app.py

from flask import Flask, request, send_file, abort, jsonify, url_for, render_template, Response
from flask_cors import CORS
import pandas as pd
from sentence_transformers import SentenceTransformer, util
import torch
from dataclasses import dataclass
from typing import List, Dict, Tuple, Optional, Any
from collections import deque
import os
import logging
import atexit
from threading import Thread, Lock
import time
from datetime import datetime
from uuid import uuid4 as generate_uuid
import csv as csv_lib
import functools
import json
import re
import subprocess
import sys
import sqlite3

from dotenv import load_dotenv

# Load environment variables from .env file AT THE VERY TOP
load_dotenv()

# MODIFIED: Import from the new refactored modules
from llm_fallback import get_groq_fallback_response
from rag_system import initialize_and_get_rag_system
from rag_components import KnowledgeRAG
from utils import download_and_unzip_gdrive_file, download_gdrive_file # MODIFIED: Import the new utility
from config import (
    RAG_SOURCES_DIR,
    RAG_STORAGE_PARENT_DIR,
    RAG_CHUNKED_SOURCES_FILENAME,
    GDRIVE_INDEX_ENABLED,
    GDRIVE_INDEX_ID_OR_URL,
    GDRIVE_USERS_CSV_ENABLED,      # NEW
    GDRIVE_USERS_CSV_ID_OR_URL     # NEW
)

# Setup logging (remains global for the app)
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    handlers=[
        logging.FileHandler("app_hybrid_rag.log"),
        logging.StreamHandler()
    ]
)
logger = logging.getLogger(__name__) # Main app logger

# --- Application Constants and Configuration ---
# MODIFIED: These are now fallbacks if users.csv is not found
ADMIN_USERNAME = os.getenv('FLASK_ADMIN_USERNAME', 'admin')
ADMIN_PASSWORD = os.getenv('FLASK_ADMIN_PASSWORD', 'fleetblox')
REPORT_PASSWORD = os.getenv('FLASK_REPORT_PASSWORD', 'e$$!@2213r423er31')
FLASK_APP_HOST = os.getenv("FLASK_HOST", "0.0.0.0")
FLASK_APP_PORT = int(os.getenv("FLASK_PORT", "5002"))
FLASK_DEBUG_MODE = os.getenv("FLASK_DEBUG", "False").lower() == "true"
_APP_BASE_DIR = os.path.dirname(os.path.abspath(__file__))
TEXT_EXTRACTIONS_DIR = os.path.join(_APP_BASE_DIR, 'text_extractions')
RELATED_QUESTIONS_TO_SHOW = 10
QUESTIONS_TO_SEND_TO_GROQ_QA = 3
DB_QA_CONFIDENCE = 85
GENERAL_QA_CONFIDENCE = 85
HIGH_CONFIDENCE_THRESHOLD = 90
CHAT_HISTORY_TO_SEND = 5
CHAT_LOG_FILE = os.path.join(_APP_BASE_DIR, 'chat_history.csv')

# MODIFIED: Global variable for user data
user_df = None

logger.info(f"APP LAUNCH: Admin username loaded as '{ADMIN_USERNAME}' (fallback)")

# --- NEW: User loading from users.csv ---
def load_users_from_csv():
    global user_df
    # CHANGED: users.csv should be in assets folder
    assets_folder = os.path.join(_APP_BASE_DIR, 'assets')
    os.makedirs(assets_folder, exist_ok=True)  # Ensure assets folder exists
    users_csv_path = os.path.join(assets_folder, 'users.csv')
    
    try:
        if os.path.exists(users_csv_path):
            user_df = pd.read_csv(users_csv_path)
            # Ensure required columns are present
            required_cols = ['sl', 'name', 'email', 'password', 'role']
            if not all(col in user_df.columns for col in required_cols):
                 logger.error(f"users.csv is missing one of the required columns: {required_cols}")
                 user_df = None
                 return
            user_df['email'] = user_df['email'].str.lower().str.strip()
            logger.info(f"Successfully loaded {len(user_df)} users from {users_csv_path}")
        else:
            logger.warning(f"users.csv not found at '{users_csv_path}'. Admin auth will use fallback .env credentials.")
            user_df = None
    except Exception as e:
        logger.error(f"Failed to load or process users.csv: {e}", exc_info=True)
        user_df = None

# --- inside the ChatHistoryManager class ---

    def clear_history(self, session_id: str):
        """
        Deletes the entire chat history for a given session_id.
        """
        with self.lock:
            try:
                with self._get_connection() as conn:
                    cursor = conn.cursor()
                    cursor.execute("DELETE FROM chat_histories WHERE session_id = ?", (session_id,))
                    conn.commit()
                    logger.info(f"Successfully cleared history for session: {session_id}")
            except Exception as e:
                logger.error(f"Error clearing history for session {session_id}: {e}", exc_info=True)
                
# --- NEW: Persistent Chat History Management using SQLite ---
class ChatHistoryManager:
    def __init__(self, db_path):
        self.db_path = db_path
        self.lock = Lock()
        self._create_table()
        logger.info(f"SQLite chat history manager initialized at: {self.db_path}")

    def _get_connection(self):
        # The timeout parameter is crucial to prevent "database is locked" errors under load.
        conn = sqlite3.connect(self.db_path, timeout=10)
        return conn

    def _create_table(self):
        with self.lock:
            with self._get_connection() as conn:
                cursor = conn.cursor()
                # Use TEXT to store the history as a JSON string
                cursor.execute("""
                    CREATE TABLE IF NOT EXISTS chat_histories (
                        session_id TEXT PRIMARY KEY,
                        history TEXT NOT NULL
                    )
                """)
                conn.commit()

    def get_history(self, session_id: str, limit: int = 10) -> list:
        """
        Retrieves history from the DB and returns it as a list of dictionaries.
        """
        try:
            with self._get_connection() as conn:
                cursor = conn.cursor()
                cursor.execute("SELECT history FROM chat_histories WHERE session_id = ?", (session_id,))
                row = cursor.fetchone()
                if row:
                    # Deserialize the JSON string back into a Python list
                    history_list = json.loads(row[0])
                    # Return the last 'limit' * 2 items (user + assistant messages)
                    return history_list[-(limit * 2):]
                else:
                    return []
        except Exception as e:
            logger.error(f"Error fetching history for session {session_id}: {e}", exc_info=True)
            return []

    def update_history(self, session_id: str, query: str, answer: str):
        with self.lock:
            try:
                with self._get_connection() as conn:
                    cursor = conn.cursor()
                    # First, get the current history
                    cursor.execute("SELECT history FROM chat_histories WHERE session_id = ?", (session_id,))
                    row = cursor.fetchone()
                    
                    history = json.loads(row[0]) if row else []
                    
