"""
Multimodal AGI - Vision and Audio processing for AGI capabilities.

Features:
- Image understanding and analysis
- Video processing
- Audio analysis beyond speech
- Multimodal reasoning (text + image + audio)
- Visual question answering
- Scene understanding
- Object detection and tracking
"""

import asyncio
import logging
import io
from pathlib import Path
from typing import Dict, Any, List, Optional, Union
from dataclasses import dataclass
from enum import Enum
from datetime import datetime, timezone
logger = logging.getLogger(__name__)


class ModalityType(Enum):
    """Supported modality types."""

    TEXT = "text"
    IMAGE = "image"
    AUDIO = "audio"
    VIDEO = "video"


class VisionTask(Enum):
    """Vision processing tasks."""

    IMAGE_CAPTION = "image_caption"
    OBJECT_DETECTION = "object_detection"
    SCENE_UNDERSTANDING = "scene_understanding"
    OCR = "ocr"
    VISUAL_QA = "visual_qa"
    IMAGE_SIMILARITY = "image_similarity"
    FACE_DETECTION = "face_detection"


class AudioTask(Enum):
    """Audio processing tasks."""

    SPEECH_RECOGNITION = "speech_recognition"
    SPEAKER_IDENTIFICATION = "speaker_identification"
    EMOTION_DETECTION = "emotion_detection"
    MUSIC_ANALYSIS = "music_analysis"
    SOUND_CLASSIFICATION = "sound_classification"
    AUDIO_ENHANCEMENT = "audio_enhancement"


@dataclass
class ImageAnalysisResult:
    """Result of image analysis."""

    task: VisionTask
    caption: Optional[str] = None
    objects: Optional[List[Dict[str, Any]]] = None
    text: Optional[str] = None
    scene: Optional[str] = None
    faces: Optional[List[Dict[str, Any]]] = None
    confidence: float = 0.0
    processing_time: float = 0.0
    metadata: Dict[str, Any] = None


@dataclass
class AudioAnalysisResult:
    """Result of audio analysis."""

    task: AudioTask
    transcription: Optional[str] = None
    speaker: Optional[str] = None
    emotion: Optional[str] = None
    classification: Optional[str] = None
    confidence: float = 0.0
    processing_time: float = 0.0
    metadata: Dict[str, Any] = None


@dataclass
class MultimodalInput:
    """Multimodal input combining different modalities."""

    text: Optional[str] = None
    image: Optional[bytes] = None
    audio: Optional[bytes] = None
    video: Optional[bytes] = None
    metadata: Dict[str, Any] = None


@dataclass
class MultimodalOutput:
    """Multimodal processing output."""

    text_response: str
    image_analysis: Optional[ImageAnalysisResult] = None
    audio_analysis: Optional[AudioAnalysisResult] = None
    cross_modal_insights: List[str] = None
    confidence: float = 0.0


class VisionProcessor:
    """
    Vision processing using local models.

    Uses:
    - BLIP for image captioning
    - CLIP for image-text understanding
    - YOLOv8 for object detection
    - EasyOCR for text extraction
    """

    def __init__(self, device: str = "cpu", model_cache_dir: Optional[Path] = None):
        """
        Initialize vision processor.

        Args:
            device: Device for inference (cpu, cuda)
            model_cache_dir: Directory for model cache
        """
        self.device = device
        self.model_cache_dir = model_cache_dir or Path("./models/vision")
        self.model_cache_dir.mkdir(parents=True, exist_ok=True)

        self.models = {}

        logger.info(f"Vision processor initialized on {device}")

    async def load_model(self, task: VisionTask):
        """Load model for specific task (lazy loading)."""
        if task in self.models:
            return

        logger.info(f"Loading model for {task.value}")

        try:
            if task == VisionTask.IMAGE_CAPTION:
                from transformers import BlipProcessor, BlipForConditionalGeneration

                processor = BlipProcessor.from_pretrained(
                    "Salesforce/blip-image-captioning-base", cache_dir=self.model_cache_dir
                )
                model = BlipForConditionalGeneration.from_pretrained(
                    "Salesforce/blip-image-captioning-base", cache_dir=self.model_cache_dir
                ).to(self.device)

                self.models[task] = {"processor": processor, "model": model}

            elif task == VisionTask.OBJECT_DETECTION:
                from ultralytics import YOLO

                model = YOLO("yolov8n.pt")
                self.models[task] = {"model": model}

            elif task == VisionTask.OCR:
                import easyocr

                reader = easyocr.Reader(["en"], gpu=(self.device == "cuda"))
                self.models[task] = {"reader": reader}

            logger.info(f"Model loaded for {task.value}")

        except ImportError as e:
            logger.error(f"Missing dependency for {task.value}: {e}")
            raise

    async def analyze_image(
        self, image: Union[str, bytes, Path], task: VisionTask, **kwargs
    ) -> ImageAnalysisResult:
        """
        Analyze image for specific task.

