import gradio as gr
from transformers import pipeline, RagTokenizer, RagRetriever, RagSequenceForGeneration
import paho.mqtt.client as mqtt
from gtts import gTTS
import os
import sqlite3
from sklearn.ensemble import IsolationForest

# Initialize Database
conn = sqlite3.connect('preferences.db')
cursor = conn.cursor()
cursor.execute('''CREATE TABLE IF NOT EXISTS preferences (id INTEGER PRIMARY KEY, setting TEXT, value TEXT)''')
cursor.execute('''CREATE TABLE IF NOT EXISTS history (id INTEGER PRIMARY KEY, command TEXT, response TEXT)''')
conn.commit()

# Anomaly Detection Model
anomaly_model = IsolationForest(contamination=0.1)
data = []

# Initialize Models
retriever = RagRetriever.from_pretrained("facebook/rag-sequence-base")
tokenizer = RagTokenizer.from_pretrained("facebook/rag-sequence-base")
model = RagSequenceForGeneration.from_pretrained("facebook/rag-sequence-base")
nlp = pipeline("conversational")

# IoT Device Control
def control_device(command):
    client = mqtt.Client()
    client.connect("broker.hivemq.com", 1883, 60)
    if "light" in command and "on" in command:
        client.publish("home/light", "ON")
        return "Light turned on."
    elif "light" in command and "off" in command:
        client.publish("home/light", "OFF")
        return "Light turned off."
    else:
        return "Command not recognized."

# Process Command
def process_command(command):
    if "light" in command:
        return control_device(command)
    else:
        inputs = tokenizer(command, return_tensors="pt")
        retrieved_docs = retriever(command, return_tensors="pt")
        outputs = model.generate(input_ids=inputs['input_ids'], context_input_ids=retrieved_docs['context_input_ids'])
        return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Log History
def log_history(command, response):
    cursor.execute("INSERT INTO history (command, response) VALUES (?, ?)", (command, response))
    conn.commit()

# Anomaly Detection
def detect_anomalies(command):
    global data
    data.append(len(command))
    if len(data) > 10:
        anomaly_model.fit([[x] for x in data])
        if anomaly_model.predict([[len(command)]])[0] == -1:
            return True
    return False

# Gradio Interface
def assistant(command):
    if detect_anomalies(command):
        return "Warning: Anomalous behavior detected!", ""
    response = process_command(command)
    log_history(command, response)
    tts = gTTS(text=response, lang='en')
    tts.save("response.mp3")
    return response, "response.mp3"

# Launch App
demo = gr.Interface(fn=assistant, inputs="text", outputs=["text", "audio"])
demo.launch()