                    # Append the new conversation turn
                    history.append({'role': 'user', 'content': query})
                    history.append({'role': 'assistant', 'content': answer})

                    # Serialize the updated list back to a JSON string
                    updated_history_json = json.dumps(history)

                    # Use INSERT OR REPLACE to either create a new row or update the existing one
                    cursor.execute("""
                        INSERT OR REPLACE INTO chat_histories (session_id, history)
                        VALUES (?, ?)
                    """, (session_id, updated_history_json))
                    conn.commit()
            except Exception as e:
                logger.error(f"Error updating history for session {session_id}: {e}", exc_info=True)

# --- EmbeddingManager for CSV QA (remains in app.py) ---
@dataclass
class QAEmbeddings:
    questions: List[str]
    question_map: List[int]
    embeddings: torch.Tensor
    df_qa: pd.DataFrame
    original_questions: List[str]

class EmbeddingManager:
    def __init__(self, model_name='all-MiniLM-L6-v2'):
        self.model = SentenceTransformer(model_name)
        self.embeddings = {
            'general': None,
            'personal': None,
            'greetings': None
        }
        logger.info(f"EmbeddingManager initialized with model: {model_name}")

    def _process_questions(self, df: pd.DataFrame) -> Tuple[List[str], List[int], List[str]]:
        questions = []
        question_map = []
        original_questions = []

        if 'Question' not in df.columns:
            logger.warning(f"DataFrame for EmbeddingManager is missing 'Question' column. Cannot process questions from it.")
            return questions, question_map, original_questions

        for idx, question_text_raw in enumerate(df['Question']):
            if pd.isna(question_text_raw):
                continue
            question_text_cleaned = str(question_text_raw).strip()
            if not question_text_cleaned or question_text_cleaned.lower() == "nan":
                continue

            questions.append(question_text_cleaned)
            question_map.append(idx)
            original_questions.append(question_text_cleaned)

        return questions, question_map, original_questions

    def update_embeddings(self, general_qa: pd.DataFrame, personal_qa: pd.DataFrame, greetings_qa: pd.DataFrame):
        gen_questions, gen_question_map, gen_original_questions = self._process_questions(general_qa)
        gen_embeddings = self.model.encode(gen_questions, convert_to_tensor=True, show_progress_bar=False) if gen_questions else None

        pers_questions, pers_question_map, pers_original_questions = self._process_questions(personal_qa)
        pers_embeddings = self.model.encode(pers_questions, convert_to_tensor=True, show_progress_bar=False) if pers_questions else None

        greet_questions, greet_question_map, greet_original_questions = self._process_questions(greetings_qa)
        greet_embeddings = self.model.encode(greet_questions, convert_to_tensor=True, show_progress_bar=False) if greet_questions else None

        self.embeddings['general'] = QAEmbeddings(
            questions=gen_questions, question_map=gen_question_map, embeddings=gen_embeddings,
            df_qa=general_qa, original_questions=gen_original_questions
        )
        self.embeddings['personal'] = QAEmbeddings(
            questions=pers_questions, question_map=pers_question_map, embeddings=pers_embeddings,
            df_qa=personal_qa, original_questions=pers_original_questions
        )
        self.embeddings['greetings'] = QAEmbeddings(
            questions=greet_questions, question_map=greet_question_map, embeddings=greet_embeddings,
            df_qa=greetings_qa, original_questions=greet_original_questions
        )
        logger.info("CSV QA embeddings updated in EmbeddingManager.")

    def find_best_answers(self, user_query: str, qa_type: str, top_n: int = 5) -> Tuple[List[float], List[str], List[str], List[str], List[int]]:
        qa_data = self.embeddings[qa_type]
        if qa_data is None or qa_data.embeddings is None or len(qa_data.embeddings) == 0:
            return [], [], [], [], []

        query_embedding_tensor = self.model.encode([user_query], convert_to_tensor=True, show_progress_bar=False)
        if not isinstance(qa_data.embeddings, torch.Tensor):
             qa_data.embeddings = torch.tensor(qa_data.embeddings) # Safeguard

        cos_scores = util.cos_sim(query_embedding_tensor, qa_data.embeddings)[0]

        top_k = min(top_n, len(cos_scores))
        if top_k == 0:
            return [], [], [], [], []

        top_scores_tensor, indices_tensor = torch.topk(cos_scores, k=top_k)

        top_confidences = [score.item() * 100 for score in top_scores_tensor]
        top_indices_mapped = []
        top_questions = []

        for idx_tensor in indices_tensor:
            item_idx = idx_tensor.item()
            if item_idx < len(qa_data.question_map) and item_idx < len(qa_data.original_questions):
                 original_df_idx = qa_data.question_map[item_idx]
                 if original_df_idx < len(qa_data.df_qa):
                    top_indices_mapped.append(original_df_idx)
                    top_questions.append(qa_data.original_questions[item_idx])
                 else:
                    logger.warning(f"Index out of bounds: original_df_idx {original_df_idx} for df_qa length {len(qa_data.df_qa)}")
            else:
                logger.warning(f"Index out of bounds: item_idx {item_idx} for question_map/original_questions")

        valid_count = len(top_indices_mapped)
        top_confidences = top_confidences[:valid_count]
        top_questions = top_questions[:valid_count]

        top_answers = [str(qa_data.df_qa['Answer'].iloc[i]) for i in top_indices_mapped]
        top_images = [str(qa_data.df_qa['Image'].iloc[i]) if 'Image' in qa_data.df_qa.columns and pd.notna(qa_data.df_qa['Image'].iloc[i]) else None for i in top_indices_mapped]

        return top_confidences, top_questions, top_answers, top_images, top_indices_mapped

