. Your primary goal is to build confidence.
-        **Scoring Guidelines for Beginners:**
-        - Be VERY lenient. Focus on ANY sign of understanding or effort.
-        - A one-liner, if relevant, deserves a good score (e.g., 6-8/10). Partial correctness with effort: 7-9/10. Generally correct but brief: 8-10/10.(Just doubles the score)
-        - Only score 0/10 if completely irrelevant or no attempt.
-        - If all answers are empty/placeholders (like '[No response]'), overall score MUST be 0/10.
-        Provide highly positive, motivating feedback.
-        """
-    elif level_string == "intermediate":
-        level_specific_instructions = """
-        You are a supportive evaluator for an **INTERMEDIATE** candidate. Expect reasonable conceptual grasp.
-        **Scoring Guidelines for Intermediate:**
-        - 7–10: Mostly correct, clear, good understanding.
-        - 4–6: Partially correct, or needs clarity/depth.
-        - 1–3: Largely incorrect or irrelevant.
-        - Overall score 0/10 if all responses are empty.
-        Provide balanced feedback.
-        """
-    else: # Advanced
-        level_specific_instructions = """
-        You are a discerning evaluator for an **ADVANCED** candidate. Expect accuracy and depth.
-        **Scoring Guidelines for Advanced:**
-        - 8–10: Accurate, comprehensive, deep understanding.
-        - 5–7: Generally correct but lacks some depth/precision.
-        - 1–4: Significant inaccuracies or superficial.
-        - Overall score 0/10 if all responses are empty.
-        Provide precise feedback.
-        """
-
-    evaluation_prompt_template = f"""
-    {level_specific_instructions}
-    {base_assessment_criteria}
-
-    **YOUR RESPONSE MUST STRICTLY START WITH THE OVERALL SCORE ON THE VERY FIRST LINE.**
-    Follow this exact format for your entire output:
-
-    *Overall Score:* [score]/10
-    *Evaluation Summary:*
-    - Concept Understanding: [Your feedback here]
-    - Communication: [Your feedback here]
-    - Depth of Explanation: [Your feedback here]
-    - Examples: [Your feedback here]
-    - Logical Flow: [Your feedback here]
-    [Any additional overall encouraging remarks can optionally follow here]
-
-    The [score] must be a number (e.g., 7 or 7.5) between 0 and 10.
-    """
-
-    candidate_responses_formatted = "\n\n".join(
-        [f"Q: {entry['question']}\nA: {str(entry.get('response', '[No response provided]'))}" for entry in st.session_state["answers"]]
-    )
-    full_prompt_for_evaluation = f"{evaluation_prompt_template}\n\nCandidate Responses:\n{candidate_responses_formatted}"
-
-    try:
-        response_content = model.generate_content(full_prompt_for_evaluation)
-        st.session_state["evaluation_feedback"] = response_content.text.strip()
-        
-        extracted_text_for_scoring = response_content.text.strip()
-        print("--- LLM Output for Score Extraction (evaluate_answers) ---")
-        print(extracted_text_for_scoring)
-        print("----------------------------------------------------------")
-        
-        overall_score_val = 0.0 
-
-        # Pattern 1: More flexible, looks for "Overall Score" then captures number/10
-        score_pattern_flexible = r"(?i).*Overall Score[\s:]*(\d+(?:\.\d+)?)\s*/\s*10"
-        score_match = re.search(score_pattern_flexible, extracted_text_for_scoring)
-
-        if score_match:
-            try:
-                score_text = score_match.group(1)
-                print(f"Flexible Pattern Matched! Score text: '{score_text}', Full context: '{score_match.group(0)}'")
-                overall_score_val = float(score_text)
-                if overall_score_val.is_integer():
-                    overall_score_val = int(overall_score_val)
-                print(f"Parsed score value: {overall_score_val}")
-            except ValueError:
-                st.warning(f"Flexible pattern matched, but could not parse '{score_text}' as a number. Defaulting score to 0.")
-                print(f"ValueError during parsing (flexible pattern). Score text: '{score_text}'")
-                overall_score_val = 0.0
-        else:
-            # Fallback Pattern: Simplest possible X/10 if "Overall Score" line completely missing/mangled
-            score_pattern_fallback = r"(\d+(?:\.\d+)?)\s*/\s*10"
-            # Search for fallback only if primary pattern fails
-            print(f"Flexible pattern ('{score_pattern_flexible}') did not match. Trying fallback.")
-            score_match_fallback = re.search(score_pattern_fallback, extracted_text_for_scoring)
-            if score_match_fallback:
-                try:
-                    score_text = score_match_fallback.group(1)
-                    print(f"Fallback Pattern Matched! Score text: '{score_text}', Full context: '{score_match_fallback.group(0)}'")
-                    overall_score_val = float(score_text)
-                    if overall_score_val.is_integer():
-                        overall_score_val = int(overall_score_val)
-                    print(f"Parsed score value (from fallback): {overall_score_val}")
-                    st.warning("Used fallback regex to find score. LLM format for 'Overall Score' line was unexpected.")
-                except ValueError:
-                    st.warning(f"Fallback pattern matched, but could not parse '{score_text}' as a number. Score set to 0.")
-                    print(f"ValueError during parsing (fallback pattern). Score text: '{score_text}'")
-                    overall_score_val = 0.0
-            else:
-                st.warning(f"Could not find any 'X/10' score pattern in the LLM response. Score defaulted to 0. LLM Output (first 300 chars):\n{extracted_text_for_scoring[:300]}...")
-                print(f"All score patterns failed. LLM output did not contain a recognizable score.")
-                overall_score_val = 0.0
-        
-        st.session_state["overall_score"] = overall_score_val
-        st.session_state["percentage_score"] = float(overall_score_val) * 10 
-
-    except Exception as e:
-        st.error(f"An error occurred during evaluation or score parsing: {e}")
-        st.session_state["evaluation_feedback"] = (
-            f"Could not evaluate answers due to an error: {e}. "
-            f"LLM output might be missing or malformed."
-        )
-        st.session_state["overall_score"] = 0
-        st.session_state["percentage_score"] = 0
-
-# --- Prompts for Question Generation ---
-BEGINNER_PROMPT = """
-You are a friendly mock interview trainer conducting a **Beginner-level** spoken interview in the domain of **{domain}**.
-Ask basic verbal interview questions based on the candidate's input: **{input_text}**.
-
-Guidelines:
-- Ask simple conceptual questions.
-- Avoid jargon and complex examples.
-- Use easy language.
-- No coding or technical syntax required.
-Ensure the questions are clear, to the point, and suitable for a {difficulty_level}-level interview in {selected_domain}.
-**New Requirement:**
-🚫 **Do NOT repeat any questions from previous generations again and again.** Ensure all generated questions are unique and different from past sessions.
-
-**Guidelines:**
-    ✅ Questions should focus on key concepts, best practices, and problem-solving within {selected_domain}.
-    ✅ Ensure questions are direct, structured, and relevant to real-world applications.
-    ❌ Do NOT include greetings like 'Let's begin' or 'Welcome to the interview'.
