main.py

"""
AGI Pipeline Module

This module integrates NLP, Computer Vision, and Speech Processing into a
multimodal AGI pipeline using FastAPI.
"""

import asyncio
import hashlib
import io
import os
import signal
import sys
import tempfile
import uuid
from datetime import datetime
from datetime import timezone
from functools import lru_cache
from typing import List, Optional

import jwt
import pyttsx3
import torch
import uvicorn
import whisper
from fastapi import Depends, FastAPI, HTTPException, UploadFile
from fastapi.middleware.cors import CORSMiddleware
from fastapi.security import OAuth2PasswordBearer
from loguru import logger
from PIL import Image
from pydantic import BaseModel
from transformers import T5ForConditionalGeneration, T5Tokenizer
from ultralytics import YOLO

# === Configuration and Logging Setup ===
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
logger.add("pipeline_{time}.log", rotation="1 MB", level="DEBUG", enqueue=True,
           backtrace=True, diagnose=True)
logger.info("Application startup")

# === Security Setup ===
SECRET_KEY = os.getenv("SECRET_KEY", "dummy-secret-key-for-local-dev-only")
ALGORITHM = "HS256"
oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")


def create_access_token(data: dict):
    """
    Creates a JWT access token.
    """
    to_encode = data.copy()
    encoded_jwt = jwt.encode(to_encode, SECRET_KEY, algorithm=ALGORITHM)
    return encoded_jwt


def authenticate_user(token: str = Depends(oauth2_scheme)):
    """
    Authenticates a user via JWT token.
    """
    try:
        payload = jwt.decode(token, SECRET_KEY, algorithms=[ALGORITHM])
    except jwt.PyJWTError as exc:
        logger.warning("Authentication failed.")
        raise HTTPException(status_code=401, detail="Invalid token") from exc
    return payload

# === Pydantic Models ===


class NLPResponse(BaseModel):
    """Model for NLP Expert responses."""
    text: str
    expert: str


class TextRequest(BaseModel):
    """Request model for text-based endpoints."""
    text: str


class ZKFairnessProof(BaseModel):
    """Model for Zero-Knowledge Fairness Proofs (MAS FEAT)."""
    proof_hash: str
    status: str
    demographic_parity_score: float


class ContextualAttributionEnvelope(BaseModel):
    """Model for Contextual Attribution Envelopes (HKMA Ethics)."""
    attribution_id: str
    contribution_scores: dict
    interpretability_summary: str = "Analysis completed via ASA Interpretability Layer."
    timestamp: str


class RecursiveContextEnvelope(BaseModel):
    """Model for Recursive Context Envelopes (EAIP State Management)."""
    task_lineage: List[str]
    context_depth: int
    metadata: dict = {}
    parent_rce: Optional['RecursiveContextEnvelope'] = None


class TextResponse(BaseModel):
    """Response model for text-based endpoints."""
    response: str
    zk_proof: ZKFairnessProof = None
    cae_metadata: ContextualAttributionEnvelope = None
    rce_state: RecursiveContextEnvelope = None

# === NLP Module (T5 Transformer) ===


class NLPModule:
    """Module for Natural Language Processing using T5 with MoE logic."""

    def __init__(self):
        model_name = "google/flan-t5-small"
        self.tokenizer = T5Tokenizer.from_pretrained(model_name)
        self.model = T5ForConditionalGeneration.from_pretrained(model_name)
        self.experts = ["Expert_Retail", "Expert_Finance", "Expert_General"]
        logger.info("NLP model and MoE experts loaded successfully.")

    def _route_to_expert(self, prompt: str) -> str:
        """Simulates routing logic to select an expert node."""
        lower_prompt = prompt.lower()
        if any(
            word in lower_prompt for word in [
                "buy",
                "price",
                "retail",
                "shop"]):
            return "Expert_Retail"
        if any(
            word in lower_prompt for word in [
                "finance",
                "stock",
                "investment",
                "bank"]):
            return "Expert_Finance"
        return "Expert_General"

    @lru_cache(maxsize=100)
    def generate_text(self, prompt: str) -> NLPResponse:
        """Generates a text response for a given prompt using MoE."""
        if not prompt.strip():
            raise ValueError("Prompt cannot be empty.")
        try:
            selected_expert = self._route_to_expert(prompt)
            logger.debug(
                f"Routing to expert: {selected_expert} for prompt: {prompt}")
            inputs = self.tokenizer(prompt, return_tensors="pt").to(device)
            with torch.no_grad():
                outputs = self.model.generate(
                    inputs["input_ids"], max_length=100)
            response = self.tokenizer.decode(
                outputs[0], skip_special_tokens=True)
            logger.info(f"Generated response by {selected_expert}: {response}")
            return NLPResponse(text=response, expert=selected_expert)
        except Exception as e:
            logger.error(f"Error during text generation: {e}")
            raise HTTPException(
                status_code=500,
                detail="Internal server error during text generation."
            ) from e
# === CV Module (YOLOv8 for Object Detection) ===


class CVModule:
    """Module for Computer Vision using YOLOv8."""