# --- DatabaseMonitor for personal_qa.csv placeholders (remains in app.py) ---
class DatabaseMonitor:
    def __init__(self, database_path):
        self.logger = logging.getLogger(__name__ + ".DatabaseMonitor")
        self.database_path = database_path
        self.last_modified = None
        self.last_size = None
        self.df = None
        self.lock = Lock()
        self.running = True
        self._load_database()
        self.monitor_thread = Thread(target=self._monitor_database, daemon=True)
        self.monitor_thread.start()
        self.logger.info(f"DatabaseMonitor initialized for: {database_path}")

    def _load_database(self):
        try:
            if not os.path.exists(self.database_path):
                self.logger.warning(f"Personal data file not found: {self.database_path}.")
                self.df = None
                return
            with self.lock:
                self.df = pd.read_csv(self.database_path, encoding='cp1252')
                self.last_modified = os.path.getmtime(self.database_path)
                self.last_size = os.path.getsize(self.database_path)
                self.logger.info(f"Personal data file reloaded: {self.database_path}")
        except Exception as e:
            self.logger.error(f"Error loading personal data file '{self.database_path}': {e}", exc_info=True)
            self.df = None

    def _monitor_database(self):
        while self.running:
            try:
                if not os.path.exists(self.database_path):
                    if self.df is not None:
                        self.logger.warning(f"Personal data file disappeared: {self.database_path}")
                        self.df = None; self.last_modified = None; self.last_size = None
                    time.sleep(5)
                    continue
                current_modified = os.path.getmtime(self.database_path); current_size = os.path.getsize(self.database_path)
                if (self.last_modified is None or current_modified != self.last_modified or
                    self.last_size is None or current_size != self.last_size):
                    self.logger.info("Personal data file change detected.")
                    self._load_database()
                time.sleep(1)
            except Exception as e:
                self.logger.error(f"Error monitoring personal data file: {e}", exc_info=True)
                time.sleep(5)

    def get_data(self, user_id):
        with self.lock:
            if self.df is not None and user_id:
                try:
                    # MODIFIED: The user_id from the frontend is the 'sl' column
                    target_id_col = 'sl'
                    if target_id_col not in self.df.columns:
                        self.logger.warning(f"'{target_id_col}' column not found in personal_data.csv (database.csv)")
                        return None
                    
                    # Ensure the user_id is of the same type as the column
                    id_col_type = self.df[target_id_col].dtype
                    try:
                        typed_user_id = pd.Series(user_id).astype(id_col_type).iloc[0]
                    except (ValueError, TypeError):
                         self.logger.warning(f"Could not convert user_id '{user_id}' to the required type {id_col_type}")
                         return None

                    user_data = self.df[self.df[target_id_col] == typed_user_id]
                    if not user_data.empty: return user_data.iloc[0].to_dict()
                except Exception as e:
                    self.logger.error(f"Error retrieving data for user_id {user_id}: {e}", exc_info=True)
            return None

    def stop(self):
        self.running = False
        if hasattr(self, 'monitor_thread') and self.monitor_thread.is_alive():
            self.monitor_thread.join(timeout=5)
        self.logger.info("DatabaseMonitor stopped.")

# --- Flask App Initialization ---
app = Flask(__name__, 
            static_folder='static',
            static_url_path='/static',
            template_folder='templates')

CORS(app, resources={r"/*": {"origins": "*"}}, supports_credentials=True)


# Add this logging to debug requests
@app.before_request
def log_request_info():
    logger.info(f'Request: {request.method} {request.path}')
    if request.method == 'POST':
        logger.info(f'Request from: {request.remote_addr}')

# --- Initialize Managers ---
embedding_manager = EmbeddingManager()
history_manager = ChatHistoryManager('chat_history.db')
database_csv_path = os.path.join(RAG_SOURCES_DIR, 'database.csv')
personal_data_monitor = DatabaseMonitor(database_csv_path)

# --- Helper Functions (App specific) ---
def normalize_text(text):
    if isinstance(text, str):
        replacements = {
            '\x91': "'", '\x92': "'", '\x93': '"', '\x94': '"',
            '\x96': '-', '\x97': '-', '\x85': '...', '\x95': '-',
            '"': '"', '"': '"', '‘': "'", '’': "'",
            '–': '-', '—': '-', '…': '...', '•': '-',
        }
        for old, new in replacements.items(): text = text.replace(old, new)
    return text

def require_admin_auth(f):
    @functools.wraps(f)
    def decorated(*args, **kwargs):
        auth = request.authorization
        if not auth:
            return Response('Admin auth failed.', 401, {'WWW-Authenticate': 'Basic realm="Admin Login Required"'})

        # MODIFIED: Authenticate against users.csv
        if user_df is not None:
            user_email = auth.username.lower().strip()
            user_record = user_df[user_df['email'] == user_email]

            if not user_record.empty:
                user_data = user_record.iloc[0]
                # Important: Compare password as string
                if str(user_data['password']) == auth.password and user_data['role'] == 'admin':
                    return f(*args, **kwargs) # Success
        # Fallback to .env credentials if users.csv failed or user not found
        elif auth.username == ADMIN_USERNAME and auth.password == ADMIN_PASSWORD:
            logger.warning("Admin authenticated using fallback .env credentials.")
            return f(*args, **kwargs)

        return Response('Admin auth failed.', 401, {'WWW-Authenticate': 'Basic realm="Admin Login Required"'})
    return decorated

def require_report_auth(f):
    @functools.wraps(f)
    def decorated(*args, **kwargs):
        auth = request.authorization
        if not auth or auth.username != ADMIN_USERNAME or auth.password != REPORT_PASSWORD:
            return Response('Report auth failed.', 401, {'WWW-Authenticate': 'Basic realm="Report Login Required"'})
        return f(*args, **kwargs)
    return decorated

def initialize_chat_log():
    if not os.path.exists(CHAT_LOG_FILE):
        with open(CHAT_LOG_FILE, 'w', newline='', encoding='utf-8') as f:
            writer = csv_lib.writer(f)
            writer.writerow(['sl', 'date_time', 'session_id', 'user_id', 'query', 'answer'])

def store_chat_history(sid: str, uid: Optional[str], query: str, resp: Dict[str, Any]):
    """
    Stores chat history in both the persistent SQLite DB and the CSV log file.
    """
    try:
        answer = str(resp.get('answer', ''))
        history_manager.update_history(sid, query, answer)

        initialize_chat_log()
        next_sl = 1
        try:
            if os.path.exists(CHAT_LOG_FILE) and os.path.getsize(CHAT_LOG_FILE) > 0:
                df_log = pd.read_csv(CHAT_LOG_FILE, on_bad_lines='skip')
                if not df_log.empty and 'sl' in df_log.columns and pd.api.types.is_numeric_dtype(df_log['sl'].dropna()):
                    if not df_log['sl'].dropna().empty:
                        next_sl = int(df_log['sl'].dropna().max()) + 1
        except Exception as e:
            logger.error(f"Error reading SL from {CHAT_LOG_FILE}: {e}", exc_info=True)

