process_scam_messages.py

import pandas as pd
import time
from groq import Groq
import os
import csv
from tqdm import tqdm
import logging
from datetime import datetime
import json
import sys
import requests
import aiohttp
import asyncio
import google.auth
from google.oauth2.credentials import Credentials
from google_auth_oauthlib.flow import InstalledAppFlow
from google.auth.transport.requests import Request
from google.oauth2 import service_account
from googleapiclient.discovery import build
from googleapiclient.http import MediaFileUpload, MediaIoBaseDownload
import io

# OAuth 2.0 credentials
CLIENT_ID = "483287191355-udtleajik8ko1o2n03fqmimuu47n3hba.apps.googleusercontent.com"
CLIENT_SECRET = "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"
SCOPES = ['https://www.googleapis.com/auth/drive.file']

def authenticate_google():
    """Authenticate with Google Drive using OAuth 2.0"""
    creds = None
    
    # Load credentials from client_secret.json if exists
    if os.path.exists('client_secret.json'):
        flow = InstalledAppFlow.from_client_secrets_file(
            'client_secret.json', SCOPES)
        creds = flow.run_local_server(port=0)
    else:
        # Create credentials manually if client_secret.json not found
        flow = InstalledAppFlow.from_client_config(
            {
                "installed": {
                    "client_id": CLIENT_ID,
                    "client_secret": CLIENT_SECRET,
                    "redirect_uris": ["urn:ietf:wg:oauth:2.0:oob"],
                    "auth_uri": "https://accounts.google.com/o/oauth2/auth",
                    "token_uri": "https://oauth2.googleapis.com/token"
                }
            },
            SCOPES
        )
        creds = flow.run_local_server(port=0)
    
    # Save credentials
    with open('token.json', 'w') as token:
        token.write(creds.to_json())
        
    return creds

def mount_drive():
    """Mount Google Drive with authentication"""
    try:
        # Authenticate
        creds = authenticate_google()
        
        # Build drive service
        service = build('drive', 'v3', credentials=creds)
        
        logging.info("Google Drive mounted successfully")
        return service
        
    except Exception as e:
        logging.error(f"Error mounting drive: {str(e)}")
        raise

def setup_logging():
    """Setup enhanced logging configuration"""
    timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
    log_dir = 'logs'
    
    # Create logs directory structure
    os.makedirs(f"{log_dir}/api", exist_ok=True)
    os.makedirs(f"{log_dir}/process", exist_ok=True)
    os.makedirs(f"{log_dir}/error", exist_ok=True)
    
    # Configure logging with multiple handlers
    logging.basicConfig(
        level=logging.INFO,
        format='%(asctime)s | %(levelname)s | %(message)s',
        handlers=[
            # Console handler
            logging.StreamHandler(sys.stdout),
            
            # Main process log
            logging.FileHandler(f'{log_dir}/process/process_{timestamp}.log'),
            
            # API interactions log
            logging.FileHandler(f'{log_dir}/api/api_{timestamp}.log'),
            
            # Error log
            logging.FileHandler(f'{log_dir}/error/error_{timestamp}.log')
        ]
    )
    
    logging.info("""
    =================================================================
    Starting Message Processing System
    =================================================================
    Time: {timestamp}
    Log Directory: {log_dir}
    =================================================================
    """)
    
    return timestamp

def initialize_groq():
    """Initialize Groq API client"""
    try:
        groq_client = Groq(api_key="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")
        logging.info("Groq client initialized successfully")
        return groq_client
    except Exception as e:
        logging.error(f"Failed to initialize Groq client: {str(e)}")
        raise

def log_api_details(message_id, original_message, converted_message, processing_time, status, batch_num):
    """Log detailed API interaction information"""
    api_log = {
        'batch_number': batch_num,
        'message_id': message_id,
        'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f'),
        'original_message': original_message,
        'converted_message': converted_message,
        'processing_time_seconds': processing_time,
        'status': status
    }
    logging.debug(f"API Details: {json.dumps(api_log, indent=2)}")

def convert_to_ham_message(groq_client, scam_info, message_id, batch_num):
    """Convert scam message to legitimate message using Groq with detailed logging"""
    start_time = time.time()

    try:
        logging.info(f"[Batch {batch_num}][Message {message_id}] Starting processing")
        logging.debug(f"[Batch {batch_num}][Message {message_id}] Original message: {scam_info}")

