Creative Generation Agent

"""
Image Generation Agent
Generates images using diffusion models with prompt engineering and style control.
"""

from diffusers import StableDiffusionPipeline
import torch


class ImageGenerationAgent:
    """Generates images from text prompts using Stable Diffusion."""

    def __init__(self, model_id="runwayml/stable-diffusion-v1-5"):
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        self.pipeline = StableDiffusionPipeline.from_pretrained(
            model_id,
            torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
        ).to(self.device)

    def generate_image(self, prompt, num_inference_steps=50, guidance_scale=7.5, style="realistic"):
        """Generate image from text prompt.

        Args:
            prompt: Text description of image
            num_inference_steps: Number of diffusion steps
            guidance_scale: Classifier-free guidance scale
            style: Artistic style to apply

        Returns:
            PIL.Image.Image: Generated image
        """
        # Enhance prompt with style
        enhanced_prompt = self.enhance_prompt(prompt, style)

        # Generate image
        image = self.pipeline(
            enhanced_prompt,
            num_inference_steps=num_inference_steps,
            guidance_scale=guidance_scale,
            height=512,
            width=512
        ).images[0]

        return image

    def enhance_prompt(self, prompt, style="realistic"):
        """Add style-specific enhancements to prompt.

        Args:
            prompt: Base prompt
            style: Artistic style

        Returns:
            str: Enhanced prompt
        """
        style_modifiers = {
            "realistic": "photorealistic, high quality, detailed, 8k",
            "anime": "anime style, manga art, beautiful animation",
            "oil_painting": "oil painting, renaissance, masterpiece",
            "cyberpunk": "cyberpunk, neon, futuristic, digital art",
            "watercolor": "watercolor painting, soft colors, artistic"
        }

        modifier = style_modifiers.get(style, "high quality")
        return f"{prompt}, {modifier}"

    def generate_variations(self, image, num_variations=4):
        """Generate variations of an existing image.

        Args:
            image: Seed image
            num_variations: Number of variations to create

        Returns:
            list: List of variation images
        """
        variations = []

        # Use image as seed
        seed_embedding = self.encode_image(image)

        for _ in range(num_variations):
            variation = self.pipeline(
                image=image,
                num_inference_steps=30,
                strength=0.7  # Strength of modification
            ).images[0]

            variations.append(variation)

        return variations

    def encode_image(self, image):
        """Encode image to latent space.

        Args:
            image: Image to encode

        Returns:
            torch.Tensor: Latent encoding
        """
        pass

"""
Meme Generation Agent
Generates memes through image captioning, text-based formats, and style adaptation.
"""

from PIL import Image, ImageDraw, ImageFont
import textwrap


class MemeGenerationAgent:
    """Generates image-based memes with AI captions."""

    def __init__(self):
        self.image_generator = self.load_image_generator()
        self.caption_generator = self.load_caption_generator()

    def generate_meme(self, topic, meme_template="drake"):
        """Generate complete meme with caption and template.

        Args:
            topic: Topic for meme generation
            meme_template: Template name to use

        Returns:
            PIL.Image.Image: Generated meme
        """
        # Generate caption
        caption = self.generate_caption(topic)

        # Get meme template image
        template_image = self.get_template(meme_template)

        # Apply text to image
        meme = self.apply_caption_to_template(template_image, caption)

        return meme

    def generate_caption(self, topic, style="humorous"):
        """Generate meme caption for given topic.

        Args:
            topic: Topic for caption
            style: Style of humor

        Returns:
            list: List of caption lines
        """
        prompt = f"Generate a funny two-line meme caption about {topic} in {style} style"
        caption = self.caption_generator.generate(prompt, max_tokens=100)
        return caption.strip().split('\n')[:2]

    def get_template(self, template_name):
        """Get meme template image.

        Args:
            template_name: Name of template

        Returns:
            PIL.Image.Image: Template image
        """
        templates = {
            "drake": "templates/drake.jpg",
            "loss": "templates/loss.jpg",
            "expanding_brain": "templates/expanding_brain.jpg",
            "wojak": "templates/wojak.jpg"
        }
        return Image.open(templates.get(template_name, templates["drake"]))

    def apply_caption_to_template(self, image, captions):
        """Apply text captions to meme template.