        with open(CHAT_LOG_FILE, 'a', newline='', encoding='utf-8') as f:
            csv_lib.writer(f).writerow([next_sl, datetime.now().strftime('%Y-%m-%d %H:%M:%S'), sid, uid or "N/A", query, answer])

    except Exception as e:
        logger.error(f"Error in store_chat_history for session {sid}: {e}", exc_info=True)

def get_formatted_chat_history(session_id: str) -> List[Dict[str, str]]:
    """
    Retrieves the chat history for a session from the persistent SQLite database.
    """
    return history_manager.get_history(session_id, limit=CHAT_HISTORY_TO_SEND)

def get_qa_context_for_groq(all_questions: List[Dict]) -> str:
    valid_qa_pairs = []
    non_greeting_questions = [q for q in all_questions if q.get('source_type') != 'greetings']
    sorted_questions = sorted(non_greeting_questions, key=lambda x: x.get('confidence', 0), reverse=True)

    for qa in sorted_questions[:QUESTIONS_TO_SEND_TO_GROQ_QA]:
        answer = qa.get('answer')
        if (not pd.isna(answer) and isinstance(answer, str) and answer.strip() and
            "not available" not in answer.lower()):
            valid_qa_pairs.append(f"Q: {qa.get('question')}\nA: {answer}")
    return '\n'.join(valid_qa_pairs)

def replace_placeholders_in_answer(answer, db_data):
    if pd.isna(answer) or str(answer).strip() == '':
        return "Sorry, this information is not available yet"
    answer_str = str(answer)
    placeholders = re.findall(r'\{(\w+)\}', answer_str)
    if not placeholders: return answer_str
    if db_data is None:
        return "To get this specific information, please ensure you are logged in or have provided your user ID."
    missing_count = 0; replacements_made = 0
    for placeholder in set(placeholders):
        key = placeholder.strip()
        value = db_data.get(key)
        if value is None or (isinstance(value, float) and pd.isna(value)) or str(value).strip() == '':
            answer_str = answer_str.replace(f'{{{key}}}', "not available")
            missing_count += 1
        else:
            answer_str = answer_str.replace(f'{{{key}}}', str(value))
            replacements_made +=1
    if missing_count == len(placeholders) and len(placeholders) > 0 :
        return "Sorry, some specific details for you are not available at the moment."
    if "not available" in answer_str.lower() and replacements_made < len(placeholders):
         if answer_str == "not available" and len(placeholders) == 1:
             return "Sorry, this information is not available yet."
    if re.search(r'\{(\w+)\}', answer_str):
        logger.warning(f"Unresolved placeholders remain after replacement attempt: {answer_str}")
        answer_str = re.sub(r'\{(\w+)\}', "a specific detail", answer_str)
        if "a specific detail" in answer_str and not "Sorry" in answer_str:
            return "Sorry, I couldn't retrieve all the specific details for this answer. " + answer_str
        return "Sorry, I couldn't retrieve all the specific details for this answer. Some information has been generalized."
    return answer_str

# --- NEW User Login Endpoint ---
@app.route('/user-login', methods=['POST'])
def user_login():
    if user_df is None:
        return jsonify({"error": "User authentication is not available."}), 503

    data = request.json
    email = data.get('email', '').lower().strip()
    password = data.get('password')

    if not email or not password:
        return jsonify({"error": "Email and password are required."}), 400

    user_record = user_df[user_df['email'] == email]
    if not user_record.empty:
        user_data = user_record.iloc[0]
        # Compare password as string to avoid type issues
        if str(user_data['password']) == str(password):
            # Return user data but exclude password
            response_data = user_data.to_dict()
            del response_data['password']
            return jsonify(response_data), 200

    return jsonify({"error": "Invalid credentials"}), 401


# --- Main Chat Endpoint ---
@app.route('/chat-bot', methods=['POST'])
def get_answer_hybrid():
    global rag_system
    data = request.json
    user_query = data.get('query', '')
    user_id = data.get('user_id')
    session_id = data.get('session_id')

    if not user_query: return jsonify({'error': 'No query provided'}), 400
    if not session_id: return jsonify({'error': 'session_id is required'}), 400

    personal_db_data = personal_data_monitor.get_data(user_id) if user_id else None

    conf_greet, q_greet, a_greet, img_greet, _ = embedding_manager.find_best_answers(user_query, 'greetings', top_n=1)
    conf_pers, q_pers, a_pers, img_pers, _ = embedding_manager.find_best_answers(user_query, 'personal', top_n=RELATED_QUESTIONS_TO_SHOW)
    conf_gen, q_gen, a_gen, img_gen, _ = embedding_manager.find_best_answers(user_query, 'general', top_n=RELATED_QUESTIONS_TO_SHOW)

    all_csv_candidate_answers = []
    if conf_greet and conf_greet[0] >= HIGH_CONFIDENCE_THRESHOLD:
        all_csv_candidate_answers.append({'question': q_greet[0], 'answer': a_greet[0], 'image': img_greet[0] if img_greet else None, 'confidence': conf_greet[0], 'source_type': 'greetings'})
    if conf_pers:
        for c, q, a, img in zip(conf_pers, q_pers, a_pers, img_pers):
            processed_a = replace_placeholders_in_answer(a, personal_db_data)
            if not ("Sorry, this information is not available yet" in processed_a or "To get this specific information" in processed_a):
                all_csv_candidate_answers.append({'question': q, 'answer': processed_a, 'image': img, 'confidence': c, 'source_type': 'personal'})
    if conf_gen:
        for c, q, a, img in zip(conf_gen, q_gen, a_gen, img_gen):
            if not (pd.isna(a) or str(a).strip() == '' or str(a).lower() == 'nan'):
                all_csv_candidate_answers.append({'question': q, 'answer': str(a), 'image': img, 'confidence': c, 'source_type': 'general'})

    all_csv_candidate_answers.sort(key=lambda x: x['confidence'], reverse=True)

    related_questions_list = []