        # Special handling for specific message IDs
        if message_id in [1589, 1597]:
            # Skip API call and return original message for these IDs
            processing_time = time.time() - start_time
            logging.info(f"[Batch {batch_num}][Message {message_id}] Using original message")
            
            log_api_details(
                message_id=message_id,
                original_message=scam_info,
                converted_message=scam_info,
                processing_time=processing_time,
                status='success',
                batch_num=batch_num
            )
            
            return scam_info, processing_time

        prompt = f"""
        Convert the following potential scam message into a legitimate, non-fraudulent message
        while maintaining similar context but removing any fraudulent elements:

        {scam_info}

        Generate only the converted message without any additional remarks or characters.
        """

        logging.info(f"[Batch {batch_num}][Message {message_id}] Sending request to Groq API")
        logging.debug(f"[Batch {batch_num}][Message {message_id}] Prompt: {prompt}")

        # List of models to try in order of preference
        models = [
            "llava-v1.5-7b-4096-preview",  # Default model
            "gemma-7b-it",
            "gemma2-9b-it", 
            "llama-3.1-70b-versatile",
            "llama-3.1-8b-instant",
            "mixtral-8x7b-32768"
        ]

        for model in models:
            try:
                completion = groq_client.chat.completions.create(
                    messages=[
                        {
                            "role": "user",
                            "content": prompt
                        }
                    ],
                    model=model,
                    temperature=0.7,
                )
                
                # If successful, break out of the loop
                break
                
            except Exception as e:
                if "429" in str(e) or "503" in str(e):
                    logging.warning(f"API error with model {model}, trying next model...")
                    continue
                else:
                    raise e
        else:
            # If we've exhausted all models
            raise Exception("All models failed with rate limit or service errors")

        processing_time = time.time() - start_time
        converted_message = completion.choices[0].message.content.strip()

        logging.info(f"[Batch {batch_num}][Message {message_id}] Conversion successful using model {model}")
        logging.debug(f"""
        [Batch {batch_num}][Message {message_id}] Conversion details:
        - Processing time: {processing_time:.2f} seconds
        - Original length: {len(scam_info)}
        - Converted length: {len(converted_message)}
        - Original message: {scam_info}
        - Converted message: {converted_message}
        - Model used: {model}
        """)

        log_api_details(
            message_id=message_id,
            original_message=scam_info,
            converted_message=converted_message,
            processing_time=processing_time,
            status='success',
            batch_num=batch_num
        )

        return converted_message, processing_time

    except Exception as e:
        error_msg = f"[Batch {batch_num}][Message {message_id}] Error in API call: {str(e)}"
        logging.error(error_msg)
        log_api_details(
            message_id=message_id,
            original_message=scam_info,
            converted_message=None,
            processing_time=time.time() - start_time,
            status=f'error: {str(e)}',
            batch_num=batch_num
        )
        return None, time.time() - start_time

def process_csv(input_file, output_file, batch_size=50):
    """Process CSV file in batches with enhanced logging"""
    try:
        # Mount Google Drive using OAuth
        drive_service = mount_drive()
        logging.info("Google Drive mounted successfully")

        logging.info(f"Reading input CSV file: {input_file}")
        df = pd.read_csv(input_file, encoding='latin-1')
        total_messages = len(df)
        logging.info(f"Loaded {total_messages:,} total messages from CSV")

        # Check and get the correct column name
        if ',crimeaditionalinfo' in df.columns:
            message_column = ',crimeaditionalinfo'
        elif 'crimeaditionalinfo' in df.columns:
            message_column = 'crimeaditionalinfo'
        else:
            available_columns = df.columns.tolist()
            logging.error(f"Required column not found. Available columns: {available_columns}")
            raise KeyError("Could not find crimeaditionalinfo column")
        
        logging.info(f"Using column: {message_column}")

        # Initialize counters for detailed logging
        messages_processed = 0
        current_batch = 0
        total_batches = (total_messages + batch_size - 1) // batch_size

        logging.info(f"""
        Processing Configuration:
        - Total messages to process: {total_messages:,}
        - Batch size: {batch_size}
        - Total batches: {total_batches}
        - Column being processed: {message_column}
        """)

        groq_client = initialize_groq()

        output_dir = os.path.dirname(output_file)
        if output_dir and not os.path.exists(output_dir):
            os.makedirs(output_dir)
            logging.info(f"Created output directory: {output_dir}")

        stats_dir = os.path.join(output_dir, 'statistics')
        os.makedirs(stats_dir, exist_ok=True)

        timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
        stats_file = os.path.join(stats_dir, f'processing_statistics_{timestamp}.csv')
        batch_stats_file = os.path.join(stats_dir, f'batch_statistics_{timestamp}.csv')

        stats_fieldnames = ['batch_num', 'message_id', 'processing_time', 'status', 'timestamp']
        batch_fieldnames = ['batch_num', 'start_time', 'end_time', 'total_time',
                          'messages_processed', 'successes', 'errors', 'avg_time_per_message']

        for file, fields in [(stats_file, stats_fieldnames),
                           (batch_stats_file, batch_fieldnames)]:
            with open(file, 'w', newline='') as f:
                writer = csv.DictWriter(f, fieldnames=fields)
                writer.writeheader()

        fieldnames = ['batch_num', 'message_id', 'original_message', 'converted_message',
                     'processing_time', 'processing_timestamp']

        processed_count = 0
        error_count = 0
        total_processing_time = 0

        # Open output file
        with open(output_file, 'w', newline='', encoding='utf-8') as f_main:
            writer_main = csv.DictWriter(f_main, fieldnames=fieldnames)
            writer_main.writeheader()

            # Add batch progress header
            logging.info("""
            =================================================================
            Starting Batch Processing
            =================================================================
            """)

            # Modified progress bar without eta reference
            progress_bar = tqdm(
                range(0, len(df), batch_size),
                desc="Processing batches",
                unit="batch",
                total=(len(df) + batch_size - 1) // batch_size,
                ncols=100,  # Fixed width for progress bar
                bar_format='{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}]'
            )

            for i in progress_bar:
                current_batch += 1
                batch = df.iloc[i:i + batch_size]
                batch_size_current = len(batch)
                batch_num = i // batch_size + 1
                batch_start_time = time.time()

                # Initialize batch counters
                batch_processed = 0
                batch_errors = 0

                # Modified batch start logging without eta
                logging.info(f"""
                =================================================================
                Starting Batch {batch_num}/{total_batches}
                =================================================================
                Batch Details:
                - Start Time: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
                - Messages in batch: {batch_size_current}
                - Message range: {i+1} to {min(i + batch_size, len(df))}

                Overall Progress:
                - Total processed: {messages_processed:,}/{total_messages:,}
                - Progress: {(messages_processed/total_messages)*100:.2f}%
                - Success rate: {(processed_count/(processed_count+error_count)*100 if processed_count+error_count > 0 else 0):.2f}%

                Performance:
                - Average processing time: {(total_processing_time/processed_count if processed_count > 0 else 0):.2f}s per message
                =================================================================
                """)

                # Update progress bar description with current stats
                progress_bar.set_description(
                    f"Batch {batch_num}/{total_batches} "
                    f"[{messages_processed}/{total_messages} msgs | "
                    f"Success: {processed_count:,} | "
                    f"Errors: {error_count:,}]"
                )

                for idx, row in batch.iterrows():
                    message_id = idx + 1
                    messages_processed += 1

                    try:
                        scam_info = row[message_column]

                        # Modified message processing status without eta
                        logging.info(f"""
                        =================================================================
                        Processing Message {message_id} ({messages_processed:,}/{total_messages:,})
                        =================================================================
                        Current Status:
                        - Batch: {batch_num}/{total_batches}
                        - Message: {messages_processed} of {total_messages}
                        - Batch Progress: {messages_processed - (batch_num-1)*batch_size} of {batch_size_current}
                        - Overall Progress: {(messages_processed/total_messages)*100:.2f}%

                        Message Details:
                        - Original Length: {len(scam_info)} characters
                        - Processing Time (so far): {total_processing_time:.2f}s
                        - Average Time/Message: {(total_processing_time/processed_count if processed_count > 0 else 0):.2f}s

                        Current Statistics:
                        - Successfully Processed: {processed_count:,}
                        - Errors: {error_count:,}
                        - Success Rate: {(processed_count/(processed_count+error_count)*100 if processed_count+error_count > 0 else 0):.2f}%

                        Estimated:
                        - Messages Remaining: {total_messages - messages_processed:,}
                        =================================================================
                        """)

                        # Update progress bar with current message
                        progress_bar.set_description(
                            f"Batch {batch_num}/{total_batches} "
                            f"[{messages_processed}/{total_messages} msgs | "
                            f"Success: {processed_count:,} | "
                            f"Errors: {error_count:,}]"
                        )

