import spacy
import mysql.connector
import os
import sys
from collections import Counter

# === Load spaCy model ===
nlp = spacy.load("en_core_web_sm")

# === DB Credentials ===
DB_HOST = os.getenv("DB_HOST", "localhost")
DB_PORT = int(os.getenv("DB_PORT", 3306))
DB_USER = os.getenv("DB_USER", "emailuser")
DB_PASSWORD = os.getenv("DB_PASSWORD", "miguel33020")
DB_NAME = os.getenv("DB_NAME", "emailassistant")

# === Connect to DB ===
conn = mysql.connector.connect(
    host=DB_HOST,
    port=DB_PORT,
    user=DB_USER,
    password=DB_PASSWORD,
    database=DB_NAME
)
cursor = conn.cursor(dictionary=True)

# === Logging Helper ===
def log_event(cursor, level, source, message):
    cursor.execute(
        "INSERT INTO logs (level, source, message) VALUES (%s, %s, %s)",
        (level, source, message)
    )

# === Subject-Based Summarization ===
def summarize_subject(subject):
    doc = nlp(subject)
    keywords = [token.text for token in doc if token.is_alpha and not token.is_stop]
    if not keywords:
        return subject, 1.0  # fallback to raw subject

    # Prioritize noun chunks that include keywords
    noun_chunks = list(doc.noun_chunks)
    chunks = [chunk.text for chunk in noun_chunks if any(tok.text in keywords for tok in chunk)]

    # Combine and limit summary length
    compressed = " ".join(chunks or keywords)
    compressed_words = compressed.split()
    subject_word_count = len(subject.split())
    summary = " ".join(compressed_words[:max(1, subject_word_count - 1)]).strip()

    # Confidence is relative to subject word count
    confidence = round(len(summary.split()) / max(1, subject_word_count), 2)

    # Fallback if summary is too short or confidence too low
    if len(summary.split()) < 2 or confidence < 0.3:
        return subject, 1.0

    return summary, confidence

# === Fetch emails with NULL ai_summary ===
cursor.execute("SELECT id, subject FROM emails")
emails = cursor.fetchall()

# === Main Processing Loop ===
# === Main Processing Loop ===
for email in emails:
    email_id = email["id"]
    subject = email["subject"]

    if not subject or not subject.strip():
        log_event(cursor, "WARNING", "subject_summarizer", f"Skipped empty subject for email ID {email_id}")
        continue

    try:
        summary, confidence = summarize_subject(subject)

        cursor.execute("""
            UPDATE emails
            SET ai_summary = %s,
                ai_confidence = %s
            WHERE id = %s
        """, (summary, confidence, email_id))

        log_event(cursor, "INFO", "subject_summarizer", f"Subject summarized for email ID {email_id}")
        print(f"✅ Subject summarized for email {email_id} (confidence: {confidence})")

    except Exception as e:
        log_event(cursor, "ERROR", "subject_summarizer", f"Error on email ID {email_id}: {str(e)}")
        print(f"❌ Error summarizing subject for email {email_id}: {e}")


# === Commit & Close ===
conn.commit()
cursor.close()
conn.close()