    if all_csv_candidate_answers:
        best_csv_match = all_csv_candidate_answers[0]
        is_direct_csv_answer = False
        source_name = ""
        if best_csv_match['source_type'] == 'greetings' and best_csv_match['confidence'] >= HIGH_CONFIDENCE_THRESHOLD:
            source_name = 'greetings_qa'; is_direct_csv_answer = True
        elif best_csv_match['source_type'] == 'personal' and best_csv_match['confidence'] >= DB_QA_CONFIDENCE:
            source_name = 'personal_qa'; is_direct_csv_answer = True
        elif best_csv_match['source_type'] == 'general' and best_csv_match['confidence'] >= GENERAL_QA_CONFIDENCE:
            source_name = 'general_qa'; is_direct_csv_answer = True

        if is_direct_csv_answer:
            response_data = {'query': user_query, 'answer': best_csv_match['answer'], 'confidence': best_csv_match['confidence'], 'original_question': best_csv_match['question'], 'source': source_name}
            if best_csv_match['image']: response_data['image_url'] = url_for('static', filename=best_csv_match['image'], _external=True)
            for i, cand_q in enumerate(all_csv_candidate_answers):
                if i == 0: continue
                if cand_q['source_type'] != 'greetings':
                     related_questions_list.append({'question': cand_q['question'], 'answer': cand_q['answer'], 'match': cand_q['confidence']})
                if len(related_questions_list) >= RELATED_QUESTIONS_TO_SHOW: break
            response_data['related_questions'] = related_questions_list
            store_chat_history(session_id, user_id, user_query, response_data)
            return jsonify(response_data)

    if rag_system and rag_system.retriever:
        try:
            logger.info(f"Attempting FAISS RAG query for: {user_query[:50]}...")
            rag_result = rag_system.query(user_query)
            rag_answer = rag_result.get("answer")
            rag_sources_details = rag_result.get("cited_source_details")

            if rag_answer and \
               "based on the provided excerpts, i cannot answer" not in rag_answer.lower() and \
               "based on the available documents, i could not find relevant information" not in rag_answer.lower() and \
               "error:" not in rag_answer.lower() and \
               "i could not find relevant information" not in rag_answer.lower() and \
               "please provide a valid question" not in rag_answer.lower():
                logger.info(f"FAISS RAG system provided an answer: {rag_answer[:100]}...")

                if not related_questions_list:
                    for cand_q in all_csv_candidate_answers:
                        if cand_q['source_type'] != 'greetings':
                            related_questions_list.append({'question': cand_q['question'], 'answer': cand_q['answer'], 'match': cand_q['confidence']})
                        if len(related_questions_list) >= RELATED_QUESTIONS_TO_SHOW: break

                response_data = {
                    'query': user_query,
                    'answer': rag_answer,
                    'confidence': 85,
                    'source': 'document_rag_faiss',
                    'related_questions': related_questions_list,
                    'document_sources_details': rag_sources_details
                }
                store_chat_history(session_id, user_id, user_query, response_data)
                return jsonify(response_data)
            else:
                logger.info(f"FAISS RAG system could not answer or returned an error/no info/invalid query. RAG Answer: '{rag_answer}'. Proceeding to general Groq.")
        except Exception as e:
            logger.error(f"Error during FAISS RAG system query: {e}", exc_info=True)

    logger.info(f"No high-confidence CSV or FAISS RAG answer for '{user_query[:50]}...'. Proceeding to general Groq fallback.")

    qa_context_for_groq_str = get_qa_context_for_groq(all_csv_candidate_answers)
    chat_history_messages_for_groq = get_formatted_chat_history(session_id)

    groq_context = {
        'current_query': user_query,
        'chat_history': chat_history_messages_for_groq,
        'qa_related_info': qa_context_for_groq_str,
        'document_related_info': ""
    }

    try:
        groq_answer = get_groq_fallback_response(groq_context)

        if groq_answer and \
           "Sorry, this information is not available yet" not in groq_answer and \
           "I'm currently experiencing a technical difficulty" not in groq_answer and \
           "I specialize in topics related to AMO Green Energy." not in groq_answer:

            if not related_questions_list:
                for cand_q in all_csv_candidate_answers:
                    if cand_q['source_type'] != 'greetings':
                        related_questions_list.append({'question': cand_q['question'], 'answer': cand_q['answer'], 'match': cand_q['confidence']})
                    if len(related_questions_list) >= RELATED_QUESTIONS_TO_SHOW: break

            response_data = {
                'query': user_query, 'answer': groq_answer,
                'confidence': 75,
                'source': 'groq_general_fallback',
                'related_questions': related_questions_list,
                'document_sources_details': []
            }
            store_chat_history(session_id, user_id, user_query, response_data)
            return jsonify(response_data)
    except Exception as e:
        logger.error(f"General Groq fallback pipeline error: {e}", exc_info=True)

    if not related_questions_list:
        for cand_q in all_csv_candidate_answers:
            if cand_q['source_type'] != 'greetings':
                related_questions_list.append({'question': cand_q['question'], 'answer': cand_q['answer'], 'match': cand_q['confidence']})
            if len(related_questions_list) >= RELATED_QUESTIONS_TO_SHOW: break

    fallback_message = (
        "For the most current and specific details on your query, particularly regarding product specifications or pricing, "
        "please contact AMO Green Energy Limited directly. Our team is ready to assist you.\n\n"
        "Contact Information:\n"
        "Email: [email protected]\n"
        "Phone: +880 1781-469951\n"
        "Website: ge-bd.com"
    )
    response_data = {
        'query': user_query, 'answer': fallback_message, 'confidence': 0,
        'source': 'fallback', 'related_questions': related_questions_list
    }
    store_chat_history(session_id, user_id, user_query, response_data)
    return jsonify(response_data)

# --- Admin and Utility Routes ---
@app.route('/')
def index_route():
    template_to_render = 'chat-bot.html'
    # CHANGED: Check in templates folder
    template_path = os.path.join(app.root_path, 'templates', template_to_render)
    
    if not os.path.exists(template_path):
        logger.error(f"Template '{template_to_render}' not found at {template_path}")
        return f"Chatbot interface not found at {template_path}. Please ensure 'templates/chat-bot.html' exists.", 404
    
    logger.info(f"Serving template: {template_to_render}")
    return render_template(template_to_render)

