#!/usr/bin/env python
# coding: utf-8
import re
import numpy as np
import joblib
import sqlite3
from datetime import datetime, timedelta
import gradio as gr
from transformers import pipeline
from better_profanity import profanity

# ---------------------- Profanity Filter ----------------------
profanity.load_censor_words()

# ---------------------- SQLite DB Connection ----------------------
db = sqlite3.connect("anomaly1.db", check_same_thread=False)
cursor = db.cursor()

# ---------------------- Load Models ----------------------
try:
    spam_model = joblib.load("spam_classifier.pkl")
except Exception as e:
    raise RuntimeError(f"Failed to load spam model: {e}")

try:
    toxicity_model = pipeline("text-classification", model="unitary/toxic-bert")
except Exception as e:
    raise RuntimeError(f"Failed to load toxicity model: {e}")

# ---------------------- Anomaly Heuristic Score ----------------------
def basic_anomaly_score(text):
    score = 0
    if is_low_quality(text): score += 0.3
    if contains_suspicious_content(text): score += 0.3
    if is_nonsensical_structure(text): score += 0.2
    if len(text.split()) < 3: score += 0.2
    return score

# ---------------------- Rule-Based Checks ----------------------
def is_toxic(text):
    try:
        result = toxicity_model(text)[0]
        return result['label'].lower() == "toxic" and result['score'] > 0.7
    except Exception:
        return False

def is_low_quality(text):
    return len(text.strip()) < 10 or text.strip().isupper() or re.search(r"(.)\1{3,}", text)

def contains_suspicious_content(text):
    patterns = [r"\b\d{10}\b", r"\bcall me\b", r"\bwhatsapp\b", r"\bnumber\b", r"\bcontact\b", r"\bemail\b"]
    return any(re.search(p, text.lower()) for p in patterns)

def is_nonsensical_structure(text):
    patterns = [r"\bi am a\b", r"\bi will be a\b", r"\bthis is my\b"]
    return any(re.search(p, text.lower()) for p in patterns)

# ---------------------- Main Prediction Function ----------------------
def predict_review(text):
    text = text.strip()
    if not text:
        return "⚠️ Please enter a review."

    flags = []

    # Spam
    try:
        if spam_model.predict([text])[0]:
            flags.append("Spam")
    except Exception:
        flags.append("Spam Detection Failed")

    # Rule-based
    if is_toxic(text):
        flags.append("Toxic")
    if is_low_quality(text):
        flags.append("Low Quality")
    if contains_suspicious_content(text):
        flags.append("Suspicious")
    if is_nonsensical_structure(text):
        flags.append("Nonsensical")
    if len(text.split()) < 3:
        flags.append("Too Short")

    score = basic_anomaly_score(text)
    if score >= 0.5:
        flags.append("Anomalous")

    prediction = ", ".join(flags) if flags else "Normal"
    now = datetime.now()
    is_anomaly = 1 if "Anomalous" in flags else 0

    # ---------------------- Save in DB ----------------------
    try:
        cursor.execute("SELECT user_id FROM users ORDER BY user_id DESC LIMIT 1")
        result = cursor.fetchone()
        user_id = result[0] if result else 1
        vendor_id = 1  # Static for now

        cursor.execute("""
            INSERT INTO reviews (user_id, vendor_id, review_text, timestamp, is_anomaly, prediction, review)
            VALUES (?, ?, ?, ?, ?, ?, ?)
        """, (user_id, vendor_id, text, now, is_anomaly, prediction, text))
        db.commit()

        if is_anomaly:
            suspend_until = now + timedelta(hours=24)
            cursor.execute("UPDATE users SET suspended_until = ? WHERE user_id = ?", (suspend_until, user_id))
            db.commit()
            return f"❌ {prediction}\nUser temporarily suspended until {suspend_until.strftime('%Y-%m-%d %H:%M:%S')}."

        return f"✅ Prediction: {prediction}"

    except Exception as e:
        return f"⚠️ Database Error: {str(e)}"

interface = gr.Interface(
    fn=predict_review,
    inputs=gr.Textbox(lines=4, placeholder="Type a product review here...", label="Review Text"),
    outputs=gr.Textbox(label="Prediction"),
    title="🛍️ Byte Bazar Review Anomaly Detector",
    description="Enter a vendor or product review to check if it's anomalous or normal. This system uses spam detection, toxicity check, and custom rules to identify suspicious content."
)

# ---------------------- Launch App ----------------------
interface.launch()