app.py

import gradio as gr
import threading
from email.header import decode_header
import mysql.connector
from transformers import pipeline  # Assuming you'll use Hugging Face pipeline
import email, imaplib, json, time
import logging


# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

# Email and database configuration
IMAP_SERVER = 'imap.gmail.com'
EMAIL_ADDRESS = '[email protected]'
PASSWORD = 'gclc wsnx kywt uvqy'  # Store this securely in production
DB_CONFIG = {
    'host': '0.tcp.in.ngrok.io',
    'port': 11329,
    'user': 'root',
    'password': '',  # Add the correct password
    'database': 'shipment_details'
}

# JSON format for extracted shipment details
output_format = {
    "origin": "",
    "destination": "",
    "expected_shipment_datetime": "",
    "types_of_service": "",
    "warehouse": "",
    "description": "",
    "quantities": "",
    "carrier_details": ""
}

# Prompt for LLM to process shipment-related emails
prompt = """
System prompt: You will be provided with an email containing shipment details. Your task is to extract specific information based on the given instructions.
Instructions:
1. Focus only on extracting details about future shipments, ignore irrelevant information.
2. Output should be in JSON format. Missing information should be marked as null.
3. Extract the following:
    - origin
    - destination
    - expected_shipment_datetime (format: yyyy-mm-dd hh:mm:ss)
    - types_of_service (AIR, LCL, FCL)
    - warehouse
    - description (max 100 words)
    - quantities
    - carrier_details
4. The output should be formatted as follows:
{
    "origin": "",
    "destination": "",
    "expected_shipment_datetime": "",
    "types_of_service": "",
    "warehouse": "",
    "description": "",
    "quantities": "",
    "carrier_details": ""
}
"""

# Function to insert extracted shipment details into MySQL database
def insert_data(extracted_details):
    try:
        # Initialize MySQL database connection
        mydb = mysql.connector.connect(**DB_CONFIG)
        cursor = mydb.cursor()

        # Skip insertion if all required fields are empty
        required_fields = [
            'origin', 'destination', 'expected_shipment_datetime',
            'types_of_service', 'warehouse', 'description',
            'quantities', 'carrier_details'
        ]
        if all(extracted_details.get(field) in ["", None] for field in required_fields):
            logger.info("Skipping insertion: All extracted values are empty.")
            return

        # Insert data into database
        sql = """
        INSERT INTO shipment_details (
            origin, destination, expected_shipment_datetime, types_of_service, 
            warehouse, description, quantities, carrier_details, 
            sender, receiver, cc, bcc, subject
        ) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
        """
        values = (
            extracted_details.get('origin'),
            extracted_details.get('destination'),
            extracted_details.get('expected_shipment_datetime'),
            extracted_details.get('types_of_service'),
            extracted_details.get('warehouse'),
            extracted_details.get('description'),
            extracted_details.get('quantities'),
            extracted_details.get('carrier_details'),
            extracted_details.get('sender'),
            extracted_details.get('receiver'),
            extracted_details.get('cc'),
            extracted_details.get('bcc'),
            extracted_details.get('subject')
        )
        cursor.execute(sql, values)
        mydb.commit()
        logger.info("Data inserted successfully.")

    except mysql.connector.Error as db_err:
        logger.error(f"Database error: {db_err}")
    except Exception as ex:
        logger.error(f"Error inserting data: {ex}")

# Function to extract shipment details using an LLM
def get_details(mail):
    try:
        # Initialize LLM model and tokenizer
        # Uncomment below if using Hugging Face models, or load your specific model accordingly
        # pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
        # output = pipe(f"{prompt}\n{mail}", max_new_tokens=200)

        # Assuming Llama model for completion (example)
        response = {  # Placeholder response for testing purposes
            "origin": "New York",
            "destination": "Los Angeles",
            "expected_shipment_datetime": "2024-10-20 12:00:00",
            "types_of_service": "AIR",
            "warehouse": "Warehouse 1",
            "description": "Electronics shipment",
            "quantities": "10",
            "carrier_details": "Carrier XYZ"
        }
        return json.dumps(response)  # Returning mock response for testing

    except Exception as ex:
        logger.error(f"Error generating details from LLM: {ex}")
        return None

# Function to read and process unread emails
def read_email():
    logging.info('Ready to read email...')
    try:
        logging.info('Connecting to IMAP server...')
        mail = imaplib.IMAP4_SSL(IMAP_SERVER)
        logger.info('mail ')
        mail.login(EMAIL_ADDRESS, PASSWORD)
        logger.info('logrd in ! ...')
        mail.select('inbox')
        logging.info('Selected inbox')
        status, messages = mail.search(None, 'UNSEEN')
        message_ids = messages[0].split()
        logging.info(f"Total unread emails: {len(message_ids)}")

        for message_id in message_ids:
            try:
                status, data = mail.fetch(message_id, '(RFC822)')
                raw_email = data[0][1]
                email_message = email.message_from_bytes(raw_email)

                # Extract metadata
                sender = email_message['From']
                receiver = email_message['To']
                cc = email_message.get('Cc', '')
                bcc = email_message.get('Bcc', '')
                subject = email_message['Subject']

                # Extract email body
                if email_message.is_multipart():
                    for part in email_message.walk():
                        if part.get_content_type() == 'text/plain':
                            email_body = part.get_payload(decode=True).decode('utf-8')
                            break
                else:
                    email_body = email_message.get_payload(decode=True).decode('utf-8')

                # Extract and store details
                extracted_details_str = get_details(email_body)
                extracted_details = json.loads(extracted_details_str)
                meta_data = {
                    'sender': sender, 'receiver': receiver, 'cc': cc, 'bcc': bcc, 'subject': subject
                }
                extracted_details.update(meta_data)
                insert_data(extracted_details)

            except Exception as e:
                logger.error(f"Error processing email {message_id}: {e}")

        mail.close()
        mail.logout()

    except Exception as e:
        logger.error(f"Error reading emails: {e}")

# Email processing loop variables
running = False
loop_thread = None

# Dummy function to simulate email processing (replace with actual email processing code)
def email_processing_loop():
    global running
    logger.info("Starting email processing loop...")
    while running:
        logger.info("Processing emails...")
        read_email()
        logger.info('waiting !...')
        time.sleep(10)  # Simulating a process that checks every 10 seconds

# Start the email processing loop
def start_processing():
    global running, loop_thread
    if not running:
        running = True
        loop_thread = threading.Thread(target=email_processing_loop, daemon=True)
        loop_thread.start()
    return update_status()

# Stop the email processing loop
def stop_processing():
    global running
    if running:
        running = False
    return update_status()

# Update the status of email processing
def update_status():
    return "Running" if running else "Stopped"

# Create Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# Email Processing")

    status_display = gr.Textbox(label="Email Processing Status", value=update_status(), interactive=False)

    start_button = gr.Button("Start Processing")
    stop_button = gr.Button("Stop Processing")

    # Link buttons to processing functions
    start_button.click(fn=start_processing, outputs=status_display)
    stop_button.click(fn=stop_processing, outputs=status_display)

    # Add a manual timer without the interval parameter
    # gr.Timer.every(2, fn=update_status, outputs=status_display)  # Run `update_status()` every 2 seconds
        
if __name__ == "__main__":
    logging.info('Starting project...')
    demo.launch()