@app.route('/admin/verify-session', methods=['POST'])
def verify_admin_session():
    """
    Verifies if the current user (from frontend session) is an admin.
    No HTTP Basic Auth needed - uses the user data from frontend.
    """
    data = request.json
    user_email = data.get('email', '').lower().strip()
    
    if not user_email:
        return jsonify({"is_admin": False, "error": "Email required"}), 400
    
    if user_df is None:
        return jsonify({"is_admin": False, "error": "User data not available"}), 503
    
    user_record = user_df[user_df['email'] == user_email]
    
    if not user_record.empty:
        user_data = user_record.iloc[0]
        is_admin = user_data['role'] == 'admin'
        return jsonify({"is_admin": is_admin}), 200
    
    return jsonify({"is_admin": False}), 200

@app.route('/admin/login', methods=['POST'])
@require_admin_auth
def admin_login():
    """
    This endpoint is solely for verifying admin credentials via the decorator.
    If credentials are valid, it returns 200 OK.
    If not, the decorator returns 401 Unauthorized.
    """
    return jsonify({"status": "success", "message": "Authentication successful"}), 200

@app.route('/admin/faiss_rag_status', methods=['GET'])
@require_admin_auth
def get_faiss_rag_status():
    global rag_system
    if not rag_system:
        return jsonify({"error": "FAISS RAG system not initialized."}), 500
    try:
        status = {
            "status": "Initialized" if rag_system.retriever else "Initialized (Retriever not ready)",
            "index_storage_dir": rag_system.index_storage_dir,
            "embedding_model": rag_system.embedding_model_name,
            "groq_model": rag_system.groq_model_name,
            "retriever_k": rag_system.retriever.final_k if rag_system.retriever else "N/A",
            "processed_source_files": rag_system.processed_source_files,
            "index_type": "FAISS",
            "index_loaded_or_built": rag_system.vector_store is not None
        }
        if rag_system.vector_store and hasattr(rag_system.vector_store, 'index') and rag_system.vector_store.index:
            try:
                status["num_vectors_in_index"] = rag_system.vector_store.index.ntotal
            except Exception:
                status["num_vectors_in_index"] = "N/A (Could not get count)"
        else:
            status["num_vectors_in_index"] = "N/A (Vector store or index not available)"
        return jsonify(status)
    except Exception as e:
        logger.error(f"Error getting FAISS RAG status: {e}", exc_info=True)
        return jsonify({"error": str(e)}), 500

@app.route('/admin/rebuild_faiss_index', methods=['POST'])
@require_admin_auth
def rebuild_faiss_index_route():
    global rag_system
    logger.info("Admin request to rebuild FAISS RAG index received. Starting two-step process.")

    data = request.json or {}
    source_dir_override = data.get('source_directory')
    source_dir_to_use = source_dir_override if source_dir_override else RAG_SOURCES_DIR
    
    if source_dir_override and not os.path.isdir(source_dir_override):
        return jsonify({"error": f"Custom source directory '{source_dir_override}' not found on the server."}), 400
    
    logger.info(f"Using source directory: {source_dir_to_use}")

    logger.info("Step 1: Running chunker.py to pre-process source documents.")
    chunker_script_path = os.path.join(_APP_BASE_DIR, 'chunker.py')
    chunked_json_output_path = os.path.join(RAG_STORAGE_PARENT_DIR, RAG_CHUNKED_SOURCES_FILENAME)

    os.makedirs(TEXT_EXTRACTIONS_DIR, exist_ok=True)

    if not os.path.exists(chunker_script_path):
        logger.error(f"Chunker script not found at '{chunker_script_path}'. Aborting rebuild.")
        return jsonify({"error": f"chunker.py not found. Cannot proceed with rebuild."}), 500
    
    chunk_size = os.getenv("RAG_CHUNK_SIZE", "1000")
    chunk_overlap = os.getenv("RAG_CHUNK_OVERLAP", "150")

    command = [
        sys.executable,
        chunker_script_path,
        '--sources-dir', source_dir_to_use,
        '--output-file', chunked_json_output_path,
        '--text-output-dir', TEXT_EXTRACTIONS_DIR,
        '--chunk-size', chunk_size,
        '--chunk-overlap', chunk_overlap
    ]

    try:
        process = subprocess.run(command, capture_output=True, text=True, check=True)
        logger.info("Chunker script executed successfully.")
        logger.info(f"Chunker stdout:\n{process.stdout}")
    except subprocess.CalledProcessError as e:
        logger.error(f"Chunker script failed with exit code {e.returncode}.")
        logger.error(f"Chunker stderr:\n{e.stderr}")
        return jsonify({"error": "Step 1 (Chunking) failed.", "details": e.stderr}), 500
    except Exception as e:
        logger.error(f"An unexpected error occurred while running the chunker script: {e}", exc_info=True)
        return jsonify({"error": f"An unexpected error occurred during the chunking step: {str(e)}"}), 500

    logger.info("Step 2: Rebuilding FAISS index from the newly generated chunks.")
    try:
        new_rag_system_instance = initialize_and_get_rag_system(force_rebuild=True, source_dir_override=source_dir_override)

        if new_rag_system_instance and new_rag_system_instance.vector_store:
            rag_system = new_rag_system_instance
            logger.info("FAISS RAG index rebuild completed and new RAG system instance is active.")
            updated_status_response = get_faiss_rag_status()
            return jsonify({"message": "FAISS RAG index rebuild completed.", "status": updated_status_response.get_json()}), 200
        else:
            logger.error("FAISS RAG index rebuild failed during the indexing phase.")
            return jsonify({"error": "Step 2 (Indexing) failed. Check logs."}), 500

    except Exception as e:
        logger.error(f"Error during admin FAISS index rebuild (indexing phase): {e}", exc_info=True)
        return jsonify({"error": f"Failed to rebuild index during indexing phase: {str(e)}"}), 500

@app.route('/admin/update_faiss_index', methods=['POST'])
@require_admin_auth
def update_faiss_index_route():
    global rag_system
    logger.info("Admin request to update FAISS RAG index with new files received.")
    
    if not rag_system or not rag_system.vector_store:
        return jsonify({"error": "RAG system not initialized or index not loaded. Cannot perform update."}), 503

    data = request.json or {}
    source_dir_override = data.get('source_directory')
    source_dir_to_use = source_dir_override if source_dir_override else RAG_SOURCES_DIR

    max_files_to_process = data.get('max_new_files') 

    if source_dir_override and not os.path.isdir(source_dir_override):
        return jsonify({"error": f"Custom source directory '{source_dir_override}' not found on the server."}), 400

    logger.info(f"Checking for new files in: {source_dir_to_use}")
    if max_files_to_process:
        logger.info(f"Will process a maximum of {max_files_to_process} new files this session.")