-    ❌ Avoid vague or open-ended statements—each question should be concise and specific.
-"""
-
-INTERMEDIATE_PROMPT = """
-You are a professional mock interviewer conducting an **Intermediate-level** spoken interview in the domain of **{domain}**.
-Ask moderately challenging verbal interview questions based on the candidate's input: **{input_text}**.
-
-Guidelines:
-- Use a mix of conceptual and real-world scenario questions.
-- Include light critical thinking.
-- Still no need for code, formulas, or complex diagrams.
-Ensure the questions are clear, to the point, and suitable for a {difficulty_level}-level interview in {selected_domain}.
-**New Requirement:**
-🚫 **Do NOT repeat any questions from previous generations again and again.** Ensure all generated questions are unique and different from past sessions.
-
-**Guidelines:**
-    ✅ Questions should focus on key concepts, best practices, and problem-solving within {selected_domain}.
-    ✅ Ensure questions are direct, structured, and relevant to real-world applications.
-    ❌ Do NOT include greetings like 'Let's begin' or 'Welcome to the interview'.
-    ❌ Avoid vague or open-ended statements—each question should be concise and specific.
-"""
-
-ADVANCED_PROMPT = """
-You are a strict mock interviewer conducting an **Advanced-level** spoken interview in the domain of **{domain}**.
-Ask deep, analytical, real-world scenario-based questions from the candidate's input: **{input_text}**.
-
-Guidelines:
-- Expect detailed, logical, well-structured answers.
-- Include challenging “why” and “how” based questions.
-- No need for code, but assume candidate has high expertise.
-Ensure the questions are clear, to the point, and suitable for a {difficulty_level}-level interview in {selected_domain}.
-**New Requirement:**
-🚫 **Do NOT repeat any questions from previous generations again and again.** Ensure all generated questions are unique and different from past sessions.
-
-**Guidelines:**
-    ✅ Questions should focus on key concepts, best practices, and problem-solving within {selected_domain}.
-    ✅ Ensure questions are direct, structured, and relevant to real-world applications.
-    ❌ Do NOT include greetings like 'Let's begin' or 'Welcome to the interview'.
-    ❌ Avoid vague or open-ended statements—each question should be concise and specific.
-"""
-
-
-# UI styles
-st.markdown("""
-<style>
-    /* Base style for all stButton elements */
-    .stButton > button {
-        background-color: #007BFF !important;
-        color: white !important;
-        border-radius: 10px !important;
-        font-weight: bold !important;
-        width: 100% !important;
-        padding: 0.4rem 0.75rem !important;
-        font-size: 0.95rem !important;
-        line-height: 1.5 !important;
-        border: 1px solid transparent !important;
-        transition: background-color 0.2s ease-in-out, border-color 0.2s ease-in-out, box-shadow 0.2s ease-in-out !important;
-        margin-bottom: 8px !important;
-        box-sizing: border-box;
-    }
-    .stButton > button:hover {
-        background-color: #0056b3 !important;
-        color: white !important;
-        border-color: #0056b3 !important;
-    }
-    .stButton > button:focus,
-    .stButton > button:active {
-        background-color: #0056b3 !important;
-        border-color: #004085 !important;
-        box-shadow: 0 0 0 0.2rem rgba(0,123,255,.5) !important;
-        outline: none !important;
-    }
-
-    .timer-text {
-        font-size: 1.3rem;
-        font-weight: 600;
-        color: #00bcd4;
-        animation: pulse 1s infinite;
-    }
-    @keyframes pulse {
-        0% {opacity: 1;}
-        50% {opacity: 0.4;}
-        100% {opacity: 1;}
-    }
-
-    .summary-card {
-        background-color: #f9f9f9;
-        padding: 20px;
-        border-radius: 12px;
-        border: 1px solid #ddd;
-        box-shadow: 0 2px 6px rgba(0, 0, 0, 0.05);
-    }
-    /* More specific selector for the pre text color */
-    div.summary-card > pre {
-        white-space: pre-wrap !important;
-        word-wrap: break-word !important;
-        font-family: inherit !important;
-        font-size: 0.95rem !important;
-        color: #000000 !important;   /* TRYING PURE BLACK with !important */
-        background-color: #ffffff !important; /* Ensure background is white */
-        padding: 15px !important;
-        border-radius: 8px !important;
-        border: 1px solid #e0e0e0 !important;
-        max-height: 400px !important;
-        overflow-y: auto !important;
-    }
-</style>
-""", unsafe_allow_html=True)
-
-# Header
-st.markdown("""
-<div style='text-align: center; margin-top: -30px; padding-top: 10px;'>
-    <h1 style='font-size: 2.8rem; font-weight: 800; color: #003366;'>🎯 Welcome to <span style='color: #007BFF;'>GrillMaster</span></h1>
-    <p style='font-size: 1.1rem; color: #555;'>Your AI-powered mock interview assistant</p>
-</div>
-<hr style='border: 1px solid #e0e0e0; margin: 20px auto;'>
-""", unsafe_allow_html=True)
-
-if not st.session_state["generated_questions"]:
-    st.markdown("""
-    <div style='text-align: center; margin-top: -10px; margin-bottom: 30px;'>
-        <h3 style='font-weight: 700; color: #333;'>🚀 Let's get started!</h3>
-        <p style='font-size: 1rem; color: #666;'>Select your interview domain and input type to begin your practice session.</p>
-    </div>
-    <hr style='border: 1px solid #e0e0e0; margin-top: 0px;'>
-    """, unsafe_allow_html=True)
-
-# Example soft skills questions for HR/Soft Skills domain
-if st.session_state["selected_domain"] == "Soft Skills":
-        hr_questions = [
-    "Tell me about yourself.",
-    "Why should we hire you?",
-    "What are your strengths and weaknesses?",
-    "What is the difference between hard work and smart work?",
-    "Why do you want to work at our company?",
-    "How do you feel about working nights and weekends?",
-    "Can you work under pressure?",
-    "What are your goals?",
-    "Are you willing to relocate or travel?",
-    "What motivates you to do good job?",
-    "What would you want to accomplish within your first 30 days of employment?",
-    "What do you prefer working alone or in collaborative environment?",
-    "Give me an example of your creativity.",
-    "How long would you expect to work for us if hired?",
-    "Are not you overqualified for this position?",
-    "Describe your ideal company, location and job.",
-    "Explain how would you be an asset to this organization?",
-    "What are your interests?",
-    "Would you lie for the company?",
-    "Who has inspired you in your life and why?",
-    "What was the toughest decision you ever had to make?",
-    "Have you considered starting your own business?",
-    "How do you define success and how do you measure up to your own definition?",
-    "Tell me something about our company.",
-    "How much salary do you expect?",
-    "Where do you see yourself five years from now?",
-    "Do you have any questions for me?",
-    "Are you a manager or a leader?",
-    "Imagine that you are not lucky enough to get this job, how will you take it?"