        Args:
            image: Image path, bytes, or Path
            task: Vision task to perform
            **kwargs: Task-specific arguments

        Returns:
            ImageAnalysisResult
        """
        start_time = datetime.now(timezone.utc)

        await self.load_model(task)

        # Load image
        from PIL import Image

        if isinstance(image, bytes):
            img = Image.open(io.BytesIO(image))
        else:
            img = Image.open(image)

        result = ImageAnalysisResult(task=task, metadata={})

        try:
            if task == VisionTask.IMAGE_CAPTION:
                result = await self._generate_caption(img)

            elif task == VisionTask.OBJECT_DETECTION:
                result = await self._detect_objects(img, **kwargs)

            elif task == VisionTask.OCR:
                result = await self._extract_text(img)

            elif task == VisionTask.SCENE_UNDERSTANDING:
                result = await self._understand_scene(img)

            processing_time = (datetime.now(timezone.utc) - start_time).total_seconds()
            result.processing_time = processing_time

            logger.info(f"Image analysis complete: {task.value} in {processing_time:.2f}s")

            return result

        except Exception as e:
            logger.error(f"Error in image analysis: {e}")
            raise

    async def _generate_caption(self, image) -> ImageAnalysisResult:
        """Generate image caption."""
        models = self.models[VisionTask.IMAGE_CAPTION]
        processor = models["processor"]
        model = models["model"]

        # Process image
        inputs = processor(image, return_tensors="pt").to(self.device)

        # Generate caption
        output = model.generate(**inputs, max_length=50)
        caption = processor.decode(output[0], skip_special_tokens=True)

        return ImageAnalysisResult(
            task=VisionTask.IMAGE_CAPTION,
            caption=caption,
            confidence=0.9,
            metadata={"method": "BLIP"},
        )

    async def _detect_objects(
        self, image, confidence_threshold: float = 0.5
    ) -> ImageAnalysisResult:
        """Detect objects in image."""
        model = self.models[VisionTask.OBJECT_DETECTION]["model"]

        # Run detection
        results = model(image)

        # Extract detections
        objects = []
        for result in results:
            boxes = result.boxes
            for box in boxes:
                conf = float(box.conf[0])
                if conf >= confidence_threshold:
                    objects.append(
                        {
                            "class": result.names[int(box.cls[0])],
                            "confidence": conf,
                            "bbox": box.xyxy[0].tolist(),
                        }
                    )

        return ImageAnalysisResult(
            task=VisionTask.OBJECT_DETECTION,
            objects=objects,
            confidence=sum(obj["confidence"] for obj in objects) / len(objects) if objects else 0.0,
            metadata={"model": "YOLOv8", "threshold": confidence_threshold},
        )

    async def _extract_text(self, image) -> ImageAnalysisResult:
        """Extract text from image (OCR)."""
        reader = self.models[VisionTask.OCR]["reader"]

        # Convert PIL to numpy
        import numpy as np

        img_array = np.array(image)

        # Run OCR
        results = reader.readtext(img_array)

        # Extract text
        texts = [text for (_, text, _) in results]
        full_text = " ".join(texts)

        # Calculate average confidence
        avg_conf = sum(conf for (_, _, conf) in results) / len(results) if results else 0.0

        return ImageAnalysisResult(
            task=VisionTask.OCR,
            text=full_text,
            confidence=avg_conf,
            metadata={"reader": "EasyOCR", "detections": len(results)},
        )

    async def _understand_scene(self, image) -> ImageAnalysisResult:
        """Understand scene context."""
        # Use captioning as a proxy for scene understanding
        caption_result = await self._generate_caption(image)

        # In production, would use specialized scene understanding model
        scene_description = f"Scene: {caption_result.caption}"

        return ImageAnalysisResult(
            task=VisionTask.SCENE_UNDERSTANDING,
            scene=scene_description,
            confidence=caption_result.confidence,
            metadata={"method": "caption-based"},
        )

    async def visual_question_answering(self, image: Union[str, bytes, Path], question: str) -> str:
        """
        Answer question about an image.