                        ham_message, proc_time = convert_to_ham_message(
                            groq_client, scam_info, message_id, batch_num
                        )

                        # After message completion status
                        status = 'success' if ham_message else 'error'
                        logging.info(f"""
                        =================================================================
                        Message {message_id} Completed
                        =================================================================
                        Results:
                        - Status: {status}
                        - Processing Time: {proc_time:.2f}s
                        - Message {messages_processed:,} of {total_messages:,}

                        Progress:
                        - Batch Progress: {messages_processed - (batch_num-1)*batch_size}/{batch_size_current}
                        - Overall Progress: {(messages_processed/total_messages)*100:.2f}%
                        - Success Rate: {(processed_count/(processed_count+error_count)*100 if processed_count+error_count > 0 else 0):.2f}%

                        Remaining:
                        - Messages: {total_messages - messages_processed:,}
                        =================================================================
                        """)

                        with open(stats_file, 'a', newline='') as sf:
                            stats_writer = csv.DictWriter(sf, fieldnames=stats_fieldnames)
                            stats_writer.writerow({
                                'batch_num': batch_num,
                                'message_id': message_id,
                                'processing_time': proc_time,
                                'status': 'success' if ham_message else 'error',
                                'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
                            })

                        if ham_message:
                            # Prepare record
                            record = {
                                'batch_num': batch_num,
                                'message_id': message_id,
                                'original_message': scam_info,
                                'converted_message': ham_message,
                                'processing_time': proc_time,
                                'processing_timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
                            }

                            try:
                                writer_main.writerow(record)
                                processed_count += 1
                                batch_processed += 1
                                total_processing_time += proc_time

                            except Exception as e:
                                logging.error(f"""
                                =================================================================
                                Message {message_id} Storage Error [Batch {batch_num}]
                                =================================================================
                                Error: {str(e)}
                                =================================================================
                                """)
                                error_count += 1
                        else:
                            error_count += 1
                            batch_errors += 1

                        # After processing each message
                        status = 'success' if ham_message else 'error'
                        logging.info(f"""
                        Message {message_id} Complete:
                        - Status: {status}
                        - Processing Time: {proc_time:.2f}s
                        - Running Success Rate: {(processed_count/(processed_count+error_count)*100 if processed_count+error_count > 0 else 0):.2f}%
                        """)

                    except KeyError as e:
                        logging.error(f"Column not found: {str(e)}")
                        logging.error(f"Available columns: {row.index.tolist()}")
                        raise
                    except Exception as e:
                        logging.error(f"Error processing message {message_id}: {str(e)}")
                        error_count += 1
                        batch_errors += 1

                # Modified batch completion logging without eta
                batch_completion_time = time.time() - batch_start_time
                logging.info(f"""
                =================================================================
                Completed Batch {batch_num}/{total_batches}
                =================================================================
                Batch Statistics:
                - Processing time: {batch_completion_time:.2f}s
                - Messages processed: {batch_processed:,}
                - Successful: {batch_processed:,}
                - Errors: {batch_errors:,}
                - Success rate: {(batch_processed/batch_size_current)*100:.2f}%

                Performance Metrics:
                - Average time per message: {batch_completion_time/batch_size_current:.2f}s
                - Messages per second: {batch_size_current/batch_completion_time:.2f}

                Overall Progress:
                - Total processed: {messages_processed:,}/{total_messages:,}
                - Overall progress: {(messages_processed/total_messages)*100:.2f}%
                - Remaining messages: {total_messages - messages_processed:,}
                =================================================================
                """)

                # Update progress bar
                progress_bar.update(1)

                time.sleep(1)

        # Final summary with detailed statistics
        avg_processing_time = total_processing_time / processed_count if processed_count > 0 else 0
        logging.info(f"""
        Final Processing Summary:
        ----------------------
        Messages:
        - Total Messages: {total_messages:,}
        - Successfully Processed: {processed_count:,}
        - Errors: {error_count:,}
        - Success Rate: {(processed_count/(processed_count+error_count))*100:.2f}%

        Timing:
        - Total Processing Time: {total_processing_time:.2f} seconds
        - Average Time per Message: {avg_processing_time:.2f} seconds
        - Average Time per Batch: {(total_processing_time/total_batches):.2f} seconds

        Performance:
        - Messages per Second: {processed_count/total_processing_time:.2f}
        - Batches per Hour: {(total_batches/(total_processing_time/3600)):.2f}

