Delete app.py

app.py

@@ -1,1114 +0,0 @@
-import os
-import re
-import time
-from dotenv import load_dotenv
-import streamlit as st
-import PyPDF2
-import google.generativeai as genai
-import speech_recognition as sr
-from random import sample
-import random
-from html import escape
-import asyncio
-import edge_tts
-import pandas as pd
-import tempfile
-import traceback
-from streamlit_webrtc import webrtc_streamer, WebRtcMode
-from twilio.rest import Client  
-import logging
-import whisper
-model = whisper.load_model("base")
-
-
-# ✅ 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
-
-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
-    
-########################################///////////////////////////////////////////////////#########################################
-
-# HR_PARAMETERS_CONFIG - Updated based on your latest Excel sheet (input_file_0.png)
-# These are the parameters that can be judged from audio/text responses.
-HR_PARAMETERS_CONFIG = {
-    "Voice Modulation": { # Non-Verbal Cues
-        "weight_original": 5, 
-        "rubric": "1-5 (5=Good pace/tone, conversational; 3=Sounds Scripted/Slight Monotony; 1=Flat tone/Robotic)"
-    },
-    "Confidence": { # Personality
-        "weight_original": 7, 
-        "rubric": "1-5 (5=Bold & Confident throughout; 3=Confused/Nervous in parts; 1=Extremely nervous/Timid)"
-    },
-    "Attitude": { # Personality
-        "weight_original": 3, 
-        "rubric": "1-5 (5=Assertive, Positive, Open; 3=Neutral/Mildly defensive; 1=Aggressive/Pessimistic/Dismissive)"
-    },
-    "Flow & Fluency": { # Articulation
-        "weight_original": 20, 
-        "rubric": "1-5 (5=Excellent Fluency, Spontaneous; 3=Initially struggles, then manages/Takes some time; 1=Many fillers/Pauses/Dead silence)"
-    },
-    "Structured thoughts & Clarity": { # Articulation
-        "weight_original": 10, 
-        "rubric": "1-5 (5=Organized, Crisp, Coherent thoughts, e.g. STAR method; 3=Ideas are okay but clarity/structure could be better; 1=Incoherent/Rambling/Struggles to put thoughts into words)"
-    },
-    "Sentence Formation": { # Language Skills
-        "weight_original": 20, 
-        "rubric": "1-5 (5=Good Clarity, Variety in sentence structure, Good Vocab; 3=Decent communication, might find some words difficult; 1=Talks in fragments/one-liners, Hard to understand)"
-    },
-    "Basics of Grammar + SVA": { # Language Skills (SVA = Subject-Verb Agreement)
-        "weight_original": 10, 
-        "rubric": "1-5 (5=Good Command over Language, Minimal errors; 3=Average communicator, some errors but understandable; 1=Makes a lot of Grammatical Errors impacting clarity)"
-    },
-    "Persuasiveness": { # Rapport Building
-        "weight_original": 3, 
-        "rubric": "1-5 (5=Impactful, Convincing Answers, Connects with interviewer; 3=Average or Common Answers; 1=Lacks Presence of Mind/No connection)"
-    },
-    "Quality of Answers": { # Rapport Building
-        "weight_original": 7, 
-        "rubric": "1-5 (5=Handles questions well, Relevant & Thoughtful Answers, Asks good questions; 3=Very Generic Answers; 1=Vague/Lacks Depth/Shallow/Irrelevant)"
-    }
-}
-
-# Calculate total original weight for normalization
-TOTAL_ORIGINAL_WEIGHT_HR = sum(param_data["weight_original"] for param_data in HR_PARAMETERS_CONFIG.values()) # Should be 85
-
-# Add normalized weights to the config for calculating score out of 100
-for param in HR_PARAMETERS_CONFIG:
-    HR_PARAMETERS_CONFIG[param]["weight_normalized"] = (HR_PARAMETERS_CONFIG[param]["weight_original"] / TOTAL_ORIGINAL_WEIGHT_HR) * 100
-
-
-########################################///////////////////////////////////////////////////#########################################
-# SUmmary of improvement(function)
-
-def generate_improvement_suggestions():
-    model = genai.GenerativeModel('gemini-1.5-pro-latest')
-    difficulty_level = st.session_state.get("difficulty_level_select", "Beginner")
-    level_string = difficulty_level.lower()
-
-    if not st.session_state.get("answers"):
-        st.session_state.improvement_suggestions = "No answers were recorded to generate improvement suggestions."
