StarReliabilityApp_Refined.py

import os
import sqlite3
import requests
import json
import pyttsx3  # For local TTS (if desired)
import speech_recognition as sr  # For local STT (if desired)

class StarMaintAI:
    def __init__(self, db_path):
        self.connection = sqlite3.connect(db_path)
        self.short_term_memory = []
        self.medium_term_memory = []
        self.long_term_memory = {}

        # ModelsLab API key
        self.modelslab_api_key = os.getenv("MODELSLAB_API_KEY", "")

        # StarMaint-specific system rules and prompts
        self.system_prompt = (
            "You are StarMaint AI, the ultimate assistant for industrial reliability and maintenance. "
            "Your purpose is to assist with predictive maintenance, task automation, voice interactions, and knowledge management. "
            "Be professional, concise, and helpful, adhering to the highest standards of AI performance."
        )

        self.rules = [
            "Always provide accurate and contextually relevant information.",
            "Follow the user’s intent and prioritize clarity in responses.",
            "Ensure all actions align with industrial safety and reliability principles.",
            "Operate efficiently and avoid unnecessary verbosity."
        ]

        self.load_long_term_memory()

    def load_long_term_memory(self):
        """
        Load persistent memory from the 'long_term_memory' table.
        """
        cursor = self.connection.cursor()
        cursor.execute("SELECT key, value FROM long_term_memory")
        self.long_term_memory = {row[0]: row[1] for row in cursor.fetchall()}

    def process_user_input(self, user_input):
        """
        Main pipeline: interpret user input, find relevant info, execute an action, return a response.
        """
        # Step 1: Interpret
        prompt = self.fetch_prompt("Process")
        processed_intent = self.nlp_parse(user_input, prompt)

        # Step 2: Retrieve relevant data from DB
        query_data = self.find_data(processed_intent)

        # Step 3: Execute the desired function
        action_response = self.execute_function(query_data)

        # Step 4: Generate final text response
        final_response = self.generate_response(user_input, action_response)
        return final_response

    def fetch_prompt(self, prompt_name):
        """
        Fetch a stored prompt or instruction from the 'prompts' table.
        """
        cursor = self.connection.cursor()
        cursor.execute("SELECT description FROM prompts WHERE title = ?", (prompt_name,))
        result = cursor.fetchone()
        return result[0] if result else ""

    def nlp_parse(self, text, prompt):
        """
        Basic natural language parsing — can be replaced with advanced NLU.
        """
        return f"Interpreted Command: {text} with prompt context: {prompt}"

    def find_data(self, intent):
        """
        Look up the function to call from a 'functions' table, using the interpreted intent.
        """
        cursor = self.connection.cursor()
        cursor.execute("SELECT * FROM functions WHERE function_name = ?", (intent,))
        return cursor.fetchone()  # Could contain e.g. ('transcribe_audio', ...)

    def execute_function(self, function_data):
        """
        Dynamically route to the desired function based on DB data or user intent.
        """
        if not function_data:
            return "No matching function found in database."

        function_name = function_data[0]

        if function_name == "transcribe_audio":
            audio_url = "https://example.com/test.wav"
            return self.transcribe_audio(audio_url, input_language="en")

        elif function_name == "generate_audio":
            text_prompt = "Hello, this is a sample text for voice synthesis."
            init_audio_url = "https://example.com/voice_clip.wav"
            return self.generate_audio(text_prompt, init_audio_url)

        elif function_name == "uncensored_chat":
            chat_prompt = "Write a tagline for an ice cream shop."
            return self.uncensored_chat_completion(chat_prompt)

        else:
            return f"Function '{function_name}' not recognized or not yet implemented."

    def transcribe_audio(self, audio_url, input_language="en"):
        """
        Integrates ModelsLab Speech-to-Text (Whisper) endpoint.
        """
        if not self.modelslab_api_key:
            return "API key not found; cannot transcribe audio."

        url = "https://modelslab.com/api/v6/whisper/transcribe"
        payload = {
            "key": self.modelslab_api_key,
            "audio_url": audio_url,
            "input_language": input_language,
            "timestamp_level": None,
            "webhook": None,
            "track_id": None
        }

        headers = {"Content-Type": "application/json"}

        try:
            response = requests.post(url, headers=headers, data=json.dumps(payload))
            return f"Transcription request sent. Response: {response.text}"
        except Exception as e:
            return f"Error during transcription: {e}"

    def generate_audio(self, text_prompt, init_audio_url=None, voice_id=None, language="english"):
        """
        Integrates ModelsLab Text-to-Audio (Voice Cloning / TTS).
        """
        if not self.modelslab_api_key:
            return "API key not found; cannot generate audio."

        url = "https://modelslab.com/api/v6/voice/text_to_audio"
        payload = {
            "key": self.modelslab_api_key,
            "prompt": text_prompt,
            "language": language,
            "webhook": None,
            "track_id": None
        }

        if init_audio_url:
            payload["init_audio"] = init_audio_url
        elif voice_id:
            payload["voice_id"] = voice_id

        headers = {"Content-Type": "application/json"}

        try:
            response = requests.post(url, headers=headers, data=json.dumps(payload))
            return f"Audio generation request sent. Response: {response.text}"
        except Exception as e:
            return f"Error during audio generation: {e}"

    def uncensored_chat_completion(self, prompt):
        """
        Integrates ModelsLab Uncensored Chat Completions.
        """
        if not self.modelslab_api_key:
            return "API key not found; cannot complete uncensored chat."

        base_url = "https://modelslab.com/api/uncensored-chat/v1/completions"
        payload = {
            "model": "ModelsLab/Llama-3.1-8b-Uncensored-Dare",
            "prompt": prompt,
            "max_tokens": 50,
            "temperature": 0.7
        }
        headers = {
            "Content-Type": "application/json",
            "Authorization": f"Bearer {self.modelslab_api_key}"
        }

        try:
            response = requests.post(base_url, headers=headers, data=json.dumps(payload))
            data = response.json()
            if "choices" in data and len(data["choices"]) > 0:
                return data["choices"][0].get("text", "")
            else:
                return f"Unexpected chat response: {data}"
        except Exception as e:
            return f"Error during uncensored chat completion: {e}"

    def generate_response(self, user_input, action_response):
        """
        Combine user input, system rules, and action response into a final message.
        """
        return (
            f"System Prompt: {self.system_prompt}\n"
            f"Rules: {'; '.join(self.rules)}\n"
            f"User Input: {user_input}\n"
            f"System Action: {action_response}"
        )


def run_app():
    """
    Example main loop to run the app in a console.
    """
    db_path = "central_data.db"  # Adjust for your environment
    starmaint_ai = StarMaintAI(db_path)

    print("Welcome to StarMaint AI.")
    while True:
        user_input = input("You: ")
        if user_input.lower() in ["exit", "quit"]:
            print("Exiting application.")
            break
        response = starmaint_ai.process_user_input(user_input)
        print(f"AI: {response}")


if __name__ == "__main__":
    run_app()