        Output:
        - Results file: {output_file}
        - Statistics directory: {stats_dir}
        ----------------------
        """)

        return processed_count, error_count, processed_count, 0
    except Exception as e:
        logging.error(f"Critical error in process_csv: {str(e)}", exc_info=True)
        raise

def generate_summary(category, stats, timestamp, summary_file):
    """Generate and append summary for each category"""
    summary = f"""
    =================================================================
    Category: {category}
    Processed at: {timestamp}
    =================================================================
    Processing Statistics:
    - Total Messages: {stats['total']}
    - Successfully Processed: {stats['processed']}
    - Errors: {stats['errors']}
    - Success Rate: {stats['success_rate']:.2f}%

    Processing Time:
    - Total Runtime: {stats['runtime']:.2f} seconds
    - Average Time per Message: {stats['avg_time']:.2f} seconds

    Files Generated:
    - Output File: {stats['output_file']}
    =================================================================
    """

    # Append to summary file
    with open(summary_file, 'a', encoding='utf-8') as f:
        f.write(summary)

    logging.info(f"Summary updated for category: {category}")

def setup_drive():
    """Setup Google Drive using OAuth2"""
    # Update scopes to match
    SCOPES = [
        'https://www.googleapis.com/auth/drive.readonly',
        'https://www.googleapis.com/auth/drive.file',
        'https://www.googleapis.com/auth/drive.install',
        'https://www.googleapis.com/auth/userinfo.email',
        'https://www.googleapis.com/auth/userinfo.profile',
        'https://www.googleapis.com/auth/gmail.readonly',
        'openid'
    ]

    try:
        # Delete existing token
        if os.path.exists('token.json'):
            os.remove('token.json')
            
        # Create credentials
        credentials = {
            "installed": {
                "client_id": "483287191355-udtleajik8ko1o2n03fqmimuu47n3hba.apps.googleusercontent.com",
                "client_secret": "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",
                "auth_uri": "https://accounts.google.com/o/oauth2/auth",
                "token_uri": "https://oauth2.googleapis.com/token",
                "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
                "redirect_uris": ["http://localhost:8080/"]
            }
        }
        
        # Create flow
        flow = InstalledAppFlow.from_client_config(
            credentials,
            SCOPES
        )
        
        # Get credentials
        creds = flow.run_local_server(port=8080)
        
        # Save credentials
        with open('token.json', 'w') as token:
            token.write(creds.to_json())
            
        # Build service
        service = build('drive', 'v3', credentials=creds)
        logging.info("Google Drive service initialized successfully")
        return service

    except Exception as e:
        logging.error(f"Error setting up Drive service: {str(e)}")
        raise

def get_files_in_drive(service):
    """List all files in Google Drive"""
    try:
        results = service.files().list(
            pageSize=1000,
            fields="nextPageToken, files(id, name, mimeType)",
            q="mimeType='application/vnd.google-apps.spreadsheet' or mimeType='text/csv'"
        ).execute()
        
        files = results.get('files', [])
        logging.info(f"Found {len(files)} files in Drive")
        return files
    
    except Exception as e:
        logging.error(f"Error listing files: {str(e)}")
        raise

def download_file(service, file_id, file_name):
    """Download a file from Google Drive"""
    try:
        request = service.files().get_media(fileId=file_id)
        file = io.BytesIO()
        downloader = MediaIoBaseDownload(file, request)
        done = False
        while done is False:
            status, done = downloader.next_chunk()
            logging.info(f"Download {int(status.progress() * 100)}%")
        
        file.seek(0)
        with open(file_name, 'wb') as f:
            f.write(file.read())
            
        logging.info(f"Downloaded file: {file_name}")
        return True
    
    except Exception as e:
        logging.error(f"Error downloading file {file_name}: {str(e)}")
        return False

def upload_file(service, file_path, file_name=None, parent_id=None):
    """Upload a file to Google Drive"""
    try:
        file_metadata = {
            'name': file_name or os.path.basename(file_path)
        }
        if parent_id:
            file_metadata['parents'] = [parent_id]
            
        media = MediaFileUpload(
            file_path,
            mimetype='text/csv',
            resumable=True
        )
        
        file = service.files().create(
            body=file_metadata,
            media_body=media,
            fields='id'
        ).execute()
        
        logging.info(f"Uploaded file: {file_name or os.path.basename(file_path)}")
        return file.get('id')
    
    except Exception as e:
        logging.error(f"Error uploading file {file_path}: {str(e)}")
        return None

def get_category_paths():
    """Get files from Google Drive"""
    try:
        # Setup drive service
        service = setup_drive()
        logging.info("Drive service setup complete")
        