-]
-
-# === Sidebar: Domain and Input Configuration ===
-st.sidebar.subheader("Select Interview Domain:")
-for domain in ["Analytics", "Finance", "Soft Skills"]:
-    if st.sidebar.button(domain):
-        st.session_state["selected_domain"] = domain
-        st.session_state["generated_questions"] = []
-        st.session_state["current_question_index"] = 0
-        st.session_state["answers"] = []
-        st.session_state["evaluation_feedback"] = ""
-        st.session_state["recorded_text"] = ""
-        st.session_state["response_captured"] = False
-        st.session_state["timer_start"] = None
-        st.session_state["show_summary"] = False
-        st.session_state["question_played"] = False
-        st.session_state["recording_complete"] = False
-        st.rerun()
-
-if not st.session_state["selected_domain"]:
-    st.sidebar.info("Please select a domain to continue.")
-    st.stop()
-
-st.sidebar.markdown(f"**Selected Domain:** {st.session_state['selected_domain']}")
-num_qs = st.sidebar.slider("Number of Questions:", 1, 10, 3)
-
-if st.session_state["selected_domain"] == "Soft Skills":
-    if st.sidebar.button("Generate Questions"):
-        st.session_state["generated_questions"] = sample(hr_questions, num_qs)
-        st.session_state["current_question_index"] = 0
-        st.rerun()
-else:
-    section_choice = st.sidebar.radio("Choose Input Type:", ("Resume", "Job Description", "Skills"))
-    difficulty = st.sidebar.selectbox("Select Difficulty Level:", ["Beginner", "Intermediate", "Advanced"])
-    input_text = ""
-
-    if section_choice == "Resume":
-        uploaded_file = st.sidebar.file_uploader("Upload Resume:", type=["pdf", "txt"])
-        if uploaded_file:
-            input_text = extract_pdf_text(uploaded_file)
-
-    elif section_choice == "Job Description":
-        input_text = st.sidebar.text_area("Paste Job Description:")
-
-    elif section_choice == "Skills":
-        # Define available skills for each domain
-        skills = {
-            "Analytics": ["Python", "SQL", "Machine Learning", "Statistics", "Business Analytics"],
-            "Finance": ["Fund Accounting", "AML/KYC", "Derivatives"]
-            }
-        skill_list = skills.get(st.session_state["selected_domain"], [])
-        if skill_list:
-            selected_skill = st.sidebar.selectbox("Select a Skill:", skill_list, key="skill_select")
-            input_text = selected_skill
-            st.sidebar.markdown(f"✅ Selected Skill: **{selected_skill}**")  # Debug display
-
-    if st.sidebar.button("Generate Questions"):
-        if not input_text.strip():
-            st.warning("⚠️ Please provide input based on the selected method.")
-            st.stop()
-
-        prompt = f"Ask {num_qs} direct and core-level {difficulty} interview questions related to {input_text}. Do not include intros or numbering."
-        model = genai.GenerativeModel('gemini-1.5-pro-latest')
-        response = model.generate_content([prompt, input_text])
-        lines = response.text.strip().split("\n")
-        questions = [q.strip("* ") for q in lines if q.strip()]
-        st.session_state["generated_questions"] = questions[:num_qs]
-        st.session_state["current_question_index"] = 0
-        st.session_state["answers"] = []
-        st.session_state["evaluation_feedback"] = ""
-        st.session_state["recorded_text"] = ""
-        st.session_state["response_captured"] = False
-        st.session_state["timer_start"] = None
-        st.session_state["show_summary"] = False
-        st.session_state["question_played"] = False
-        st.session_state["recording_complete"] = False
-        st.rerun()
-
-# === Main QA Interface ===
-if st.session_state["generated_questions"]:
-    idx = st.session_state["current_question_index"]
-    if idx < len(st.session_state["generated_questions"]):
-        question = st.session_state["generated_questions"][idx].lstrip("1234567890. ").strip()
-
-        # Phase 0: Play audio first and wait 5s before countdown
-        if not st.session_state.get("question_played"):
-            st.session_state["question_audio_file"] = asyncio.run(generate_question_audio(question))
-            st.session_state.update({
-                "question_played": True,
-                "question_start_time": time.time(),
-                "record_phase": "audio_playing",
-                "recorded_text": ""
-            })
-
-        st.markdown(f"**Q{idx + 1}:** {question}")
-        st.audio(st.session_state["question_audio_file"], format="audio/mp3")
-
-        now = time.time()
-        elapsed = now - st.session_state.get("question_start_time", 0)
-
-        if st.session_state["record_phase"] == "audio_playing":
-            if elapsed < 5:
-                st.markdown(f"<h4 class='timer-text'>🔊 Playing question audio... Please listen</h4>", unsafe_allow_html=True)
-                time.sleep(1)
-                st.rerun()
-            else:
-                st.session_state["record_phase"] = "waiting_to_start"
-                st.session_state["question_start_time"] = time.time()
-                st.rerun()
-
-        elif st.session_state["record_phase"] == "waiting_to_start":
-            remaining = 10 - int(elapsed)
-            if remaining > 0:
-                st.markdown(f"<h4 class='timer-text'>⏳ {remaining} seconds to click 'Start Recording'...</h4>", unsafe_allow_html=True)
-                if st.button("🎙️ Start Recording"):
-                    st.session_state.update({
-                        "record_phase": "recording",
-                        "timer_start": time.time(),
-                        "recording_started": False
-                    })
-                    st.rerun()
-                time.sleep(1)
-                st.rerun()
-            else:
-                st.markdown("<div style='padding:10px; background:#fff8e1; border-left:5px solid orange;color: #212529;'>⚠️ <strong>No action detected.</strong> Automatically skipping to next question...</div>", unsafe_allow_html=True)
-                st.session_state["answers"].append({"question": question, "response": "[No response]"})
-                st.session_state.update({
-                    "record_phase": "idle",
-                    "question_played": False,
-                    "question_start_time": 0.0,
-                    "current_question_index": idx + 1
-                })
-                if st.session_state["current_question_index"] == len(st.session_state["generated_questions"]):
-                    evaluate_answers()
-                    st.session_state["show_summary"] = True
-                st.rerun()
-
-        elif st.session_state["record_phase"] == "recording":
-            remaining = 15 - int(now - st.session_state.get("timer_start", 0))
-            if remaining > 0:
-                st.markdown(f"<h4 class='timer-text'>🎙️ {remaining} seconds to answer...</h4>", unsafe_allow_html=True)
-                recognizer = sr.Recognizer()
-                with sr.Microphone() as source:
-                    recognizer.adjust_for_ambient_noise(source)
-                    try:
-                        audio = recognizer.listen(source, timeout=1, phrase_time_limit=1)
-                        try:
-                            result = recognizer.recognize_google(audio)
-                            st.session_state.update({
-                                "recorded_text": result,
-                                "record_phase": "listening"
-                            })
-                        except sr.UnknownValueError:
-                            pass
-                    except sr.WaitTimeoutError:
-                        pass
-                time.sleep(1)
-                st.rerun()
-            else:
-                st.markdown("<div style='padding:10px; background:#fff3e0; border-left:5px solid orange;'>⚠️ <strong>No response detected.</strong> Moving to next question...</div>", unsafe_allow_html=True)
-                st.session_state["answers"].append({"question": question, "response": "[No response]"})
-                st.session_state.update({
-                    "record_phase": "idle",
-                    "recording_started": False,
-                    "question_played": False,
-                    "question_start_time": 0.0,
-                    "current_question_index": idx + 1
-                })
-                if st.session_state["current_question_index"] == len(st.session_state["generated_questions"]):
-                    evaluate_answers()
-                    st.session_state["show_summary"] = True
-                st.rerun()
-
-        elif st.session_state["record_phase"] == "listening":
-            st.success("🎧 Listening... You may stop when ready.")