        Args:
            image: Template image
            captions: List of caption texts

        Returns:
            PIL.Image.Image: Image with captions
        """
        draw = ImageDraw.Draw(image)
        font = self.get_font(size=40)

        positions = self.calculate_caption_positions(image, len(captions))

        for i, caption in enumerate(captions):
            text = self.wrap_text(caption, 40)
            self.draw_text_with_outline(draw, positions[i], text, font)

        return image

    def draw_text_with_outline(self, draw, position, text, font, outline_width=2):
        """Draw text with outline for visibility on images.

        Args:
            draw: PIL ImageDraw object
            position: Text position
            text: Text to draw
            font: Font to use
            outline_width: Width of outline
        """
        x, y = position

        # Draw outline
        for adj_x in range(-outline_width, outline_width + 1):
            for adj_y in range(-outline_width, outline_width + 1):
                draw.text((x + adj_x, y + adj_y), text, fill='black', font=font)

        # Draw text
        draw.text(position, text, fill='white', font=font)

    def wrap_text(self, text, width):
        """Wrap text to specified width.

        Args:
            text: Text to wrap
            width: Maximum width

        Returns:
            str: Wrapped text
        """
        return '\n'.join(textwrap.wrap(text, width=width))

    def calculate_caption_positions(self, image, num_captions):
        """Calculate text positions for captions.

        Args:
            image: Image to position text on
            num_captions: Number of captions

        Returns:
            list: List of (x, y) positions
        """
        width, height = image.size
        positions = []

        if num_captions == 1:
            positions.append((20, 20))
        elif num_captions == 2:
            positions.append((20, 20))
            positions.append((20, height - 100))

        return positions

    def get_font(self, size=40):
        """Get font for text rendering.

        Args:
            size: Font size

        Returns:
            PIL.ImageFont: Font object
        """
        try:
            return ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf", size)
        except Exception:
            return ImageFont.load_default()

    def load_image_generator(self):
        """Load image generation model."""
        pass

    def load_caption_generator(self):
        """Load caption generation model."""
        pass


class TextMemeGenerator:
    """Generates text-based memes in various formats."""

    def __init__(self):
        self.joke_engine = self.load_joke_engine()
        self.format_templates = self.load_format_templates()

    def generate_text_meme(self, topic, format_type="joke"):
        """Generate text-only meme in specified format.

        Args:
            topic: Topic for meme
            format_type: Type of meme format

        Returns:
            str: Generated meme text
        """
        if format_type == "joke":
            return self.generate_joke_meme(topic)
        elif format_type == "unexpected":
            return self.generate_unexpected_meme(topic)
        elif format_type == "deep":
            return self.generate_deep_meme(topic)

    def generate_joke_meme(self, topic):
        """Generate joke-style meme.

        Args:
            topic: Topic for joke

        Returns:
            str: Joke meme
        """
        setup = self.joke_engine.generate_setup(topic)
        punchline = self.joke_engine.generate_punchline(setup)

        return f"Setup: {setup}\nPunchline: {punchline}"

    def generate_unexpected_meme(self, topic):
        """Generate meme with unexpected twist.

        Args:
            topic: Topic for meme

        Returns:
            str: Unexpected meme
        """
        prompt = f"Create a meme with an unexpected twist about {topic}"
        meme_text = self.joke_engine.generate(prompt)
        return meme_text

    def generate_deep_meme(self, topic):
        """Generate deep/philosophical meme.

        Args:
            topic: Topic for meme

        Returns:
            str: Deep meme
        """
        prompt = f"Generate a deep, philosophical meme about {topic}"
        meme_text = self.joke_engine.generate(prompt)
        return meme_text

    def load_joke_engine(self):
        """Load joke/text generation engine."""
        pass

    def load_format_templates(self):
        """Load meme format templates."""
        pass

"""
Music Generation Agent
Generates music as MIDI/musical notation and provides audio synthesis capabilities.
"""

import music21
from music21 import stream, instrument, note, tempo
import numpy as np
import scipy.io.wavfile as wavfile
from scipy.fft import fft


class MusicGenerationAgent:
    """Generates music using symbolic notation and continuation techniques."""

    def __init__(self, model_name="music-transformer"):
        self.model = self.load_model(model_name)

    def generate_melody(self, seed_notes, length=32, temperature=0.8):
        """Generate melody continuation from seed notes.