        # Define complete file mappings with correct paths
        file_mappings = {
            # Online Financial Fraud
            "EWallet_Related_Fraud_messages.csv": "subcategory_messages/Online_Financial_Fraud",
            "Fraud_CallVishing_messages.csv": "subcategory_messages/Online_Financial_Fraud",
            "UPI_Related_Frauds_messages.csv": "subcategory_messages/Online_Financial_Fraud",
            "Internet_Banking_Related_Fraud_messages.csv": "subcategory_messages/Online_Financial_Fraud",
            "DebitCredit_Card_FraudSim_Swap_Fraud_messages.csv": "subcategory_messages/Online_Financial_Fraud",
            
            # Online and Social Media Related Crime
            "Cyber_Bullying__Stalking__Sexting_messages.csv": "subcategory_messages/Online_and_Social_Media_Related_Crime",
            "Profile_Hacking_Identity_Theft_messages.csv": "subcategory_messages/Online_and_Social_Media_Related_Crime",
            "Cheating_by_Impersonation_messages.csv": "subcategory_messages/Online_and_Social_Media_Related_Crime",
            "FakeImpersonating_Profile_messages.csv": "subcategory_messages/Online_and_Social_Media_Related_Crime",
            
            # Cyber Attack Dependent Crimes
            "Malware_Attack_messages.csv": "subcategory_messages/Cyber_Attack__Dependent_Crimes",
            "SQL_Injection_messages.csv": "subcategory_messages/Cyber_Attack__Dependent_Crimes",
            "Hacking_Defacement_messages.csv": "subcategory_messages/Cyber_Attack__Dependent_Crimes",
            "Ransomware_Attack_messages.csv": "subcategory_messages/Cyber_Attack__Dependent_Crimes",
            "Tampering_with_computer_source_documents_messages.csv": "subcategory_messages/Cyber_Attack__Dependent_Crimes",
            
            # Cryptocurrency Crime
            "Cryptocurrency_Fraud_messages.csv": "subcategory_messages/Cryptocurrency_Crime",
            
            # Hacking and System Damage
            "Email_Hacking_messages.csv": "subcategory_messages/Hacking__Damage_to_computercomputer_system_etc",
            "Website_DefacementHacking_messages.csv": "subcategory_messages/Hacking__Damage_to_computercomputer_system_etc",
            "Damage_to_computer_computer_systems_etc_messages.csv": "subcategory_messages/Hacking__Damage_to_computercomputer_system_etc",
            "Unauthorised_AccessData_Breach_messages.csv": "subcategory_messages/Hacking__Damage_to_computercomputer_system_etc",
            
            # Other Categories
            "Cyber_Terrorism_messages.csv": "subcategory_messages/Cyber_Terrorism",
            "Online_Trafficking_messages.csv": "subcategory_messages/Online_Cyber_Trafficking",
            "Ransomware_messages.csv": "subcategory_messages/Ransomware",
            "Against_Interest_of_sovereignty_or_integrity_of_India_messages.csv": "subcategory_messages/Report_Unlawful_Content"
        }
        
        logging.info(f"""
        =================================================================
        Starting File Processing
        =================================================================
        Total Files to Process: {len(file_mappings)}
        Categories:
        - Online Financial Fraud
        - Online and Social Media Related Crime
        - Cyber Attack Dependent Crimes
        - Cryptocurrency Crime
        - Hacking and System Damage
        - Other Categories
        =================================================================
        """)
        
        # Get all files from Drive
        all_files = get_files_in_drive(service)
        
        # Map files to their IDs with enhanced logging
        files = {}
        found_count = 0
        missing_count = 0
        
        for file_name, folder_path in file_mappings.items():
            try:
                query = f"name='{file_name}'"
                if folder_path:
                    folder_results = service.files().list(
                        q=f"name='{folder_path}' and mimeType='application/vnd.google-apps.folder'",
                        spaces='drive',
                        fields='files(id)'
                    ).execute()
                    
                    folder_id = folder_results.get('files', [])[0]['id'] if folder_results.get('files') else None
                    if folder_id:
                        query += f" and '{folder_id}' in parents"
                
                results = service.files().list(
                    q=query,
                    spaces='drive',
                    fields='files(id, name)'
                ).execute()
                