-            if st.button("⏹️ Stop Recording"):
-                st.session_state["answers"].append({"question": question, "response": st.session_state["recorded_text"]})
-                st.session_state.update({
-                    "record_phase": "idle",
-                    "recorded_text": "",
-                    "recording_started": False,
-                    "question_played": False,
-                    "question_start_time": 0.0,
-                    "current_question_index": idx + 1
-                })
-                if st.session_state["current_question_index"] == len(st.session_state["generated_questions"]):
-                    evaluate_answers()
-                    st.session_state["show_summary"] = True
-                st.rerun()
-
-
-# === Summary Display ===
-if st.session_state.get("show_summary", False):
-    # st.balloons()
-    st.subheader("📊 Complete Mock Interview Summary")
-
-    feedback_content = st.session_state.get('evaluation_feedback', "Evaluation not yet available.")
-    if not isinstance(feedback_content, str):
-        feedback_content = str(feedback_content)
-    
-    # Escape HTML special characters from the feedback for security
-    escaped_feedback_for_html = escape(feedback_content)
-    # Replace newlines with <br> for HTML display within markdown
-    formatted_feedback_for_markdown = escaped_feedback_for_html.replace("\n", "<br>")
-
-    st.markdown(f"""
-        <div class='summary-card'>
-            <h4 style="color: #212529;">✅ <strong>Overall Score (Model Evaluation):</strong></h4>{st.session_state.get('percentage_score', 0):.0f}% ({st.session_state.get('overall_score',0)}/10)</h4>
-            <div style='margin:10px 0; position:relative;'>
-                <div style="color: #212529; background-color: #00c851; border-radius:10px; overflow:hidden; height:30px; position:relative;">
-                    <div style="
-                        width:{st.session_state.get('percentage_score', 0)}%;
-                        background:#00c851;
-                        height:100%;
-                        border-radius:10px 0 0 10px;
-                        transition: width 0.4s ease-in-out;
-                    "></div>
-                    <div style="
-                        position:absolute;
-                        top:0;
-                        left:0;
-                        width:100%;
-                        height:100%;
-                        display:flex;
-                        align-items:center;
-                        justify-content:center;
-                        font-weight:bold;
-                        color: black !important; 
-                        font-size: 0.9rem;
-                        user-select:none;
-                    ">
-                        {st.session_state.get('percentage_score', 0):.0f}%
-                    </div>
-                </div>
-            </div>
-            <h4 style="color: #212529;">Detailed Evaluation:</h4>
-            <div style="color: #212529; background-color: #ffffff; padding: 10px; border-radius: 5px; border: 1px solid #eee; margin-top: 5px; white-space: pre-wrap; word-wrap: break-word; max-height: 400px; overflow-y: auto;">
-                {formatted_feedback_for_markdown}
-            </div>
-        </div>
-    """, unsafe_allow_html=True)
+
+import os
+import re
+import time
+from dotenv import load_dotenv
+import streamlit as st
+from gtts import gTTS
+import PyPDF2
+import google.generativeai as genai
+import speech_recognition as sr
+from random import sample
+import random
+from html import escape
+
+# ✅ MUST be the first Streamlit command
+st.set_page_config(page_title="GrillMaster", layout="wide")
+
+# Load API key
+#load_dotenv()
+genai.configure(api_key="GOOGLE_API_KEY")
+
+# Initialize session state
+for key, default in {
+    "generated_questions": [],
+    "current_question_index": 0,
+    "answers": [],
+    "evaluation_feedback": "",
+    "overall_score": 0,
+    "percentage_score": 0,
+    "is_recording": False,
+    "question_played": False,
+    "selected_domain": "",
+    "response_captured": False,
+    "timer_start": None,
+    "show_summary": False,
+    "recorded_text": "",
+    "recording_complete": False,
+    "recording_started": False,
+    "audio_played": False,
+    "question_start_time": 0.0,
+    "record_phase": ""
+}.items():
+    if key not in st.session_state:
+        st.session_state[key] = default
+
+# Utility functions
+def extract_pdf_text(uploaded_file):
+    pdf_reader = PyPDF2.PdfReader(uploaded_file)
+    return "".join(page.extract_text() or "" for page in pdf_reader.pages).strip()
+
+def get_questions(prompt, input_text, num_questions=3, max_retries=10):
+    model = genai.GenerativeModel('gemini-1.5-pro-latest')
+
+    if "previous_questions" not in st.session_state:
+        st.session_state["previous_questions"] = set()
+
+    new_questions = []
+    retries = 0
+
+    while len(new_questions) < num_questions and retries < max_retries:
+        # Add artificial noise/randomness to input
+        noise = f" [session: {random.randint(1000,9999)} time: {time.time()}]"
+        modified_input = input_text + noise
+
+        response = model.generate_content([prompt, modified_input])
+        questions = [q.strip("*•- ") for q in response.text.strip().split("") if q.strip() and "question" not in q.lower()]
+
+        for q in questions:
+            if q not in st.session_state["previous_questions"]:
+                st.session_state["previous_questions"].add(q)
+                new_questions.append(q)
+                if len(new_questions) == num_questions:
+                    break
+
+        retries += 1
+
+    return new_questions
+
+
+# def evaluate_answers():
+#     model = genai.GenerativeModel('gemini-1.5-pro-latest')
+#     prompt = """
+#     You are an expert interview evaluator. Assess responses based on:
+#     - Conceptual Understanding
+#     - Communication Skills
+#     - Clarity & Depth of Explanation
+#     - Use of Real-World Examples
+#     - Logical Flow
+
+#     Provide a score (out of 10) and an evaluation summary.
+
+#     **Format:**
+#     **Overall Score:** x/10
+#     **Evaluation Summary:**
+#     - Concept Understanding: .
+#     - Communication: .
+#     - Depth of Explanation: .
+#     - Examples: .
+#     - Logical Flow: .