-        return
-
-    # Prepare the context for the LLM
-    qa_context = []
-    for i, entry in enumerate(st.session_state["answers"]):
-        qa_context.append(
-            f"Question {i+1}: {entry['question']}\n"
-            f"Candidate's Answer {i+1}: {str(entry.get('response', '[No response provided]'))}"
-        )
-    full_qa_context = "\n\n".join(qa_context)
-
-    initial_evaluation_feedback = st.session_state.get("evaluation_feedback", "Initial evaluation not available.")
-
-    # Remove any previous "Total Calculated Score..." line from the initial feedback
-    # to avoid confusing the LLM when it sees it as part of the context.
-    initial_evaluation_lines = initial_evaluation_feedback.splitlines()
-    cleaned_initial_evaluation = "\n".join(
-        line for line in initial_evaluation_lines if not line.strip().startswith("**Total Calculated Score:**")
-    )
-
-
-    improvement_prompt_template = """
-    You are an expert interview coach. You have the following information about a candidate's mock interview:
-    - Candidate's Level: {level_string}
-    - Questions Asked and Candidate's Answers:
-    {full_qa_context}
-    - Initial Evaluation Feedback Provided to Candidate:
-    ---
-    {cleaned_initial_evaluation}
-    ---
-
-    Based on all this information, your task is to provide DETAILED and CONSTRUCTIVE suggestions for each question to help the candidate improve. Be supportive and encouraging.
-
-    For EACH question, please provide:
-    1.  **How to Improve This Answer:** Specific, actionable advice on what the candidate could have added, clarified, or approached differently to make their answer better for their {level_string} level. Focus on 1-2 key improvement points.
-    2.  **Hints for an Ideal Answer:** Briefly mention 2-3 key concepts, terms, or elements that a strong answer (appropriate for their {level_string} level) would typically include. DO NOT provide a full model answer, just hints and pointers.
-
-    Keep the tone positive and focused on learning.
-
-    Structure your response clearly for each question. Example for one question:
-
-    ---
-    **Regarding Question X: "[Original Question Text Here]"**
-
-    *How to Improve This Answer:*
-    [Your specific suggestion 1 for improvement...]
-    [Your specific suggestion 2 for improvement...]
-
-    *Hints for an Ideal Answer (Key Points to Consider):*
-    - Hint 1 or Key concept 1
-    - Hint 2 or Key concept 2
-    - Hint 3 or Key element 3 (optional)
-    ---
-    (Repeat this structure for all questions)
-    """
-
-    formatted_improvement_prompt = improvement_prompt_template.format(
-        level_string=level_string,
-        full_qa_context=full_qa_context,
-        cleaned_initial_evaluation=cleaned_initial_evaluation
-    )
-
-    try:
-        st.info("🤖 Generating detailed improvement suggestions... Please wait.")
-        response = model.generate_content(formatted_improvement_prompt)
-        st.session_state.improvement_suggestions = response.text.strip()
-        st.session_state.improvement_suggestions_generated = True
-        st.success("Detailed suggestions generated!")
-    except Exception as e:
-        st.error(f"Error generating improvement suggestions: {e}")
-        st.session_state.improvement_suggestions = f"Could not generate suggestions due to an error: {e}"
-        st.session_state.improvement_suggestions_generated = False
-
-########################################///////////////////////////////////////////////////#########################################
-
-# Evaluate candidate answers - YOUR FUNCTION
-
-
-
-def evaluate_answers():
-    model = genai.GenerativeModel('gemini-1.5-pro-latest')
-    # difficulty_level_select is the key for the difficulty selectbox in your sidebar
-    difficulty_level = st.session_state.get("difficulty_level_select", "Beginner") 
-    level_string = difficulty_level.lower()
-    num_answered_questions = len(st.session_state.get("answers", []))
-
-    # Reset improvement suggestions flag when re-evaluating
-    st.session_state.improvement_suggestions_generated = False
-    st.session_state.improvement_suggestions = ""
-
-    meaningful_answers_exist = False
-    if st.session_state.get("answers"):
-        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]", "[no action - timed out before recording]",
-                "[no speech detected in recording time]", "[no speech recorded - time up]",
-                "[recording stopped manually, possibly empty]",
-                "[no action - did not start recording]",
-                "[no speech detected in recording phase]" 
-            ]
-            if response_text not in no_response_placeholders:
-                meaningful_answers_exist = True
-                break
-    
-    if not meaningful_answers_exist:
-        no_answer_feedback_qualitative = "No meaningful answers were provided for evaluation.\n\n"
-        if st.session_state.selected_domain == "Soft Skills":
-            hr_params_na = "\n".join([f"- {param}: 0/5" for param in HR_PARAMETERS_CONFIG.keys()])
-            no_answer_feedback = (
-                 "No meaningful answers were provided for evaluation.\n\n"
-                f"**Parameter Scores (1-5):**\n{hr_params_na}\n\n"
-                "**Overall Qualitative Feedback:**\nCandidate did not provide responses to evaluate soft skills."