        Args:
            seed_notes: List of tuples (pitch, duration)
            length: Total length of melody in notes
            temperature: Controls randomness (0.1-1.0)

        Returns:
            music21.stream.Stream: Generated melody
        """
        melody = stream.Stream()

        # Add seed notes
        for pitch, duration in seed_notes:
            melody.append(note.Note(pitch, quarterLength=duration))

        # Generate continuation
        current_notes = [n for n in melody.flatten().notes]

        for _ in range(length - len(seed_notes)):
            next_note = self.predict_next_note(current_notes, temperature)
            melody.append(next_note)
            current_notes.append(next_note)

        return melody

    def predict_next_note(self, previous_notes, temperature):
        """Predict next note using model.

        Args:
            previous_notes: List of previous music21 notes
            temperature: Sampling temperature

        Returns:
            music21.note.Note: Predicted next note
        """
        context = self.encode_notes(previous_notes[-16:])  # Last 16 notes
        logits = self.model.predict(context)

        # Apply temperature sampling
        probs = self.apply_temperature(logits, temperature)
        next_pitch = self.sample_from_distribution(probs)
        next_duration = self.sample_duration(probs)

        return note.Note(next_pitch, quarterLength=next_duration)

    def generate_full_composition(self, style="classical", duration_bars=32):
        """Generate complete musical piece with melody and harmony.

        Args:
            style: Musical style (e.g., "classical", "jazz", "pop")
            duration_bars: Length of composition in bars

        Returns:
            music21.stream.Score: Complete composition
        """
        composition = stream.Score()

        # Create instruments
        piano_part = stream.Part()
        piano_part.append(instrument.Piano())

        # Generate melody
        melody = self.generate_melody_section(duration_bars)
        piano_part.append(melody)

        # Generate harmony
        harmony = self.generate_harmony(melody)
        piano_part.append(harmony)

        # Add tempo and structure
        composition.append(stream.MetronomeMark(number=120))
        composition.append(piano_part)

        return composition

    def generate_harmony(self, melody):
        """Generate harmonic accompaniment for melody.

        Args:
            melody: Melodic stream

        Returns:
            music21.stream.Stream: Harmonic accompaniment
        """
        harmony = stream.Stream()

        # Analyze melody to find chord progression
        chords = self.analyze_melody_harmony(melody)

        # Generate accompaniment based on chords
        for chord_tones in chords:
            harmony.append(chord_tones)

        return harmony

    def encode_notes(self, notes):
        """Encode notes for model input."""
        # Implementation depends on specific model
        pass

    def apply_temperature(self, logits, temperature):
        """Apply temperature scaling to probabilities.

        Args:
            logits: Raw model outputs
            temperature: Temperature parameter

        Returns:
            np.ndarray: Probability distribution
        """
        scaled = logits / temperature
        exp_logits = np.exp(scaled)
        return exp_logits / np.sum(exp_logits)

    def sample_from_distribution(self, probs):
        """Sample from probability distribution.

        Args:
            probs: Probability distribution

        Returns:
            int: Sampled note
        """
        return np.random.choice(len(probs), p=probs)

    def sample_duration(self, probs):
        """Sample note duration from distribution."""
        pass

    def load_model(self, model_name):
        """Load the music generation model."""
        pass

    def analyze_melody_harmony(self, melody):
        """Analyze melody and extract harmonic structure."""
        pass

    def generate_melody_section(self, duration_bars):
        """Generate a melody section with given duration."""
        pass


class AudioSynthesisAgent:
    """Synthesizes audio waveforms from MIDI or other inputs."""

    def __init__(self, sample_rate=44100):
        self.sample_rate = sample_rate

    def synthesize_from_midi(self, midi_data, duration_seconds=60):
        """Convert MIDI to audio waveform.

        Args:
            midi_data: MIDI note data with start, duration, pitch
            duration_seconds: Total audio duration

        Returns:
            np.ndarray: Audio samples
        """
        audio_data = np.zeros(int(self.sample_rate * duration_seconds))

        for note in midi_data:
            start_time = note['start'] * self.sample_rate
            duration = note['duration'] * self.sample_rate
            pitch = note['pitch']

            # Generate sine wave for note
            sine_wave = self.generate_sine_wave(
                pitch,
                duration,
                envelope=self.generate_adsr_envelope(duration)
            )

            # Add to audio
            start_idx = int(start_time)
            end_idx = start_idx + len(sine_wave)
            audio_data[start_idx:end_idx] += sine_wave * 0.3

        return audio_data

    def generate_sine_wave(self, frequency, num_samples, envelope=None):
        """Generate sine wave for a specific frequency.