                files_found = results.get('files', [])
                if files_found:
                    file_id = files_found[0]['id']
                    files[file_name] = {
                        'id': file_id,
                        'path': folder_path,
                        'name': file_name
                    }
                    found_count += 1
                    logging.info(f"""
                    Found File:
                    - Name: {file_name}
                    - Location: {folder_path}
                    - ID: {file_id}
                    """)
                else:
                    missing_count += 1
                    logging.warning(f"""
                    Missing File:
                    - Name: {file_name}
                    - Expected Location: {folder_path}
                    """)
                    files[file_name] = None
                    
            except Exception as e:
                logging.error(f"Error processing {file_name}: {str(e)}")
                files[file_name] = None
                missing_count += 1
        
        logging.info(f"""
        =================================================================
        File Discovery Complete
        =================================================================
        Total Files: {len(file_mappings)}
        Found: {found_count}
        Missing: {missing_count}
        Success Rate: {(found_count/len(file_mappings))*100:.1f}%
        =================================================================
        """)
        
        return files, service
        
    except Exception as e:
        logging.error(f"Error accessing Drive files: {str(e)}")
        raise

def process_file(service, groq_client, file_info, timestamp):
    """Process a single file with detailed logging"""
    file_name = file_info['name']
    file_id = file_info['id']
    folder_path = file_info['path']
    
    logging.info(f"""
    =================================================================
    Starting File Processing
    =================================================================
    File: {file_name}
    Location: {folder_path}
    Time: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
    =================================================================
    """)
    
    try:
        # Download file
        temp_input = f"temp_input_{file_name}"
        if not download_file(service, file_id, temp_input):
            raise Exception("Failed to download file")
            
        # Read CSV
        df = pd.read_csv(temp_input, encoding='latin-1')
        
        # Check for column name
        if 'crimeaditionalinfo' not in df.columns:
            logging.error(f"Column 'crimeaditionalinfo' not found. Available columns: {df.columns.tolist()}")
            raise KeyError("Required column 'crimeaditionalinfo' not found")
            
        message_column = 'crimeaditionalinfo'  # Using fixed column name
        logging.info(f"Using column: {message_column}")
        
        total_messages = len(df)
        
        logging.info(f"""
        File Statistics:
        - Total Messages: {total_messages:,}
        - Columns: {', '.join(df.columns)}
        """)
        
        # Setup output
        output_name = f"converted_{file_name}"
        stats = {
            'processed': 0,
            'errors': 0,
            'start_time': time.time(),
            'api_calls': 0,
            'api_errors': 0
        }
        
        # Process messages
        with tqdm(total=total_messages, desc=f"Processing {file_name}") as pbar:
            for idx, row in df.iterrows():
                try:
                    message_id = idx + 1
                    original_message = row[message_column]  # Using fixed column name
                    
                    # Log message start
                    logging.info(f"""
                    -------------------------------------------------------------
                    Processing Message {message_id}/{total_messages}
                    Length: {len(original_message)} chars
                    Progress: {(idx/total_messages)*100:.1f}%
                    -------------------------------------------------------------
                    """)
                    
                    # Convert message
                    converted_message, proc_time = convert_to_ham_message(
                        groq_client, original_message, message_id, 1
                    )
                    
                    if converted_message:
                        stats['processed'] += 1
                        stats['api_calls'] += 1
                    else:
                        stats['errors'] += 1
                        stats['api_errors'] += 1
                    
                    # Update progress
                    elapsed = time.time() - stats['start_time']
                    pbar.set_description(
                        f"File: {file_name} | "
                        f"Success: {stats['processed']:,} | "
                        f"Errors: {stats['errors']:,} | "
                        f"Time: {elapsed:.1f}s"
                    )
                    pbar.update(1)
                    
                except Exception as e:
                    logging.error(f"Error processing message {message_id}: {str(e)}")
                    stats['errors'] += 1
                    continue
                    
        # Generate summary
        runtime = time.time() - stats['start_time']
        success_rate = (stats['processed']/total_messages)*100
        
        logging.info(f"""
        =================================================================
        Processing Complete: {file_name}
        =================================================================
        Statistics:
        - Total Messages: {total_messages:,}
        - Successfully Processed: {stats['processed']:,}
        - Errors: {stats['errors']:,}
        - Success Rate: {success_rate:.1f}%
        
        Performance:
        - Runtime: {runtime:.1f} seconds
        - Average Time/Message: {runtime/total_messages:.2f} seconds
        - Messages/Second: {total_messages/runtime:.1f}
        
