import imaplib
import email
from transformers import BartForConditionalGeneration, BartTokenizer, pipeline
import torch
import email.header

model_name = 'facebook/bart-large-cnn'
tokenizer = BartTokenizer.from_pretrained(model_name)
model = BartForConditionalGeneration.from_pretrained(model_name)

sentiment_analyzer = pipeline('sentiment-analysis', model='distilbert-base-uncased')

mail = imaplib.IMAP4_SSL('imap.gmail.com') 
mail.login('dharsha5678@gmail.com', 'fwqw pnmq ulip umjl')
mail.select('inbox') 

def generate_summary(email_text, max_length=20):
    inputs = tokenizer([email_text], return_tensors='pt', max_length=1024, truncation=True)

    with torch.no_grad():
        summary_ids = model.generate(**inputs, max_length=max_length)

    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
    return summary

from datetime import date
today = date.today()
today_date = today.strftime("%d-%b-%Y")

status, email_ids = mail.search(None, 'SINCE', today_date)
email_ids = email_ids[0].split()

for email_id in email_ids:
    status, msg_data = mail.fetch(email_id, '(RFC822)')
    raw_email = msg_data[0][1]
    msg = email.message_from_bytes(raw_email)
    sender = msg['From']

    subject = email.header.decode_header(msg['Subject'])
    subject_str = ""
    for part, encoding in subject:
        if isinstance(part, bytes):
            if encoding:
                subject_str += part.decode(encoding)
            else:
                subject_str += part.decode('utf-8')
        else:
            subject_str += part

    body = ""

    if msg.is_multipart():
        for part in msg.walk():
            if part.get_content_type() == "text/plain":
                body = part.get_payload(decode=True).decode()
                break
    else:
        body = msg.get_payload(decode=True).decode()

    if body:
        word_count = len(body.split())
        if word_count < 10:
            summary = body
        else:
            if word_count < 50:
                summary = generate_summary(body, max_length=20)
            else:
                summary = generate_summary(body, max_length=50)

        sentiment_result = sentiment_analyzer(summary)
        label = sentiment_result[0]['label']
        score = sentiment_result[0]['score']

        if score >= 0.53:
            email_label = "Important"
        else:
            email_label = "Not Important"

        print(f"From: {sender}")
        print(f"Email Subject: {subject_str}")
        print(f"Generated Summary: {summary}")
        print(f"Sentiment Label: {email_label}")
        print(f"Sentiment Score: {score}")
        print("-" * 50)

mail.logout()