-            )
-            st.session_state["hr_parameter_scores_dict"] = {param: 0.0 for param in HR_PARAMETERS_CONFIG.keys()} # Store zeroed scores
-        else: # Non-HR domains
-            no_answer_feedback = (
-                "No meaningful answers were provided.\n"
-                "**Total Calculated Score:** 0.0 / 0.0 (0.0%)\n\n" # Placeholder for non-HR if no answers
-                "**Overall Evaluation Summary:** N/A"
-            )
-        st.session_state["evaluation_feedback"] = no_answer_feedback
-        st.session_state["overall_score"] = 0.0
-        st.session_state["percentage_score"] = 0.0
-        return
-
-    # --- BRANCHING FOR HR (SOFT SKILLS) VS OTHER DOMAINS ---
-    if st.session_state.selected_domain == "Soft Skills":
-        hr_prompt_parameter_list = ""
-        for param, config in HR_PARAMETERS_CONFIG.items():
-            hr_prompt_parameter_list += f"- **{param}:** {config['rubric']}\n"
-        
-        hr_prompt_template = f"""
-        You are an experienced HR interview evaluator assessing a candidate's soft skills based on their answers to interview questions.
-        The candidate's performance across ALL answers should inform your scores for the following parameters.
-
-        **Parameters to Score (Assign a score from 1 to 5 for each):**
-        {hr_prompt_parameter_list}
-
-        After providing a score (1-5) for each of the above parameters, also write an **Overall Qualitative Feedback** section. 
-        This section should summarize the candidate's general soft skill strengths and areas for improvement, based on their communication, engagement, and professionalism throughout the interview.
-
-        **REQUIRED OUTPUT FORMAT (Strictly Adhere):**
-
-        **Parameter Scores (1-5):**
-        Voice Modulation: [score]
-        Confidence: [score]
-        Attitude: [score]
-        Flow & Fluency: [score]
-        Structured thoughts & Clarity: [score]
-        Sentence Formation: [score]
-        Basics of Grammar + SVA: [score]
-        Persuasiveness: [score]
-        Quality of Answers: [score]
-
-        **Overall Qualitative Feedback:**
-        [Your holistic qualitative feedback here. Be encouraging and constructive.]
-        """
-        candidate_responses_formatted_hr = "\n\n".join(
-            [f"Question {i+1}: {entry['question']}\nCandidate's Answer {i+1}: {str(entry.get('response', '[No response provided]'))}" 
-             for i, entry in enumerate(st.session_state["answers"])]
-        )
-        full_prompt_for_hr_evaluation = f"{hr_prompt_template}\n\nCandidate's Interview Answers (Consider all of these for holistic parameter scoring):\n{candidate_responses_formatted_hr}"
-
-        try:
-            response_content = model.generate_content(full_prompt_for_hr_evaluation)
-            full_llm_response_text = response_content.text.strip()
-            
-            print("--- LLM Output for HR Score Extraction ---")
-            print(full_llm_response_text)
-            print("-----------------------------------------")
-
-            hr_parameter_scores_parsed_dict = {} # To store parsed scores for each HR param
-            total_weighted_score_percentage = 0.0
-
-            for param_name_config, config_data in HR_PARAMETERS_CONFIG.items():
-                # Using a more specific regex, anchored to the start of a line (after optional list marker)
-                # re.escape ensures special characters in param_name_config are treated literally.
-                param_score_pattern = re.compile(
-                    r"^\s*(?:[\*\-]\s*)?" + re.escape(param_name_config.split('(')[0].strip()) + r"\s*[:\-–—]?\s*(\d+(?:\.\d+)?)\b", 
-                    re.IGNORECASE | re.MULTILINE
-                ) # \b for word boundary after score
-                
-                match = param_score_pattern.search(full_llm_response_text)
-                param_score = 1.0 # Default to 1 (lowest actual score) if not found or unparseable
-                if match:
-                    try:
-                        score_text = match.group(1)
-                        param_score = float(score_text)
-                        param_score = max(1.0, min(5.0, param_score)) # Clamp score strictly 1-5 for HR
-                        print(f"HR Param '{param_name_config}' - Matched text: '{score_text}', Parsed: {param_score}")
-                    except ValueError:
-                        print(f"HR Param '{param_name_config}' - ValueError parsing score from '{score_text}' in match '{match.group(0)}'. Defaulting to 1.0.")