    try:
        update_result = rag_system.update_index_with_new_files(
            source_folder_path=source_dir_to_use,
            max_files_to_process=max_files_to_process
        )
        logger.info(f"Index update process finished with status: {update_result.get('status')}")
        return jsonify(update_result), 200
    except Exception as e:
        logger.error(f"Error during admin FAISS index update: {e}", exc_info=True)
        return jsonify({"error": f"Failed to update index: {str(e)}"}), 500


@app.route('/db/status', methods=['GET'])
@require_admin_auth
def get_personal_db_status():
    try:
        status_info = {
            'personal_data_csv_monitor_status': 'running',
            'file_exists': os.path.exists(personal_data_monitor.database_path),
            'data_loaded': personal_data_monitor.df is not None, 'last_update': None
        }
        if status_info['file_exists'] and os.path.getmtime(personal_data_monitor.database_path) is not None:
            status_info['last_update'] = datetime.fromtimestamp(os.path.getmtime(personal_data_monitor.database_path)).isoformat()
        return jsonify(status_info)
    except Exception as e: return jsonify({'status': 'error', 'error': str(e)}), 500

@app.route('/report', methods=['GET'])
@require_report_auth
def download_report():
    try:
        if not os.path.exists(CHAT_LOG_FILE) or os.path.getsize(CHAT_LOG_FILE) == 0:
            return jsonify({'error': 'No chat history available.'}), 404
        return send_file(CHAT_LOG_FILE, mimetype='text/csv', as_attachment=True, download_name=f'chat_history_{datetime.now().strftime("%Y%m%d_%H%M%S")}.csv')
    except Exception as e:
        logger.error(f"Error downloading report: {e}", exc_info=True)
        return jsonify({'error': 'Failed to generate report'}), 500

@app.route('/create-session', methods=['POST'])
def create_session_route():
    try:
        session_id = str(generate_uuid())
        logger.info(f"New session created: {session_id}")
        return jsonify({'status': 'success', 'session_id': session_id}), 200
    except Exception as e:
        logger.error(f"Session creation error: {e}", exc_info=True)
        return jsonify({'status': 'error', 'message': str(e)}), 500

@app.route('/version', methods=['GET'])
def get_version_route():
    return jsonify({'version': '3.9.1-CSV-Auth-Persistent-History'}), 200

@app.route('/clear-history', methods=['POST'])
def clear_session_history_route():
    session_id = request.json.get('session_id')
    if not session_id: return jsonify({'status': 'error', 'message': 'session_id is required'}), 400
    # MODIFIED: Use the new, correct method instead of the old one
    history_manager.clear_history(session_id) 
    logger.info(f"Chat history cleared for session: {session_id}")
    return jsonify({'status': 'success', 'message': 'History cleared'})
    
@app.route('/chat-history', methods=['GET'])
def get_chat_history_route():
    session_id = request.args.get('session_id')
    limit = request.args.get('limit', default=10, type=int)
    if not session_id:
        return jsonify({"error": "session_id is required"}), 400
    
    history = history_manager.get_history(session_id, limit=limit)
    
    structured_history = []
    for i in range(0, len(history), 2):
        if i + 1 < len(history):
            user_msg = history[i]
            bot_msg = history[i+1]
            structured_history.append({
                "query": user_msg.get('content'),
                "response": { "answer": bot_msg.get('content') }
            })

    return jsonify({"history": structured_history})

@app.route('/admin/retrieve-chunks', methods=['POST'])
@require_admin_auth
def retrieve_raw_chunks():
    global rag_system
    if not rag_system or not rag_system.retriever:
        return jsonify({"error": "RAG system not initialized or retriever not available."}), 503

    data = request.json
    query = data.get('query')
    if not query:
        return jsonify({"error": "A 'query' is required."}), 400

    # Get optional parameters from the request, with defaults from the RAG system's current configuration
    use_reranker = data.get('use_reranker', rag_system.retriever.reranker is not None)
    initial_fetch_k = data.get('initial_fetch_k', rag_system.retriever.initial_fetch_k)
    final_k = data.get('final_k', rag_system.retriever.final_k)

    # Store original retriever settings to ensure thread safety and no lasting changes
    original_reranker = rag_system.retriever.reranker
    original_initial_k = rag_system.retriever.initial_fetch_k
    original_final_k = rag_system.retriever.final_k

    try:
        # Temporarily modify retriever settings for this specific query
        rag_system.retriever.reranker = original_reranker if use_reranker else None
        rag_system.retriever.initial_fetch_k = int(initial_fetch_k)
        rag_system.retriever.final_k = int(final_k)

        logger.info(f"Performing raw chunk retrieval for query: '{query[:50]}...'")
        logger.info(f"Temporary Settings: use_reranker={use_reranker}, initial_fetch_k={initial_fetch_k}, final_k={final_k}")

        # Directly call the retriever to get the relevant documents
        retrieved_docs = rag_system.retriever.get_relevant_documents(query)

        # Format the results into a JSON-serializable list
        results = []
        for doc in retrieved_docs:
            results.append({
                "page_content": doc.page_content,
                "metadata": doc.metadata
            })

        return jsonify({
            "query": query,
            "retrieved_chunks": results,
            "chunk_count": len(results)
        })

    except Exception as e:
        logger.error(f"Error during raw chunk retrieval: {e}", exc_info=True)
        return jsonify({"error": f"An error occurred during retrieval: {str(e)}"}), 500
    finally:
        # Restore the original retriever settings to prevent side effects
        rag_system.retriever.reranker = original_reranker
        rag_system.retriever.initial_fetch_k = original_initial_k
        rag_system.retriever.final_k = original_final_k
        logger.info("Retriever settings have been restored to their original values.")
        