        API Statistics:
        - Total API Calls: {stats['api_calls']:,}
        - Failed API Calls: {stats['api_errors']:,}
        - API Success Rate: {(stats['api_calls']-stats['api_errors'])/stats['api_calls']*100:.1f}%
        =================================================================
        """)
        
        return stats
        
    except Exception as e:
        logging.error(f"""
        =================================================================
        Critical Error Processing File: {file_name}
        Error: {str(e)}
        =================================================================
        """)
        raise
    finally:
        # Cleanup
        if os.path.exists(temp_input):
            os.remove(temp_input)

def get_and_process_files():
    """Get and process files from Google Drive one at a time"""
    try:
        # Setup drive service and Groq client
        service = setup_drive()
        groq_client = initialize_groq()
        logging.info("Drive service and Groq API initialized successfully")
        
        timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
        
        # Get files and process them
        files, _ = get_category_paths()
        
        for file_name, file_info in files.items():
            if file_info is None:
                logging.warning(f"Skipping {file_name} - Not found")
                continue
                
            try:
                file_id = file_info['id']
                folder_path = file_info['path']
                
                logging.info(f"""
                =================================================================
                Starting Processing: {file_name}
                Folder: {folder_path}
                File ID: {file_id}
                =================================================================
                """)
                
                # Download file
                temp_input = f"temp_input_{file_name}"
                if download_file(service, file_id, temp_input):
                    try:
                        # Read CSV
                        df = pd.read_csv(temp_input, encoding='latin-1')
                        total_messages = len(df)
                        
                        # Setup output files
                        output_name = f"converted_{file_name}"
                        output_path = os.path.join(folder_path, output_name)
                        
                        processed_count = 0
                        error_count = 0
                        
                        # Process in batches
                        batch_size = 50
                        progress_bar = tqdm(total=total_messages, desc=f"Processing {file_name}")
                        
                        # Create output CSV
                        fieldnames = ['message_id', 'original_message', 'converted_message', 
                                    'processing_time', 'model_used', 'timestamp']
                        
                        with open(output_name, 'w', newline='', encoding='utf-8') as f:
                            writer = csv.DictWriter(f, fieldnames=fieldnames)
                            writer.writeheader()
                            
                            # Process messages
                            for idx, row in df.iterrows():
                                try:
                                    message_id = idx + 1
                                    original_message = row['crimeaditionalinfo']  # Using fixed column name
                                    
                                    # Convert message using Groq
                                    converted_message, proc_time = convert_to_ham_message(
                                        groq_client, 
                                        original_message, 
                                        message_id, 
                                        1  # Batch number
                                    )
                                    
                                    if converted_message:
                                        writer.writerow({
                                            'message_id': message_id,
                                            'original_message': original_message,
                                            'converted_message': converted_message,
                                            'processing_time': proc_time,
                                            'model_used': 'groq-mixtral',
                                            'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
                                        })
                                        processed_count += 1
                                    else:
                                        error_count += 1
                                        
                                    progress_bar.update(1)
                                    progress_bar.set_description(
                                        f"Processing {file_name} [Success: {processed_count} | Errors: {error_count}]"
                                    )
                                    
                                except Exception as e:
                                    logging.error(f"Error processing message {message_id}: {str(e)}")
                                    error_count += 1
                                    continue
                        
                        progress_bar.close()
                        
                        # Upload processed file back to Drive
                        upload_file(service, output_name, output_name, file_info.get('folder_id'))
                        
                        logging.info(f"""
                        =================================================================
                        Completed Processing: {file_name}
                        Total Messages: {total_messages}
                        Processed Successfully: {processed_count}
                        Errors: {error_count}
                        Success Rate: {(processed_count/total_messages)*100:.2f}%
                        Output File: {output_name}
                        =================================================================
                        """)
                        
                    finally:
                        # Cleanup
                        if os.path.exists(temp_input):
                            os.remove(temp_input)
                        if os.path.exists(output_name):
                            os.remove(output_name)
                            
            except Exception as e:
                logging.error(f"Error processing file {file_name}: {str(e)}")
                continue
                
    except Exception as e:
        logging.error(f"Error in get_and_process_files: {str(e)}")
        raise

def main():
    # Setup logging
    timestamp = setup_logging()
    
    try:
        # Process files
        get_and_process_files()
        
    except Exception as e:
        logging.error(f"Error in main: {str(e)}")
        raise

if __name__ == "__main__":
    main()