-                        param_score = 1.0 
-                else:
-                    print(f"HR Param '{param_name_config}' - Score pattern not found. Defaulting to 1.0 for this param.")
-                
-                hr_parameter_scores_parsed_dict[param_name_config] = param_score
-                total_weighted_score_percentage += (param_score / 5.0) * config_data["weight_normalized"] # Use normalized weight
-            
-            st.session_state["hr_parameter_scores_dict"] = hr_parameter_scores_parsed_dict # Store for table display
-            st.session_state["overall_score"] = round(total_weighted_score_percentage, 1) 
-            st.session_state["percentage_score"] = round(total_weighted_score_percentage, 1)
-            
-            # Construct the feedback to be displayed: Parsed scores + Qualitative from LLM
-            # The full_llm_response_text might still be useful if qualitative parsing is tricky
-            parsed_scores_display_text = "**Parsed Parameter Scores (1-5 based on AI Evaluation):**\n"
-            for p_name, p_score in hr_parameter_scores_parsed_dict.items():
-                parsed_scores_display_text += f"- {p_name}: {p_score:.1f}/5\n"
-            
-            qualitative_feedback_hr_extract = "Overall qualitative feedback section not clearly identified in AI response."
-            qualitative_match_hr = re.search(r"\*\*Overall Qualitative Feedback:\*\*(.*)", full_llm_response_text, re.DOTALL | re.IGNORECASE)
-            if qualitative_match_hr:
-                qualitative_feedback_hr_extract = qualitative_match_hr.group(1).strip()
-            
-            st.session_state["evaluation_feedback"] = f"{parsed_scores_display_text}\n\n**Overall Qualitative Feedback from AI:**\n{qualitative_feedback_hr_extract}"
-
-        except Exception as e_hr_eval:
-            st.error(f"Error during HR/Soft Skills evaluation processing: {e_hr_eval}")
-            print(f"HR EVALUATION PROCESSING TRACEBACK:\n{traceback.format_exc()}")
-            st.session_state["evaluation_feedback"] = f"Could not process HR skills evaluation: {e_hr_eval}"
-            st.session_state["overall_score"] = 0.0
-            st.session_state["percentage_score"] = 0.0
-
-    else: # --- NON-HR (Analytics, Finance) Evaluation Logic ---
-        base_assessment_criteria_qualitative_non_hr = """
-        For the OVERALL qualitative summary, assess responses based on:
-        - Conceptual Understanding (effort and relevance more than perfect accuracy for the level)
-        - 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?)
-        """
-        per_question_scoring_guidelines_non_hr = f"""
-        For EACH question and its answer, provide a score from 0 to 5 points.
-        The candidate is at a {level_string} level.
-        Consider the following when assigning the per-question score:
-        - Effort and relevance of the answer.
-        - Clarity of thought for the candidate's level.
-        - Basic logical structure.
-        - Use of examples, if any were given and appropriate.
-        """
-        if level_string == "beginner":
-            level_specific_instructions_non_hr = """
-            You are an **extremely understanding, encouraging, and supportive** interview evaluator for a **BEGINNER/FRESHER**. Your primary goal is to **build confidence**.
-            **Scoring Guidelines for Beginners (0-5 points per question):**
-            - **5 points:** Generally correct and relevant, even if brief. Shows clear effort and basic understanding.
-            - **4 points:** Good attempt, relevant, shows some understanding or key terms (e.g., one/two relevant words).
-            - **3 points:** Tries, somewhat related, or acknowledges question with a vague thought.
-            - **1-2 points:** Minimal effort, mostly irrelevant, but an attempt beyond silence.
-            - **0 points:** Completely irrelevant, no attempt, or placeholder.
-            Provide VERY positive feedback.
-            """
-        elif level_string == "intermediate":
-            level_specific_instructions_non_hr = """Supportive evaluator for **INTERMEDIATE**. Scoring (0-5): 5=Correct/Clear; 3-4=Mostly correct; 1-2=Partial/Gaps; 0=Incorrect."""
-        else: # Advanced
-            level_specific_instructions_non_hr = """Discerning evaluator for **ADVANCED**. Scoring (0-5): 5=Accurate/Comprehensive; 3-4=Correct lacks nuance; 1-2=Inaccurate; 0=Fundamentally incorrect."""