+#     """
+#     candidate_responses = "\n\n".join(
+#         [f"Q: {entry['question']}\nA: {entry['response']}" for entry in st.session_state["answers"]]
+#     )
+#     full_prompt = f"{prompt}\n\nCandidate Responses:\n{candidate_responses}"
+#     response = model.generate_content(full_prompt)
+#     st.session_state["evaluation_feedback"] = response.text.strip()
+#     match = re.search(r"\*\*Overall Score:\*\* (\d+)/10", response.text)
+#     st.session_state["overall_score"] = int(match.group(1)) if match else 0
+#     st.session_state["percentage_score"] = st.session_state["overall_score"] * 10
+
+import asyncio
+import edge_tts
+
+import re
+import tempfile
+import asyncio
+import edge_tts
+
+async def generate_question_audio(question, voice="en-IE-EmilyNeural"):
+    clean_question = re.sub(r'[^A-Za-z0-9.,?! ]+', '', question)
+    tts = edge_tts.Communicate(text=clean_question, voice=voice)
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp_file:
+        await tts.save(tmp_file.name)
+        return tmp_file.name
+
+
+# === KEYWORD-BASED SCORING LOGIC ===
+
+KEYWORDS = {
+    "Analytics": {
+        "Python": ["loops", "list", "dictionary", "function", "pandas", "numpy"],
+        "SQL": ["join", "group by", "select", "where", "index", "foreign key"],
+        "Machine Learning": ["model", "features", "training", "accuracy", "regression"]
+    },
+    "Finance": {
+        "Fund Accounting": ["NAV", "mutual fund", "reconciliation", "journal entry"],
+        "AML/KYC": ["customer", "verification", "risk", "compliance", "money laundering"]
+    },
+    "Soft Skills": {
+        "default": ["communication", "teamwork", "problem solving", "motivation", "adaptability"]
+    }
+}
+
+def score_answer(answer_text, domain, skill):
+    answer = answer_text.strip().lower()
+    if answer in ["", "[no response]", "no response", "skipped"]:
+        return 0.0
+    if any(phrase in answer for phrase in ["don't know", "not sure", "unaware"]):
+        return 0.0
+
+    keywords = KEYWORDS.get(domain, {}).get(skill, KEYWORDS["Soft Skills"]["default"])
+    match_count = sum(1 for kw in keywords if kw in answer)
+    match_ratio = match_count / len(keywords) if keywords else 0
+
+    if match_ratio == 0:
+        return 1.5
+    elif match_ratio <= 0.5:
+        return 3.0
+    else:
+        return 5.0
+
+# Evaluate candidate answers - YOUR FUNCTION
+ 
+
+ 
+def evaluate_answers():
+    model = genai.GenerativeModel('gemini-1.5-pro-latest')
+    difficulty_level = st.session_state.get("difficulty_level_select", "Beginner")
+    level_string = difficulty_level.lower()
+
+    # --- Start: Check for all no-responses ---
+    all_no_response = True
+    if not st.session_state.get("answers"):
+        all_no_response = True
+    else:
+        for entry in st.session_state["answers"]:
+            response_text = str(entry.get('response', '')).strip().lower()
+            no_response_placeholders = [
+                "", "[no response provided]", "[no response - timed out]",
+                "[no response]", "no response", "[could not understand audio]",
+                "[no clear response recorded]"
+            ]
+            if response_text not in no_response_placeholders:
+                all_no_response = False
+                break
+    
+    if all_no_response:
+        st.session_state["evaluation_feedback"] = (
+            "**Overall Score:** 0/10\n"
+            "**Evaluation Summary:**\n"
+            "- Concept Understanding: N/A - No response provided.\n"
+            "- Communication: N/A - No response provided.\n"
+            "- Depth of Explanation: N/A - No response provided.\n"
+            "- Examples: N/A - No response provided.\n"
+            "- Logical Flow: N/A - No response provided.\n\n"
+            "The candidate did not provide any meaningful answers."
+        )
+        st.session_state["overall_score"] = 0
+        st.session_state["percentage_score"] = 0
+        return
+    # --- End: Check for all no-responses ---
+
+    base_assessment_criteria = """
+    Assess responses based on:
+    - Conceptual Understanding (effort and relevance more than perfect accuracy)
+    - Communication Clarity (can the core idea be understood?)
+    - Depth of Explanation (relative to expected level)
+    - Use of Examples (if any, and if appropriate for the level)
+    - Logical Flow (is there a basic structure or train of thought?)
+    """
+
+    if level_string == "beginner":
+        level_specific_instructions = """
+        You are an extremely understanding and encouraging interview evaluator for a **BEGINNER/FRESHER**. Your primary goal is to build confidence.
+        **Scoring Guidelines for Beginners:**
+        - Be VERY lenient. Focus on ANY sign of understanding or effort.
+        - A one-liner, if relevant, deserves a good score (e.g., 6-8/10). Partial correctness with effort: 7-9/10. Generally correct but brief: 8-10/10.(Just doubles the score)
+        - Only score 0/10 if completely irrelevant or no attempt.
+        - If all answers are empty/placeholders (like '[No response]'), overall score MUST be 0/10.
+        Provide highly positive, motivating feedback.
+        """
+    elif level_string == "intermediate":
+        level_specific_instructions = """
+        You are a supportive evaluator for an **INTERMEDIATE** candidate. Expect reasonable conceptual grasp.
+        **Scoring Guidelines for Intermediate:**
+        - 7–10: Mostly correct, clear, good understanding.
+        - 4–6: Partially correct, or needs clarity/depth.
+        - 1–3: Largely incorrect or irrelevant.
+        - Overall score 0/10 if all responses are empty.
+        Provide balanced feedback.
+        """
+    else: # Advanced
+        level_specific_instructions = """
+        You are a discerning evaluator for an **ADVANCED** candidate. Expect accuracy and depth.
+        **Scoring Guidelines for Advanced:**
+        - 8–10: Accurate, comprehensive, deep understanding.
+        - 5–7: Generally correct but lacks some depth/precision.
+        - 1–4: Significant inaccuracies or superficial.
+        - Overall score 0/10 if all responses are empty.
+        Provide precise feedback.
+        """
+
+    evaluation_prompt_template = f"""
+    {level_specific_instructions}
+    {base_assessment_criteria}
+
+    **YOUR RESPONSE MUST STRICTLY START WITH THE OVERALL SCORE ON THE VERY FIRST LINE.**
+    Follow this exact format for your entire output:
+
+    *Overall Score:* [score]/10
+    *Evaluation Summary:*
+    - Concept Understanding: [Your feedback here]
+    - Communication: [Your feedback here]
+    - Depth of Explanation: [Your feedback here]
+    - Examples: [Your feedback here]
+    - Logical Flow: [Your feedback here]
+    [Any additional overall encouraging remarks can optionally follow here]
+
+    The [score] must be a number (e.g., 7 or 7.5) between 0 and 10.
+    """
+
+    candidate_responses_formatted = "\n\n".join(
+        [f"Q: {entry['question']}\nA: {str(entry.get('response', '[No response provided]'))}" for entry in st.session_state["answers"]]
+    )
+    full_prompt_for_evaluation = f"{evaluation_prompt_template}\n\nCandidate Responses:\n{candidate_responses_formatted}"
+
+    try:
+        response_content = model.generate_content(full_prompt_for_evaluation)
+        st.session_state["evaluation_feedback"] = response_content.text.strip()
+        
+        extracted_text_for_scoring = response_content.text.strip()
+        print("--- LLM Output for Score Extraction (evaluate_answers) ---")
+        print(extracted_text_for_scoring)
+        print("----------------------------------------------------------")
+        
+        overall_score_val = 0.0 
+
+        # Pattern 1: More flexible, looks for "Overall Score" then captures number/10
+        score_pattern_flexible = r"(?i).*Overall Score[\s:]*(\d+(?:\.\d+)?)\s*/\s*10"
+        score_match = re.search(score_pattern_flexible, extracted_text_for_scoring)
+
+        if score_match:
+            try:
+                score_text = score_match.group(1)
+                print(f"Flexible Pattern Matched! Score text: '{score_text}', Full context: '{score_match.group(0)}'")
+                overall_score_val = float(score_text)
+                if overall_score_val.is_integer():
+                    overall_score_val = int(overall_score_val)
+                print(f"Parsed score value: {overall_score_val}")
+            except ValueError:
+                st.warning(f"Flexible pattern matched, but could not parse '{score_text}' as a number. Defaulting score to 0.")
