Create app.py

app.py

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+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()