-        
-        evaluation_prompt_template_non_hr = f"""
-        {level_specific_instructions_non_hr} 
-        {per_question_scoring_guidelines_non_hr}
-        {base_assessment_criteria_qualitative_non_hr}
-        **YOUR RESPONSE MUST STRICTLY FOLLOW THIS FORMAT. PROVIDE SCORES FOR EACH QUESTION.**
-        Output format:
-
-        **Per-Question Scores:**
-        Question 1 Score: [Score for Q1 out of 5]
-        ... (repeat for all {num_answered_questions} questions provided)
-
-        **Overall Evaluation Summary:**
-        - Concept Understanding: [Overall qualitative feedback here]
-        - Communication: [Overall qualitative feedback here]
-        - Depth of Explanation: [Overall qualitative feedback here]
-        - Examples: [Overall qualitative feedback here]
-        - Logical Flow: [Overall qualitative feedback here]
-        [Any additional overall encouraging remarks can optionally follow here]
-        """
-        candidate_responses_formatted_non_hr = "\n\n".join(
-            [f"Question {i+1}: {entry['question']}\nAnswer {i+1}: {str(entry.get('response', '[No response provided]'))}" for i, entry in enumerate(st.session_state["answers"])]
-        )
-        full_prompt_for_non_hr_evaluation = f"{evaluation_prompt_template_non_hr}\n\nCandidate Responses:\n{candidate_responses_formatted_non_hr}"
-
-        try:
-            response_content_non_hr = model.generate_content(full_prompt_for_non_hr_evaluation)
-            full_llm_response_text_non_hr = response_content_non_hr.text.strip()
-            raw_llm_feedback_non_hr = full_llm_response_text_non_hr
-            
-            print("--- LLM Output for Non-HR Score Extraction ---"); print(full_llm_response_text_non_hr); print("---")
-            
-            total_score_non_hr = 0.0; parsed_scores_count_non_hr = 0; per_question_scores_list_non_hr = []
-            score_line_pattern_non_hr = re.compile(r"Question\s*(\d+)\s*Score:\s*(\d+(?:\.\d+)?)(?:\s*/\s*5)?", re.IGNORECASE)
-            text_to_search_non_hr = full_llm_response_text_non_hr
-            scores_block_match_non_hr = re.search(r"(?i)\*\*Per-Question Scores:\*\*(.*?)(?=\*\*Overall Evaluation Summary:\*\*|\Z)", text_to_search_non_hr, re.DOTALL)
-            
-            if scores_block_match_non_hr: 
-                text_to_search_non_hr = scores_block_match_non_hr.group(1).strip()
-                print(f"Non-HR: Found 'Per-Question Scores' block:\n{text_to_search_non_hr}")
-            else:
-                print("Non-HR: No dedicated 'Per-Question Scores' block found; searching entire response.")
-
-
-            for match_non_hr in score_line_pattern_non_hr.finditer(text_to_search_non_hr):
-                q_num_text_non_hr, score_val_text_non_hr = match_non_hr.group(1), match_non_hr.group(2)
-                try:
-                    score_non_hr = float(score_val_text_non_hr)
-                    score_non_hr = max(0.0, min(5.0, score_non_hr))
-                    total_score_non_hr += score_non_hr
-                    parsed_scores_count_non_hr += 1
-                    per_question_scores_list_non_hr.append(f"Question {q_num_text_non_hr}: {score_non_hr:.1f}/5")
-                    print(f"Non-HR Matched Q{q_num_text_non_hr} Score: {score_non_hr}")
-                except ValueError:
-                    print(f"Non-HR Warning: Could not parse score '{score_val_text_non_hr}' from: '{match_non_hr.group(0)}'")
-            
-            if parsed_scores_count_non_hr != num_answered_questions and meaningful_answers_exist: 
-                 st.warning(f"Non-HR Score Count Mismatch: Parsed {parsed_scores_count_non_hr} scores, expected {num_answered_questions}.")
-                 print(f"Non-HR Score Count Mismatch: Expected {num_answered_questions}, got {parsed_scores_count_non_hr}")
-
-            if parsed_scores_count_non_hr == 0 and meaningful_answers_exist: 
-                 st.warning("CRITICAL (Non-HR): No per-question scores parsed from LLM response. Total score set to 0.")
-                 print("CRITICAL (Non-HR): No per-question scores parsed.")