# --- App Cleanup and Startup ---
def cleanup_application():
    if personal_data_monitor: personal_data_monitor.stop()
    logger.info("Application cleanup finished.")
atexit.register(cleanup_application)

def load_qa_data_on_startup():
    global embedding_manager
    try:
        general_qa_path = os.path.join(RAG_SOURCES_DIR, 'general_qa.csv')
        personal_qa_path = os.path.join(RAG_SOURCES_DIR, 'personal_qa.csv')
        greetings_qa_path = os.path.join(RAG_SOURCES_DIR, 'greetings.csv')

        general_qa_df = pd.DataFrame(columns=['Question', 'Answer', 'Image'])
        personal_qa_df = pd.DataFrame(columns=['Question', 'Answer', 'Image'])
        greetings_qa_df = pd.DataFrame(columns=['Question', 'Answer', 'Image'])

        if os.path.exists(general_qa_path):
            try: general_qa_df = pd.read_csv(general_qa_path, encoding='cp1252')
            except Exception as e_csv: logger.error(f"Error reading general_qa.csv: {e_csv}")
        else:
            logger.warning(f"Optional file 'general_qa.csv' not found in '{RAG_SOURCES_DIR}'.")

        if os.path.exists(personal_qa_path):
            try: personal_qa_df = pd.read_csv(personal_qa_path, encoding='cp1252')
            except Exception as e_csv: logger.error(f"Error reading personal_qa.csv: {e_csv}")
        else:
            logger.warning(f"Optional file 'personal_qa.csv' not found in '{RAG_SOURCES_DIR}'.")

        if os.path.exists(greetings_qa_path):
            try: greetings_qa_df = pd.read_csv(greetings_qa_path, encoding='cp1252')
            except Exception as e_csv: logger.error(f"Error reading greetings.csv: {e_csv}")
        else:
            logger.warning(f"Optional file 'greetings.csv' not found in '{RAG_SOURCES_DIR}'.")

        dataframes_to_process = {
            "general": general_qa_df,
            "personal": personal_qa_df,
            "greetings": greetings_qa_df
        }

        for df_name, df_val in dataframes_to_process.items():
            for col in ['Question', 'Answer', 'Image']:
                if col not in df_val.columns:
                    df_val[col] = None
                    if col != 'Image':
                         logger.warning(f"'{col}' column missing in {df_name} data. Added empty column.")

            if 'Question' in df_val.columns and not df_val['Question'].isnull().all():
                 df_val['Question'] = df_val['Question'].astype(str).apply(normalize_text)
            elif 'Question' in df_val.columns:
                 df_val['Question'] = df_val['Question'].astype(str)

            if 'Answer' in df_val.columns and not df_val['Answer'].isnull().all():
                 df_val['Answer'] = df_val['Answer'].astype(str).apply(normalize_text)
            elif 'Answer' in df_val.columns:
                 df_val['Answer'] = df_val['Answer'].astype(str)

        embedding_manager.update_embeddings(
            dataframes_to_process["general"],
            dataframes_to_process["personal"],
            dataframes_to_process["greetings"]
        )
        logger.info("CSV QA data loaded and embeddings initialized.")

    except Exception as e:
        logger.critical(f"CRITICAL: Error loading or processing QA data: {e}. Semantic QA may not function.", exc_info=True)

if __name__ == '__main__':
    # CHANGED: Create necessary folders including assets and templates
    for folder_path in [os.path.join(_APP_BASE_DIR, 'templates'),
                        os.path.join(_APP_BASE_DIR, 'static'),
                        os.path.join(_APP_BASE_DIR, 'assets'),  # ADDED
                        TEXT_EXTRACTIONS_DIR]:
        os.makedirs(folder_path, exist_ok=True)

    # --- NEW: Download users.csv from GDrive if enabled ---
    if GDRIVE_USERS_CSV_ENABLED:
        logger.info("[GDRIVE_USERS_DOWNLOAD] Google Drive users.csv download is ENABLED.")
        if GDRIVE_USERS_CSV_ID_OR_URL:
            users_csv_target_path = os.path.join(_APP_BASE_DIR, 'assets', 'users.csv')
            logger.info(f"[GDRIVE_USERS_DOWNLOAD] Attempting to download users.csv to: {users_csv_target_path}")
            download_successful = download_gdrive_file(GDRIVE_USERS_CSV_ID_OR_URL, users_csv_target_path)
            if download_successful:
                logger.info("[GDRIVE_USERS_DOWNLOAD] Successfully downloaded users.csv.")
            else:
                logger.error("[GDRIVE_USERS_DOWNLOAD] Failed to download users.csv from Google Drive. Will use existing file or fallback.")
        else:
            logger.warning("[GDRIVE_USERS_DOWNLOAD] GDRIVE_USERS_CSV_ENABLED is True, but GDRIVE_USERS_CSV_URL is not set.")
    else:
        logger.info("[GDRIVE_USERS_DOWNLOAD] Google Drive users.csv download is DISABLED.")

    # Load users from CSV at startup (will use the downloaded file if successful)
    load_users_from_csv()

    load_qa_data_on_startup()
    initialize_chat_log()

    # MODIFIED: Download pre-built FAISS index from GDrive if enabled
    if GDRIVE_INDEX_ENABLED:
        logger.info("[GDRIVE_INDEX_DOWNLOAD] Google Drive index download is ENABLED.")
        if GDRIVE_INDEX_ID_OR_URL:
            logger.info(f"[GDRIVE_INDEX_DOWNLOAD] Attempting to download and extract index from: {GDRIVE_INDEX_ID_OR_URL}")
            # The root directory is the target for extraction, so 'faiss_storage' lands correctly
            download_successful = download_and_unzip_gdrive_file(GDRIVE_INDEX_ID_OR_URL, _APP_BASE_DIR)
            if download_successful:
                logger.info("[GDRIVE_INDEX_DOWNLOAD] Successfully downloaded and extracted FAISS index.")
            else:
                logger.error("[GDRIVE_INDEX_DOWNLOAD] Failed to download FAISS index from Google Drive. RAG system might build a new one if sources exist.")
        else:
            logger.warning("[GDRIVE_INDEX_DOWNLOAD] GDRIVE_INDEX_ENABLED is True, but GDRIVE_INDEX_URL is not set.")
    else:
        logger.info("[GDRIVE_INDEX_DOWNLOAD] Google Drive index download is DISABLED.")


    logger.info("Attempting to initialize RAG system from new modules...")
    rag_system = initialize_and_get_rag_system()
    if rag_system:
        logger.info("RAG system initialized successfully via new modules.")
    else:
        logger.warning("RAG system failed to initialize. Document RAG functionality will be unavailable.")

    logger.info(f"Flask application starting with Hybrid RAG (CSV + Dynamic FAISS) on {FLASK_APP_HOST}:{FLASK_APP_PORT} Debug: {FLASK_DEBUG_MODE}...")
    if not FLASK_DEBUG_MODE:
        werkzeug_log = logging.getLogger('werkzeug')
        werkzeug_log.setLevel(logging.ERROR)

    app.run(host=FLASK_APP_HOST, port=FLASK_APP_PORT, debug=FLASK_DEBUG_MODE)