+                print(f"ValueError during parsing (flexible pattern). Score text: '{score_text}'")
+                overall_score_val = 0.0
+        else:
+            # Fallback Pattern: Simplest possible X/10 if "Overall Score" line completely missing/mangled
+            score_pattern_fallback = r"(\d+(?:\.\d+)?)\s*/\s*10"
+            # Search for fallback only if primary pattern fails
+            print(f"Flexible pattern ('{score_pattern_flexible}') did not match. Trying fallback.")
+            score_match_fallback = re.search(score_pattern_fallback, extracted_text_for_scoring)
+            if score_match_fallback:
+                try:
+                    score_text = score_match_fallback.group(1)
+                    print(f"Fallback Pattern Matched! Score text: '{score_text}', Full context: '{score_match_fallback.group(0)}'")
+                    overall_score_val = float(score_text)
+                    if overall_score_val.is_integer():
+                        overall_score_val = int(overall_score_val)
+                    print(f"Parsed score value (from fallback): {overall_score_val}")
+                    st.warning("Used fallback regex to find score. LLM format for 'Overall Score' line was unexpected.")
+                except ValueError:
+                    st.warning(f"Fallback pattern matched, but could not parse '{score_text}' as a number. Score set to 0.")
+                    print(f"ValueError during parsing (fallback pattern). Score text: '{score_text}'")
+                    overall_score_val = 0.0
+            else:
+                st.warning(f"Could not find any 'X/10' score pattern in the LLM response. Score defaulted to 0. LLM Output (first 300 chars):\n{extracted_text_for_scoring[:300]}...")
+                print(f"All score patterns failed. LLM output did not contain a recognizable score.")
+                overall_score_val = 0.0
+        
+        st.session_state["overall_score"] = overall_score_val
+        st.session_state["percentage_score"] = float(overall_score_val) * 10 
+
+    except Exception as e:
+        st.error(f"An error occurred during evaluation or score parsing: {e}")
+        st.session_state["evaluation_feedback"] = (
+            f"Could not evaluate answers due to an error: {e}. "
+            f"LLM output might be missing or malformed."
+        )
+        st.session_state["overall_score"] = 0
+        st.session_state["percentage_score"] = 0
+
+# --- Prompts for Question Generation ---
+BEGINNER_PROMPT = """
+You are a friendly mock interview trainer conducting a **Beginner-level** spoken interview in the domain of **{domain}**.
+Ask basic verbal interview questions based on the candidate's input: **{input_text}**.
+
+Guidelines:
+- Ask simple conceptual questions.
+- Avoid jargon and complex examples.
+- Use easy language.
+- No coding or technical syntax required.
+Ensure the questions are clear, to the point, and suitable for a {difficulty_level}-level interview in {selected_domain}.
+**New Requirement:**
+🚫 **Do NOT repeat any questions from previous generations again and again.** Ensure all generated questions are unique and different from past sessions.
+
+**Guidelines:**
+    ✅ Questions should focus on key concepts, best practices, and problem-solving within {selected_domain}.
+    ✅ Ensure questions are direct, structured, and relevant to real-world applications.
+    ❌ Do NOT include greetings like 'Let's begin' or 'Welcome to the interview'.
+    ❌ Avoid vague or open-ended statements—each question should be concise and specific.
+"""
+
+INTERMEDIATE_PROMPT = """
+You are a professional mock interviewer conducting an **Intermediate-level** spoken interview in the domain of **{domain}**.
+Ask moderately challenging verbal interview questions based on the candidate's input: **{input_text}**.
+
+Guidelines:
+- Use a mix of conceptual and real-world scenario questions.
+- Include light critical thinking.
+- Still no need for code, formulas, or complex diagrams.
+Ensure the questions are clear, to the point, and suitable for a {difficulty_level}-level interview in {selected_domain}.
+**New Requirement:**
+🚫 **Do NOT repeat any questions from previous generations again and again.** Ensure all generated questions are unique and different from past sessions.
+
+**Guidelines:**
+    ✅ Questions should focus on key concepts, best practices, and problem-solving within {selected_domain}.
+    ✅ Ensure questions are direct, structured, and relevant to real-world applications.
+    ❌ Do NOT include greetings like 'Let's begin' or 'Welcome to the interview'.
+    ❌ Avoid vague or open-ended statements—each question should be concise and specific.
+"""
+
+ADVANCED_PROMPT = """
+You are a strict mock interviewer conducting an **Advanced-level** spoken interview in the domain of **{domain}**.
+Ask deep, analytical, real-world scenario-based questions from the candidate's input: **{input_text}**.
+
+Guidelines:
+- Expect detailed, logical, well-structured answers.
+- Include challenging “why” and “how” based questions.
+- No need for code, but assume candidate has high expertise.
+Ensure the questions are clear, to the point, and suitable for a {difficulty_level}-level interview in {selected_domain}.
+**New Requirement:**
+🚫 **Do NOT repeat any questions from previous generations again and again.** Ensure all generated questions are unique and different from past sessions.
+
+**Guidelines:**
+    ✅ Questions should focus on key concepts, best practices, and problem-solving within {selected_domain}.
+    ✅ Ensure questions are direct, structured, and relevant to real-world applications.
+    ❌ Do NOT include greetings like 'Let's begin' or 'Welcome to the interview'.
+    ❌ Avoid vague or open-ended statements—each question should be concise and specific.