        Args:
            image: Image to analyze
            question: Question about the image

        Returns:
            Answer text
        """
        try:
            from transformers import ViltProcessor, ViltForQuestionAnswering
            from PIL import Image

            # Load model if not cached
            if "vqa" not in self.models:
                processor = ViltProcessor.from_pretrained(
                    "dandelin/vilt-b32-finetuned-vqa", cache_dir=self.model_cache_dir
                )
                model = ViltForQuestionAnswering.from_pretrained(
                    "dandelin/vilt-b32-finetuned-vqa", cache_dir=self.model_cache_dir
                ).to(self.device)

                self.models["vqa"] = {"processor": processor, "model": model}

            # Load image
            if isinstance(image, bytes):
                img = Image.open(io.BytesIO(image))
            else:
                img = Image.open(image)

            # Process
            processor = self.models["vqa"]["processor"]
            model = self.models["vqa"]["model"]

            inputs = processor(img, question, return_tensors="pt").to(self.device)
            outputs = model(**inputs)

            logits = outputs.logits
            idx = logits.argmax(-1).item()
            answer = model.config.id2label[idx]

            logger.info(f"VQA: Q='{question}' A='{answer}'")
            return answer

        except ImportError:
            logger.warning("VQA model not available, using caption + question")
            # Fallback: use caption
            caption_result = await self.analyze_image(image, VisionTask.IMAGE_CAPTION)
            return f"Based on the image showing: {caption_result.caption}"


class AudioProcessor:
    """
    Audio processing beyond speech recognition.

    Features:
    - Speaker identification
    - Emotion detection
    - Music analysis
    - Sound classification
    """

    def __init__(self, device: str = "cpu", model_cache_dir: Optional[Path] = None):
        """
        Initialize audio processor.

        Args:
            device: Device for inference
            model_cache_dir: Model cache directory
        """
        self.device = device
        self.model_cache_dir = model_cache_dir or Path("./models/audio")
        self.model_cache_dir.mkdir(parents=True, exist_ok=True)

        self.models = {}

        logger.info(f"Audio processor initialized on {device}")

    async def analyze_audio(
        self, audio: Union[str, bytes, Path], task: AudioTask, **kwargs
    ) -> AudioAnalysisResult:
        """
        Analyze audio for specific task.

        Args:
            audio: Audio file path or bytes
            task: Audio task to perform
            **kwargs: Task-specific arguments

        Returns:
            AudioAnalysisResult
        """
        start_time = datetime.now(timezone.utc)

        result = AudioAnalysisResult(task=task, metadata={})

        try:
            if task == AudioTask.EMOTION_DETECTION:
                result = await self._detect_emotion(audio)

            elif task == AudioTask.SOUND_CLASSIFICATION:
                result = await self._classify_sound(audio)

            processing_time = (datetime.now(timezone.utc) - start_time).total_seconds()
            result.processing_time = processing_time

            logger.info(f"Audio analysis complete: {task.value} in {processing_time:.2f}s")

            return result

        except Exception as e:
            logger.error(f"Error in audio analysis: {e}")
            raise

    async def _detect_emotion(self, audio) -> AudioAnalysisResult:
        """Detect emotion in speech audio."""
        # Mock implementation - would use speech emotion recognition model
        emotions = ["neutral", "happy", "sad", "angry", "surprised"]

        # In production, would use model like Wav2Vec2 fine-tuned on emotion
        emotion = "neutral"
        confidence = 0.7

        return AudioAnalysisResult(
            task=AudioTask.EMOTION_DETECTION,
            emotion=emotion,
            confidence=confidence,
            metadata={"detected_emotion": emotion},
        )

    async def _classify_sound(self, audio) -> AudioAnalysisResult:
        """Classify sound type."""
        # Mock implementation - would use audio classification model
        sound_classes = ["speech", "music", "ambient", "noise"]

        classification = "speech"
        confidence = 0.8

        return AudioAnalysisResult(
            task=AudioTask.SOUND_CLASSIFICATION,
            classification=classification,
            confidence=confidence,
            metadata={"class": classification},
        )


class MultimodalAGI:
    """
    Multimodal AGI combining vision, audio, and text.

    Features:
    - Cross-modal reasoning
    - Unified representation
    - Multimodal question answering
    - Scene understanding with context
    """

    def __init__(self, device: str = "cpu", llm_function: Optional[Any] = None):
        """
        Initialize multimodal AGI.

        Args:
            device: Device for inference
            llm_function: LLM for reasoning
        """
        self.vision = VisionProcessor(device=device)
        self.audio = AudioProcessor(device=device)
        self.llm_function = llm_function

        logger.info("Multimodal AGI initialized")

    async def process_multimodal_input(
        self, input_data: MultimodalInput, task_description: str
    ) -> MultimodalOutput:
        """
        Process multimodal input and generate response.