-                 total_score_non_hr = 0.0
-
-            max_score_non_hr = num_answered_questions * 5.0
-            st.session_state["overall_score"] = total_score_non_hr
-            st.session_state["percentage_score"] = (total_score_non_hr / max_score_non_hr) * 100.0 if max_score_non_hr > 0 else 0.0
-            
-            final_feedback_non_hr = f"**Total Calculated Score:** {st.session_state['overall_score']:.1f} / {max_score_non_hr:.1f}  ({st.session_state['percentage_score']:.1f}%)\n\n"
-            if per_question_scores_list_non_hr: 
-                final_feedback_non_hr += "**Parsed Per-Question Scores:**\n" + "\n".join(per_question_scores_list_non_hr) + "\n\n"
-            
-            qual_summary_match_non_hr = re.search(r"\*\*Overall Evaluation Summary:\*\*(.*)", raw_llm_feedback_non_hr, re.DOTALL | re.IGNORECASE)
-            if qual_summary_match_non_hr: 
-                final_feedback_non_hr += "**Overall Qualitative Summary (from AI):**\n" + qual_summary_match_non_hr.group(1).strip()
-            else: 
-                final_feedback_non_hr += "\n---\n**Full AI Response (for context if summary parsing failed):**\n" + raw_llm_feedback_non_hr
-            st.session_state["evaluation_feedback"] = final_feedback_non_hr.strip()
-
-        except Exception as e_non_hr_eval:
-            st.error(f"Error during Non-HR evaluation processing: {e_non_hr_eval}")
-            print(f"NON-HR EVALUATION PROCESSING TRACEBACK:\n{traceback.format_exc()}")
-            st.session_state["evaluation_feedback"] = f"Could not process Non-HR evaluation: {e_non_hr_eval}"
-            st.session_state["overall_score"] = 0.0
-            st.session_state["percentage_score"] = 0.0
-########################################///////////////////////////////////////////////////#########################################
-# --- 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.clear()  # 🔁 Reset entire session state
-        st.session_state["selected_domain"] = domain
-        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":
-        input_text = ""
-
-        if st.session_state["selected_domain"] == "Finance":
-            finance_levels = ["Level-1", "Level-2", "Level-3"]
-            selected_level = st.sidebar.selectbox("Select a Finance Level:", finance_levels, key="finance_level_select")
-
-            difficulty = st.session_state.get("difficulty", "Beginner")
-
-            if selected_level != "Level-1":
-                st.sidebar.warning(f"🚧 {selected_level} content is still under development. Please select Level-1 to continue.")
-                st.stop()
-
-            # Map difficulty level to column in Excel
-            column_map = {
-                "Beginner": "MODULE 1-EASY",
-                "Intermediate": "MODULE 1-MEDIUM",
-                "Advanced": "MODULE 1-DIFFICULT"
-            }
-
-            selected_column = column_map[difficulty]
-
-            # Load Excel and questions
-            excel_path = os.path.join("data", "CIBOP Mock Questions.xlsx")
-            try:
-                df = pd.read_excel(excel_path, engine="openpyxl")
-                questions_from_excel = df[selected_column].dropna().astype(str).tolist()
-                input_text = selected_column  # Optional, for tracking
-            except Exception as e:
-                st.sidebar.error(f"❌ Error reading Excel file: {e}")
-                st.stop()
-
-            st.sidebar.success(f"✅ Loaded {difficulty}-level questions from {selected_level}")
-
-        else:
-            # For Analytics or any other domain
-            skills = {
-                "Analytics": ["Python", "SQL", "Machine Learning", "Statistics", "Business Analytics"]
-            }
-            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}**")
-
-
-    if st.sidebar.button("Generate Questions"):
-        if not input_text.strip():
-            st.warning("⚠️ Please provide input based on the selected method.")
-            st.stop()
-
-        if st.session_state["selected_domain"] == "Finance" and section_choice == "Skills":
-            st.session_state["generated_questions"] = sample(questions_from_excel, min(num_qs, len(questions_from_excel)))
-        else:
-            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()
-
-def get_ice_servers():
-    """Use Twilio's TURN server because Streamlit Community Cloud has changed
-    its infrastructure and WebRTC connection cannot be established without TURN server now.  # noqa: E501
-    We considered Open Relay Project (https://www.metered.ca/tools/openrelay/) too,
-    but it is not stable and hardly works as some people reported like https://github.com/aiortc/aiortc/issues/832#issuecomment-1482420656  # noqa: E501
-    See https://github.com/whitphx/streamlit-webrtc/issues/1213
-    """
-
-    # Ref: https://www.twilio.com/docs/stun-turn/api
-    try:
-        account_sid = os.environ["TWILIO_ACCOUNT_SID"]
-        auth_token = os.environ["TWILIO_AUTH_TOKEN"]
-    except KeyError:
-        logger.warning(
-            "Twilio credentials are not set. Fallback to a free STUN server from Google."  # noqa: E501
-        )
-        return [{"urls": ["stun:stun.l.google.com:19302"]}]
-
-    client = Client(account_sid, auth_token)
-
-    token = client.tokens.create()
-
-    return token.ice_servers
-
-
-
-# === 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)
-                
-                audio_value = st.audio_input("🎤 Tap to record — then stop when done", key=f"audio_{idx}")
-                if audio_value and "response_file" not in st.session_state:
-                    wav_path = f"response_{idx}.wav"
-                    with open(wav_path, "wb") as f:
-                        f.write(audio_value.getbuffer())
-                    #st.audio(wav_path, format="audio/wav")
-                    st.session_state["response_file"] = wav_path
-                    st.session_state["record_phase"] = "listening"
-                    st.success("✅ Audio uploaded. You may now confirm your answer.")