+"""
+
+
+# UI styles
+st.markdown("""
+<style>
+    /* Base style for all stButton elements */
+    .stButton > button {
+        background-color: #007BFF !important;
+        color: white !important;
+        border-radius: 10px !important;
+        font-weight: bold !important;
+        width: 100% !important;
+        padding: 0.4rem 0.75rem !important;
+        font-size: 0.95rem !important;
+        line-height: 1.5 !important;
+        border: 1px solid transparent !important;
+        transition: background-color 0.2s ease-in-out, border-color 0.2s ease-in-out, box-shadow 0.2s ease-in-out !important;
+        margin-bottom: 8px !important;
+        box-sizing: border-box;
+    }
+    .stButton > button:hover {
+        background-color: #0056b3 !important;
+        color: white !important;
+        border-color: #0056b3 !important;
+    }
+    .stButton > button:focus,
+    .stButton > button:active {
+        background-color: #0056b3 !important;
+        border-color: #004085 !important;
+        box-shadow: 0 0 0 0.2rem rgba(0,123,255,.5) !important;
+        outline: none !important;
+    }
+
+    .timer-text {
+        font-size: 1.3rem;
+        font-weight: 600;
+        color: #00bcd4;
+        animation: pulse 1s infinite;
+    }
+    @keyframes pulse {
+        0% {opacity: 1;}
+        50% {opacity: 0.4;}
+        100% {opacity: 1;}
+    }
+
+    .summary-card {
+        background-color: #f9f9f9;
+        padding: 20px;
+        border-radius: 12px;
+        border: 1px solid #ddd;
+        box-shadow: 0 2px 6px rgba(0, 0, 0, 0.05);
+    }
+    /* More specific selector for the pre text color */
+    div.summary-card > pre {
+        white-space: pre-wrap !important;
+        word-wrap: break-word !important;
+        font-family: inherit !important;
+        font-size: 0.95rem !important;
+        color: #000000 !important;   /* TRYING PURE BLACK with !important */
+        background-color: #ffffff !important; /* Ensure background is white */
+        padding: 15px !important;
+        border-radius: 8px !important;
+        border: 1px solid #e0e0e0 !important;
+        max-height: 400px !important;
+        overflow-y: auto !important;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+# Header
+st.markdown("""
+<div style='text-align: center; margin-top: -30px; padding-top: 10px;'>
+    <h1 style='font-size: 2.8rem; font-weight: 800; color: #003366;'>🎯 Welcome to <span style='color: #007BFF;'>GrillMaster</span></h1>
+    <p style='font-size: 1.1rem; color: #555;'>Your AI-powered mock interview assistant</p>
+</div>
+<hr style='border: 1px solid #e0e0e0; margin: 20px auto;'>
+""", unsafe_allow_html=True)
+
+if not st.session_state["generated_questions"]:
+    st.markdown("""
+    <div style='text-align: center; margin-top: -10px; margin-bottom: 30px;'>
+        <h3 style='font-weight: 700; color: #333;'>🚀 Let's get started!</h3>
+        <p style='font-size: 1rem; color: #666;'>Select your interview domain and input type to begin your practice session.</p>
+    </div>
+    <hr style='border: 1px solid #e0e0e0; margin-top: 0px;'>
+    """, unsafe_allow_html=True)
+
+# Example soft skills questions for HR/Soft Skills domain
+if st.session_state["selected_domain"] == "Soft Skills":
+        hr_questions = [
+    "Tell me about yourself.",
+    "Why should we hire you?",
+    "What are your strengths and weaknesses?",
+    "What is the difference between hard work and smart work?",
+    "Why do you want to work at our company?",
+    "How do you feel about working nights and weekends?",
+    "Can you work under pressure?",
+    "What are your goals?",
+    "Are you willing to relocate or travel?",
+    "What motivates you to do good job?",
+    "What would you want to accomplish within your first 30 days of employment?",
+    "What do you prefer working alone or in collaborative environment?",
+    "Give me an example of your creativity.",
+    "How long would you expect to work for us if hired?",
+    "Are not you overqualified for this position?",
+    "Describe your ideal company, location and job.",
+    "Explain how would you be an asset to this organization?",
+    "What are your interests?",
+    "Would you lie for the company?",
+    "Who has inspired you in your life and why?",
+    "What was the toughest decision you ever had to make?",
+    "Have you considered starting your own business?",
+    "How do you define success and how do you measure up to your own definition?",
+    "Tell me something about our company.",
+    "How much salary do you expect?",
+    "Where do you see yourself five years from now?",
+    "Do you have any questions for me?",
+    "Are you a manager or a leader?",
+    "Imagine that you are not lucky enough to get this job, how will you take it?"
+]
+
+# === Sidebar: Domain and Input Configuration ===
+st.sidebar.subheader("Select Interview Domain:")
+for domain in ["Analytics", "Finance", "Soft Skills"]:
+    if st.sidebar.button(domain):
+        st.session_state["selected_domain"] = domain
+        st.session_state["generated_questions"] = []
+        st.session_state["current_question_index"] = 0
+        st.session_state["answers"] = []
+        st.session_state["evaluation_feedback"] = ""
+        st.session_state["recorded_text"] = ""
+        st.session_state["response_captured"] = False
+        st.session_state["timer_start"] = None
+        st.session_state["show_summary"] = False
+        st.session_state["question_played"] = False
+        st.session_state["recording_complete"] = False
+        st.rerun()
+
+if not st.session_state["selected_domain"]:
+    st.sidebar.info("Please select a domain to continue.")
+    st.stop()
+
+st.sidebar.markdown(f"**Selected Domain:** {st.session_state['selected_domain']}")
+num_qs = st.sidebar.slider("Number of Questions:", 1, 10, 3)
+
+if st.session_state["selected_domain"] == "Soft Skills":
+    if st.sidebar.button("Generate Questions"):
+        st.session_state["generated_questions"] = sample(hr_questions, num_qs)
+        st.session_state["current_question_index"] = 0
+        st.rerun()
+else:
+    section_choice = st.sidebar.radio("Choose Input Type:", ("Resume", "Job Description", "Skills"))
+    difficulty = st.sidebar.selectbox("Select Difficulty Level:", ["Beginner", "Intermediate", "Advanced"])
+    input_text = ""
+
+    if section_choice == "Resume":
+        uploaded_file = st.sidebar.file_uploader("Upload Resume:", type=["pdf", "txt"])
+        if uploaded_file:
+            input_text = extract_pdf_text(uploaded_file)
+
+    elif section_choice == "Job Description":
+        input_text = st.sidebar.text_area("Paste Job Description:")
+
+    elif section_choice == "Skills":
+        # Define available skills for each domain
+        skills = {
+            "Analytics": ["Python", "SQL", "Machine Learning", "Statistics", "Business Analytics"],
+            "Finance": ["Fund Accounting", "AML/KYC", "Derivatives"]
+            }
+        skill_list = skills.get(st.session_state["selected_domain"], [])
+        if skill_list:
+            selected_skill = st.sidebar.selectbox("Select a Skill:", skill_list, key="skill_select")
+            input_text = selected_skill
+            st.sidebar.markdown(f"✅ Selected Skill: **{selected_skill}**")  # Debug display
+
+    if st.sidebar.button("Generate Questions"):
+        if not input_text.strip():
+            st.warning("⚠️ Please provide input based on the selected method.")
+            st.stop()
+
+        prompt = f"Ask {num_qs} direct and core-level {difficulty} interview questions related to {input_text}. Do not include intros or numbering."