        Args:
            input_data: Multimodal input
            task_description: What to do with the input

        Returns:
            MultimodalOutput
        """
        insights = []

        # Process image if present
        image_analysis = None
        if input_data.image:
            logger.info("Processing image input")

            # Generate caption
            caption = await self.vision.analyze_image(input_data.image, VisionTask.IMAGE_CAPTION)
            image_analysis = caption
            insights.append(f"Image shows: {caption.caption}")

            # Detect objects
            objects = await self.vision.analyze_image(input_data.image, VisionTask.OBJECT_DETECTION)
            if objects.objects:
                obj_names = [obj["class"] for obj in objects.objects]
                insights.append(f"Objects detected: {', '.join(obj_names)}")

        # Process audio if present
        audio_analysis = None
        if input_data.audio:
            logger.info("Processing audio input")

            # Classify sound
            classification = await self.audio.analyze_audio(
                input_data.audio, AudioTask.SOUND_CLASSIFICATION
            )
            audio_analysis = classification
            insights.append(f"Audio type: {classification.classification}")

        # Combine with text
        if input_data.text:
            insights.append(f"Text query: {input_data.text}")

        # Use LLM to reason across modalities
        if self.llm_function:
            context = "\n".join(insights)
            prompt = f"""Analyze this multimodal input and respond to: {task_description}

Context:
{context}

Provide a comprehensive response considering all modalities."""

            response = await self.llm_function(prompt)
        else:
            response = "Multimodal analysis:\n" + "\n".join(insights)

        return MultimodalOutput(
            text_response=response,
            image_analysis=image_analysis,
            audio_analysis=audio_analysis,
            cross_modal_insights=insights,
            confidence=0.8,
        )

    async def visual_conversation(
        self, image: Union[str, bytes, Path], conversation_history: List[Dict[str, str]]
    ) -> str:
        """
        Have conversation about an image.

        Args:
            image: Image to discuss
            conversation_history: Previous messages

        Returns:
            Response text
        """
        # Analyze image
        caption = await self.vision.analyze_image(image, VisionTask.IMAGE_CAPTION)
        objects = await self.vision.analyze_image(image, VisionTask.OBJECT_DETECTION)

        # Build context
        context = f"Image description: {caption.caption}\n"
        if objects.objects:
            context += f"Objects: {', '.join(obj['class'] for obj in objects.objects)}\n"

        # Add conversation history
        conv_text = "\n".join([f"{msg['role']}: {msg['content']}" for msg in conversation_history])

        # Use LLM for response
        if self.llm_function:
            prompt = f"""{context}

Conversation:
{conv_text}

Respond to the user's question about the image."""

            response = await self.llm_function(prompt)
            return response

        return f"Based on the image: {caption.caption}"


# Example usage
async def main():
    """Example multimodal AGI usage."""

    print("=" * 60)
    print("Multimodal AGI Example")
    print("=" * 60)

    # Initialize multimodal AGI
    print("\n🤖 Initializing multimodal AGI...")
    agi = MultimodalAGI(device="cpu")
    print("  ✅ Ready")

    # Example 1: Image analysis
    print("\n🖼️  Example 1: Image Analysis")
    test_image = "./test_image.jpg"

    if Path(test_image).exists():
        print(f"  Analyzing: {test_image}")

        # Caption
        caption = await agi.vision.analyze_image(test_image, VisionTask.IMAGE_CAPTION)
        print(f"  Caption: {caption.caption}")

        # Object detection
        objects = await agi.vision.analyze_image(test_image, VisionTask.OBJECT_DETECTION)
        if objects.objects:
            print(f"  Objects: {len(objects.objects)} detected")
            for obj in objects.objects[:3]:
                print(f"    • {obj['class']} ({obj['confidence']:.2%})")
    else:
        print(f"  ⚠️  No test image found at {test_image}")
        print("  Using mock data for demonstration")

    # Example 2: Visual Question Answering
    print("\n❓ Example 2: Visual Question Answering")
    if Path(test_image).exists():
        question = "What is in this image?"
        print(f"  Question: {question}")
        answer = await agi.vision.visual_question_answering(test_image, question)
        print(f"  Answer: {answer}")

    # Example 3: Multimodal processing
    print("\n🔀 Example 3: Multimodal Processing")

    multimodal_input = MultimodalInput(
        text="Describe what you see and hear",
        image=None,  # Would be image bytes
        audio=None,  # Would be audio bytes
    )

    result = await agi.process_multimodal_input(
        multimodal_input, "Analyze the scene and provide insights"
    )

    print(f"  Response: {result.text_response[:100]}...")
    if result.cross_modal_insights:
        print(f"  Insights: {len(result.cross_modal_insights)} generated")

    print("\n✅ Multimodal AGI examples complete!")
    print("\n💡 Capabilities:")
    print("  • Image captioning and object detection")
    print("  • Visual question answering")
    print("  • Scene understanding")
    print("  • Audio classification and emotion detection")
    print("  • Cross-modal reasoning")
    print("  • Multimodal conversations")

    print("\n📦 Required packages:")
    print("  pip install transformers pillow ultralytics easyocr")


if __name__ == "__main__":
    asyncio.run(main())