-                    st.audio(wav_path, format="audio/wav")
-
-                    if st.button("⏹️ Confirm & Next"):
-                        try:
-                            with st.spinner("🧠 Transcribing your answer..."):
-                                result = model.transcribe(st.session_state["response_file"])
-                                transcript = result["text"].strip()
-                                if not transcript:
-                                    transcript = "[Transcription failed or empty]"
-
-                        except Exception as e:
-                                st.error(f"❌ Transcription error: {e}")
-                                transcript = "[Transcription error]"
-                            
-                        st.session_state["answers"].append({
-                        "question": question,
-                        "response_file": st.session_state["response_file"],
-                        "response_text": transcript
-                        })
-
-                    if st.session_state["current_question_index"] == len(st.session_state["generated_questions"]):
-                        evaluate_answers()
-                        st.session_state["show_summary"] = True
-                    st.rerun()
-
-                
-
-                if elapsed > 15 and "response_file" not in st.session_state:
-                    st.warning("⚠️ No audio captured. Moving to next question.")
-                    st.session_state["answers"].append({
-                    "question": question,
-                    "response": "[No response]"
-                    })
-
-                    st.session_state.update({
-                    "record_phase": "idle",
-                    "question_played": False,
-                    "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()
-                        
-                
-                                
-            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("🎧 Review your recorded response below:")
-            st.audio(st.session_state["response_file"], format="audio/wav")
-            
-            if st.button("⏹️ Confirm & Next"):
-                st.session_state["answers"].append({
-                    "question": question,
-                    "response_file": st.session_state["response_file"]
-                })
-
-                st.session_state.update({
-                    "record_phase": "idle",
-                    "recording_started": False,
-                    "question_played": False,
-                    "question_start_time": 0.0,
-                    "current_question_index": idx + 1,
-                    "response_file": None,
-                    "audio_waiting": True
-                })
-                
-                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 ===
- 
-# === Summary Display ===
-if st.session_state.get("show_summary", False):
-    st.subheader("📊 Complete Mock Interview Summary")
-
-    # Fetch values from session state, providing defaults
-    feedback_content_for_display = st.session_state.get('evaluation_feedback', "Evaluation details not available.")
-    if not isinstance(feedback_content_for_display, str):
-        feedback_content_for_display = str(feedback_content_for_display)
-    
-    # Max score basis is the number of questions that were *generated* for the session
-    num_qs_in_session = len(st.session_state.get("generated_questions", [])) 
-    if num_qs_in_session == 0 and st.session_state.get("answers"): # Fallback if no generated_questions but answers exist
-        num_qs_in_session = len(st.session_state.answers)
-
-    max_score_possible_for_session = num_qs_in_session * 5.0
-    current_percentage_score = st.session_state.get('percentage_score', 0.0)
-    current_overall_score = st.session_state.get('overall_score', 0.0)
-
-    # Display the calculated score and percentage bar first in a card
-    st.markdown(f"""
-        <div class='summary-card' style="margin-bottom: 20px;">
-            <h4 style="color: #212529;">✅ <strong>Overall Score:</strong> {current_overall_score:.1f} / {max_score_possible_for_session:.1f} 
-                ({current_percentage_score:.1f}%)
-            </h4>
-            <div style='margin:10px 0; position:relative;'>
-                <div style="background:#eee; border-radius:10px; overflow:hidden; height:30px; position:relative;">
-                    <div style="
-                        width:{current_percentage_score}%; 
-                        background:#00c851; /* Green for progress */
-                        height:100%;
-                        border-radius:10px 0 0 10px; /* Keep left radius for progress */
-                        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; /* Ensure text is visible on green/grey */
-                        font-size: 0.9rem;
-                        user-select:none; /* Prevent text selection */
-                    ">
-                        {current_percentage_score:.1f}% 
-                    </div>
-                </div>
-            </div>
-        </div>
-    """, unsafe_allow_html=True)