+        model = genai.GenerativeModel('gemini-1.5-pro-latest')
+        response = model.generate_content([prompt, input_text])
+        lines = response.text.strip().split("\n")
+        questions = [q.strip("* ") for q in lines if q.strip()]
+        st.session_state["generated_questions"] = questions[:num_qs]
+        st.session_state["current_question_index"] = 0
+        st.session_state["answers"] = []
+        st.session_state["evaluation_feedback"] = ""
+        st.session_state["recorded_text"] = ""
+        st.session_state["response_captured"] = False
+        st.session_state["timer_start"] = None
+        st.session_state["show_summary"] = False
+        st.session_state["question_played"] = False
+        st.session_state["recording_complete"] = False
+        st.rerun()
+
+# === Main QA Interface ===
+if st.session_state["generated_questions"]:
+    idx = st.session_state["current_question_index"]
+    if idx < len(st.session_state["generated_questions"]):
+        question = st.session_state["generated_questions"][idx].lstrip("1234567890. ").strip()
+
+        # Phase 0: Play audio first and wait 5s before countdown
+        if not st.session_state.get("question_played"):
+            st.session_state["question_audio_file"] = asyncio.run(generate_question_audio(question))
+            st.session_state.update({
+                "question_played": True,
+                "question_start_time": time.time(),
+                "record_phase": "audio_playing",
+                "recorded_text": ""
+            })
+
+        st.markdown(f"**Q{idx + 1}:** {question}")
+        st.audio(st.session_state["question_audio_file"], format="audio/mp3")
+
+        now = time.time()
+        elapsed = now - st.session_state.get("question_start_time", 0)
+
+        if st.session_state["record_phase"] == "audio_playing":
+            if elapsed < 5:
+                st.markdown(f"<h4 class='timer-text'>🔊 Playing question audio... Please listen</h4>", unsafe_allow_html=True)
+                time.sleep(1)
+                st.rerun()
+            else:
+                st.session_state["record_phase"] = "waiting_to_start"
+                st.session_state["question_start_time"] = time.time()
+                st.rerun()
+
+        elif st.session_state["record_phase"] == "waiting_to_start":
+            remaining = 10 - int(elapsed)
+            if remaining > 0:
+                st.markdown(f"<h4 class='timer-text'>⏳ {remaining} seconds to click 'Start Recording'...</h4>", unsafe_allow_html=True)
+                if st.button("🎙️ Start Recording"):
+                    st.session_state.update({
+                        "record_phase": "recording",
+                        "timer_start": time.time(),
+                        "recording_started": False
+                    })
+                    st.rerun()
+                time.sleep(1)
+                st.rerun()
+            else:
+                st.markdown("<div style='padding:10px; background:#fff8e1; border-left:5px solid orange;color: #212529;'>⚠️ <strong>No action detected.</strong> Automatically skipping to next question...</div>", unsafe_allow_html=True)
+                st.session_state["answers"].append({"question": question, "response": "[No response]"})
+                st.session_state.update({
+                    "record_phase": "idle",
+                    "question_played": False,
+                    "question_start_time": 0.0,
+                    "current_question_index": idx + 1
+                })
+                if st.session_state["current_question_index"] == len(st.session_state["generated_questions"]):
+                    evaluate_answers()
+                    st.session_state["show_summary"] = True
+                st.rerun()
+
+        elif st.session_state["record_phase"] == "recording":
+            remaining = 15 - int(now - st.session_state.get("timer_start", 0))
+            if remaining > 0:
+                st.markdown(f"<h4 class='timer-text'>🎙️ {remaining} seconds to answer...</h4>", unsafe_allow_html=True)
+                recognizer = sr.Recognizer()
+                with sr.Microphone() as source:
+                    recognizer.adjust_for_ambient_noise(source)
+                    try:
+                        audio = recognizer.listen(source, timeout=1, phrase_time_limit=1)
+                        try:
+                            result = recognizer.recognize_google(audio)
+                            st.session_state.update({
+                                "recorded_text": result,
+                                "record_phase": "listening"
+                            })
+                        except sr.UnknownValueError:
+                            pass
+                    except sr.WaitTimeoutError:
+                        pass
+                time.sleep(1)
+                st.rerun()
+            else:
+                st.markdown("<div style='padding:10px; background:#fff3e0; border-left:5px solid orange;'>⚠️ <strong>No response detected.</strong> Moving to next question...</div>", unsafe_allow_html=True)
+                st.session_state["answers"].append({"question": question, "response": "[No response]"})
+                st.session_state.update({
+                    "record_phase": "idle",
+                    "recording_started": False,
+                    "question_played": False,
+                    "question_start_time": 0.0,
+                    "current_question_index": idx + 1
+                })
+                if st.session_state["current_question_index"] == len(st.session_state["generated_questions"]):
+                    evaluate_answers()
+                    st.session_state["show_summary"] = True
+                st.rerun()
+
+        elif st.session_state["record_phase"] == "listening":
+            st.success("🎧 Listening... You may stop when ready.")
+            if st.button("⏹️ Stop Recording"):
+                st.session_state["answers"].append({"question": question, "response": st.session_state["recorded_text"]})
+                st.session_state.update({
+                    "record_phase": "idle",
+                    "recorded_text": "",
+                    "recording_started": False,
+                    "question_played": False,
+                    "question_start_time": 0.0,
+                    "current_question_index": idx + 1
+                })
+                if st.session_state["current_question_index"] == len(st.session_state["generated_questions"]):
+                    evaluate_answers()
+                    st.session_state["show_summary"] = True
+                st.rerun()
+
+
+# === Summary Display ===
+if st.session_state.get("show_summary", False):
+    # st.balloons()
+    st.subheader("📊 Complete Mock Interview Summary")
+
+    feedback_content = st.session_state.get('evaluation_feedback', "Evaluation not yet available.")
+    if not isinstance(feedback_content, str):
+        feedback_content = str(feedback_content)
+    
+    # Escape HTML special characters from the feedback for security
+    escaped_feedback_for_html = escape(feedback_content)
+    # Replace newlines with <br> for HTML display within markdown
+    formatted_feedback_for_markdown = escaped_feedback_for_html.replace("\n", "<br>")
+
+    st.markdown(f"""
+        <div class='summary-card'>
+            <h4 style="color: #212529;">✅ <strong>Overall Score (Model Evaluation):</strong></h4>{st.session_state.get('percentage_score', 0):.0f}% ({st.session_state.get('overall_score',0)}/10)</h4>
+            <div style='margin:10px 0; position:relative;'>
+                <div style="color: #212529; background-color: #00c851; border-radius:10px; overflow:hidden; height:30px; position:relative;">
+                    <div style="
+                        width:{st.session_state.get('percentage_score', 0)}%;
+                        background:#00c851;
+                        height:100%;
+                        border-radius:10px 0 0 10px;
+                        transition: width 0.4s ease-in-out;
+                    "></div>
+                    <div style="
+                        position:absolute;
+                        top:0;
+                        left:0;
+                        width:100%;
+                        height:100%;
+                        display:flex;
+                        align-items:center;
+                        justify-content:center;
+                        font-weight:bold;
+                        color: black !important; 
+                        font-size: 0.9rem;
+                        user-select:none;
+                    ">
+                        {st.session_state.get('percentage_score', 0):.0f}%
+                    </div>
+                </div>
+            </div>
+            <h4 style="color: #212529;">Detailed Evaluation:</h4>
+            <div style="color: #212529; background-color: #ffffff; padding: 10px; border-radius: 5px; border: 1px solid #eee; margin-top: 5px; white-space: pre-wrap; word-wrap: break-word; max-height: 400px; overflow-y: auto;">
+                {formatted_feedback_for_markdown}
+            </div>
+        </div>
+    """, unsafe_allow_html=True)