    def __init__(self):
        self.model = YOLO('yolov8n.pt').to(device)
        logger.info("CV model loaded successfully.")

    def detect_objects(self, image: Image.Image) -> str:
        """Detects objects in the provided image."""
        if image is None:
            raise ValueError("Image cannot be None.")
        try:
            logger.debug("Detecting objects in the image.")
            results = self.model(image)
            detections = results[0].to_json()
            logger.info("Object detection completed successfully.")
            return detections
        except Exception as e:
            logger.error(f"Error during object detection: {e}")
            raise HTTPException(
                status_code=500,
                detail="Internal server error during object detection."
            ) from e

# === Regulatory Module (Compliance: MAS FEAT & HKMA Ethics) ===


class RegulatoryModule:
    """Module for handling regulatory compliance checks (MAS FEAT & HKMA Ethics)."""

    def verify_zk_fairness(
            self,
            input_data: str,
            expert: str = "Unknown") -> ZKFairnessProof:
        """
        Simulates ZK-Fairness proof generation for MAS FEAT compliance.
        Evaluates demographic parity for specific MoE expert nodes.
        """
        # Mocking demographic parity calculation for MAS FEAT compliance
        # Certain experts might have different fairness profiles
        fairness_profiles = {
            "Expert_Retail": 0.92,
            "Expert_Finance": 0.94,
            "Expert_General": 0.96
        }
        parity_base = fairness_profiles.get(expert, 0.95)
        variance = (len(input_data) % 5) / 100.0
        dp_score = min(1.0, parity_base + variance)

        proof_hash = hashlib.sha3_512(
            f"{input_data}_{expert}".encode()).hexdigest()

        return ZKFairnessProof(
            proof_hash=f"zkp_{proof_hash[:32]}",
            status="VERIFIED" if dp_score >= 0.85 else "FAILED",
            demographic_parity_score=dp_score
        )

    def generate_cae(
            self,
            module_name: str,
            _output: str) -> ContextualAttributionEnvelope:
        """
        Simulates Contextual Attribution Envelope for HKMA Ethics compliance.
        Implements an ASA (Adaptive System Attribution) Interpretability Layer.
        """
        # Detailed contribution scores for enhanced interpretability
        if "NLPModule" in module_name:
            expert_name = module_name.split("_")[-1]
            contribution_scores = {
                f"ExpertNode_{expert_name}": 0.75,
                "BaseTransformer": 0.15,
                "ContextualEncoder": 0.05,
                "RegulatoryGuardrail": 0.05
            }
        else:
            contribution_scores = {
                module_name: 0.80,
                "BaseTransformer": 0.15,
                "SystemIntegrityLayer": 0.05
            }

        return ContextualAttributionEnvelope(
            attribution_id=f"cae_{uuid.uuid4().hex[:16]}",
            contribution_scores=contribution_scores,
            interpretability_summary=(
                f"Output segment processed by {module_name} using ASA Interpretability Layer. "
                "Contextual attribution verifies alignment with ethical guidelines."
            ),
            timestamp=datetime.now(timezone.utc).isoformat()
        )


class SpeechProcessor:
    """Module for processing speech-to-text and text-to-speech."""

    def __init__(self):
        self.whisper_model = whisper.load_model("base")
        try:
            self.tts = pyttsx3.init()
        except Exception:
            logger.warning(
                "pyttsx3 initialization failed. Text-to-speech will be disabled.")
            self.tts = None
        logger.info("Speech processor initialized successfully.")

    def speech_to_text(self, audio_file: UploadFile) -> str:
        """Converts audio input to text using Whisper."""
        with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp:
            tmp.write(audio_file.file.read())
            tmp_path = tmp.name
        try:
            logger.debug("Processing speech-to-text.")
            result = self.whisper_model.transcribe(tmp_path)
            text = result['text']
            logger.info("Speech-to-text conversion completed successfully.")
            return text
        except Exception as e:
            logger.error(f"Error during speech-to-text conversion: {e}")
            raise HTTPException(
                status_code=500,
                detail="Internal server error during speech-to-text conversion."
            ) from e
        finally:
            if os.path.exists(tmp_path):
                os.remove(tmp_path)

    def text_to_speech(self, text: str) -> None:
        """Synthesizes text into speech using Pyttsx3."""
        if not text.strip():
            raise ValueError("Text cannot be empty.")
        try:
            logger.debug("Processing text-to-speech.")
            if self.tts:
                self.tts.say(text)
            if self.tts:
                self.tts.runAndWait()
            logger.info("Text-to-speech conversion completed successfully.")
        except Exception as e:
            logger.error(f"Error during text-to-speech conversion: {e}")
            raise HTTPException(
                status_code=500,
                detail="Internal server error during text-to-speech conversion."
            ) from e

        if hasattr(self, "tts") and self.tts:
            self.tts.stop()