-
-    # Display the detailed evaluation feedback text in a separate section
-    st.markdown("---") 
-    st.markdown("<h4 style='color: #212529;'>Detailed Evaluation & Feedback from AI:</h4>", unsafe_allow_html=True)
-    
-    # Use a styled div for the feedback text block to ensure good readability
-    # Replace newlines with <br> for proper HTML multiline display
-    html_formatted_feedback = feedback_content_for_display.replace('\n', '<br>')
-    st.markdown(f"""
-    <div style="background-color: #ffffff; color: #212529; padding: 15px; border-radius: 8px; border: 1px solid #e0e0e0; margin-top:10px; max-height: 500px; overflow-y: auto; white-space: normal; word-wrap: break-word;">
-    {html_formatted_feedback}
-    </div>
-    """, unsafe_allow_html=True)
-    
-    st.markdown("---") # Separator
-
-    # Buttons for suggestions, download, practice
-    cols_summary_buttons = st.columns([1, 1, 1]) # 3 columns for the buttons
-
-    with cols_summary_buttons[0]:
-        if st.button("💡 Get Improvement Suggestions", key="get_suggestions_btn_final", use_container_width=True):
-            # Regenerate suggestions if not present or explicitly requested again
-            generate_improvement_suggestions() # This function should handle st.info/st.success
-            st.rerun() # Rerun to show the expander or updated suggestions
-
-    # Helper function to prepare summary text for download
-    def prepare_summary_for_download():
-        download_text = f"# GrillMaster Mock Interview Summary\n\n"
-        download_text += f"**Selected Domain:** {st.session_state.get('selected_domain', 'N/A')}\n"
-        dl_difficulty = st.session_state.get('difficulty_level_select', 'N/A')
-        download_text += f"**Difficulty Level:** {dl_difficulty}\n"
-        
-        num_q_for_max_score = len(st.session_state.get("generated_questions", st.session_state.get("answers",[])))
-        max_s_for_dl = num_q_for_max_score * 5.0
-        
-        download_text += f"**Calculated Overall Score:** {st.session_state.get('overall_score', 0.0):.1f} / {max_s_for_dl:.1f} ({st.session_state.get('percentage_score', 0.0):.1f}%)\n\n"
-        
-        download_text += "## Questions & Candidate's Answers:\n"
-        num_answers_actually_given = len(st.session_state.get("answers", []))
-        for i in range(num_q_for_max_score):
-            question_text_dl = st.session_state.generated_questions[i] if i < len(st.session_state.generated_questions) else "Question text not found"
-            answer_text_dl = "[No answer recorded]"
-            if i < num_answers_actually_given:
-                answer_text_dl = str(st.session_state.answers[i].get('response', '[No response provided]'))
-            
-            download_text += f"**Question {i+1}:** {question_text_dl}\n"
-            download_text += f"**Your Answer {i+1}:** {answer_text_dl}\n\n"
-        
-        download_text += "\n## AI Evaluation Details (Includes Parsed Scores and Qualitative Feedback):\n"
-        # st.session_state.evaluation_feedback is now already pre-formatted
-        download_text += st.session_state.get('evaluation_feedback', "No AI evaluation available.")
-        download_text += "\n\n"
-
-        if st.session_state.get("improvement_suggestions_generated", False) and st.session_state.get("improvement_suggestions"):
-            download_text += "\n## Detailed Improvement Suggestions from AI:\n"
-            download_text += st.session_state.get('improvement_suggestions', "No improvement suggestions were generated.")
-        
-        return download_text.encode('utf-8')
-
-    with cols_summary_buttons[1]:
-        summary_bytes_dl_final = prepare_summary_for_download()
-        st.download_button(
-            label="💾 Download Full Summary", 
-            data=summary_bytes_dl_final,
-            file_name=f"GrillMaster_Summary_{st.session_state.get('selected_domain','General')}_{time.strftime('%Y%m%d_%H%M')}.md",
-            mime="text/markdown", 
-            key="download_summary_final_btn", 
-            use_container_width=True
-        )
-    
-    
-
-    # Expander for detailed suggestions, shown if generated
-    if st.session_state.get("improvement_suggestions_generated", False) and st.session_state.get("improvement_suggestions"):
-        with st.expander("🔍 View Detailed Improvement Suggestions", expanded=True): # Default to expanded once generated
-            st.markdown(st.session_state.improvement_suggestions, unsafe_allow_html=True) # LLM might use markdown
-
-    # Conditional button for low scores
-    if current_percentage_score < 50.0:
-        st.warning(f"Your score is {current_percentage_score:.1f}%. Keep practicing! You can also reset all settings to try a new domain or difficulty.")
-        if st.button("🔁 Practice Again & Reset All Settings", key="practice_full_reset_final_btn", use_container_width=True):
-            # Clear all session state keys and re-initialize to defaults
-            keys_to_fully_clear = list(st.session_state.keys())
-            for key_to_del_full in keys_to_fully_clear:
-                del st.session_state[key_to_del_full]
-