# === Enhanced AGI Pipeline ===


class EnhancedAGIPipeline:
    """Pipeline orchestrator for multimodal AGI tasks."""

    def __init__(self):
        self.nlp = NLPModule()
        self.cv = CVModule()
        self.speech_processor = SpeechProcessor()
        self.regulatory = RegulatoryModule()

    def _get_initial_rce(self, task_name: str) -> RecursiveContextEnvelope:
        return RecursiveContextEnvelope(
            task_lineage=[task_name],
            context_depth=0,
            metadata={"started_at": datetime.now(timezone.utc).isoformat()}
        )

    async def process_nlp(self, text: str) -> dict:
        """Asynchronously processes NLP requests with compliance and RCE checks."""
        rce = self._get_initial_rce("NLP_Task")
        nlp_output = await asyncio.to_thread(self.nlp.generate_text, text)
        response_text = nlp_output.text
        selected_expert = nlp_output.expert
        zk_proof = self.regulatory.verify_zk_fairness(
            text, expert=selected_expert)
        cae_metadata = self.regulatory.generate_cae(
            f"NLPModule_{selected_expert}", response_text)

        # Update RCE with results
        rce.task_lineage.append("NLP_Generated")
        rce.context_depth += 1  # pylint: disable=no-member
        rce.metadata["response_length"] = len(response_text)
        rce.metadata["selected_expert"] = selected_expert

        return {
            "response": response_text,
            "zk_proof": zk_proof,
            "cae_metadata": cae_metadata,
            "rce_state": rce
        }

    async def process_cv(self, image: Image.Image) -> dict:
        """Asynchronously processes CV requests with compliance checks."""
        detections = await asyncio.to_thread(self.cv.detect_objects, image)
        cae_metadata = self.regulatory.generate_cae("CVModule", detections)
        return {
            "detections": detections,
            "cae_metadata": cae_metadata
        }

    async def process_speech_to_text(self, audio_file: UploadFile) -> dict:
        """Asynchronously processes speech-to-text requests with compliance checks."""
        transcription = await asyncio.to_thread(self.speech_processor.speech_to_text, audio_file)
        cae_metadata = self.regulatory.generate_cae(
            "SpeechProcessor", transcription)
        return {
            "response": transcription,
            "cae_metadata": cae_metadata
        }

    async def process_text_to_speech(self, text: str) -> None:
        """Asynchronously processes text-to-speech requests."""
        await asyncio.to_thread(self.speech_processor.text_to_speech, text)


# === FastAPI Application ===
app = FastAPI()
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

pipeline = EnhancedAGIPipeline()

# === Graceful Shutdown ===


def shutdown_signal_handler(sig, frame):
    """Handles system signals for graceful shutdown."""
    # pylint: disable=unused-argument
    print('Shutting down gracefully...')
    sys.exit(0)


signal.signal(signal.SIGINT, shutdown_signal_handler)
signal.signal(signal.SIGTERM, shutdown_signal_handler)

# === Endpoints ===


@app.post("/process-nlp/", response_model=TextResponse,
          dependencies=[Depends(authenticate_user)])
async def process_nlp(request: TextRequest):
    """Endpoint for generating text responses."""
    return await pipeline.process_nlp(request.text)


@app.post("/process-cv-detection/",
          dependencies=[Depends(authenticate_user)])
async def process_cv_detection(file: UploadFile):
    """Endpoint for object detection in images."""
    image = Image.open(io.BytesIO(await file.read()))
    return await pipeline.process_cv(image)


@app.post("/batch-cv-detection/",
          dependencies=[Depends(authenticate_user)])
async def batch_cv_detection(files: List[UploadFile]):
    """Endpoint for batch object detection in images."""
    tasks = [pipeline.process_cv(Image.open(io.BytesIO(await file.read()))) for file in files]
    responses = await asyncio.gather(*tasks)
    return {"batch_detections": responses}


@app.post("/speech-to-text/", response_model=TextResponse,
          dependencies=[Depends(authenticate_user)])
async def speech_to_text(file: UploadFile):
    """Endpoint for speech-to-text transcription."""
    return await pipeline.process_speech_to_text(file)


@app.post("/text-to-speech/", dependencies=[Depends(authenticate_user)])
async def text_to_speech(request: TextRequest):
    """Endpoint for text-to-speech synthesis."""
    await pipeline.process_text_to_speech(request.text)
    return {"response": "Speech synthesis complete."}

# === Run the Application ===
if __name__ == "__main__":
    uvicorn.run(app, host="127.0.0.1", port=8000)