        Args:
            frequency: Frequency in Hz
            num_samples: Number of samples to generate
            envelope: Optional amplitude envelope

        Returns:
            np.ndarray: Generated wave samples
        """
        t = np.arange(num_samples) / self.sample_rate
        wave = np.sin(2 * np.pi * frequency * t)

        if envelope is not None:
            wave *= envelope

        return wave

    def generate_adsr_envelope(self, total_samples, attack=0.1, decay=0.2, sustain=0.6, release=0.1):
        """Generate ADSR (Attack, Decay, Sustain, Release) envelope.

        Args:
            total_samples: Total envelope length
            attack: Attack time ratio
            decay: Decay time ratio
            sustain: Sustain level
            release: Release time ratio

        Returns:
            np.ndarray: ADSR envelope
        """
        attack_samples = int(attack * self.sample_rate)
        decay_samples = int(decay * self.sample_rate)
        sustain_samples = int(sustain * self.sample_rate)
        release_samples = int(release * self.sample_rate)

        envelope = []

        # Attack
        envelope.extend(np.linspace(0, 1, attack_samples))

        # Decay
        envelope.extend(np.linspace(1, 0.7, decay_samples))

        # Sustain
        envelope.extend(np.ones(sustain_samples) * 0.7)

        # Release
        envelope.extend(np.linspace(0.7, 0, release_samples))

        # Pad to total length
        envelope.extend(np.zeros(total_samples - len(envelope)))

        return np.array(envelope[:total_samples])

    def add_effects(self, audio, effect_type="reverb"):
        """Apply audio effects to signal.

        Args:
            audio: Audio samples
            effect_type: Type of effect to apply

        Returns:
            np.ndarray: Audio with effects
        """
        if effect_type == "reverb":
            return self.add_reverb(audio)
        elif effect_type == "chorus":
            return self.add_chorus(audio)
        elif effect_type == "delay":
            return self.add_delay(audio)

        return audio

    def add_reverb(self, audio, room_size=0.5, delay_ms=50):
        """Add reverb effect to audio.

        Args:
            audio: Input audio
            room_size: Room size parameter
            delay_ms: Delay time in milliseconds

        Returns:
            np.ndarray: Audio with reverb
        """
        delay_samples = int(delay_ms * self.sample_rate / 1000)
        reverb = audio.copy()

        for i in range(1, 5):
            delayed = np.zeros_like(audio)
            delayed[delay_samples * i:] = audio[:-delay_samples * i]
            reverb += delayed * (0.5 ** i)

        return reverb / np.max(np.abs(reverb))

    def add_chorus(self, audio):
        """Add chorus effect (pitch variation).

        Args:
            audio: Input audio

        Returns:
            np.ndarray: Audio with chorus effect
        """
        chorus = audio.copy()

        # Vary playback speed slightly
        speed_variation = 1 + 0.02 * np.sin(np.arange(len(audio)) / self.sample_rate * 2 * np.pi)
        chorus_delayed = np.interp(
            np.arange(len(audio)) / speed_variation,
            np.arange(len(audio)),
            audio
        )

        return (audio + chorus_delayed) / 2

    def add_delay(self, audio, delay_ms=200, feedback=0.6):
        """Add delay effect to audio.

        Args:
            audio: Input audio
            delay_ms: Delay time in milliseconds
            feedback: Feedback amount

        Returns:
            np.ndarray: Audio with delay
        """
        delay_samples = int(delay_ms * self.sample_rate / 1000)
        delayed = np.zeros_like(audio)

        delayed[delay_samples:] = audio[:-delay_samples] * feedback

        return audio + delayed

    def save_audio(self, audio, filename, normalize=True):
        """Save audio to WAV file.

        Args:
            audio: Audio samples
            filename: Output file path
            normalize: Whether to normalize audio
        """
        if normalize:
            audio = audio / np.max(np.abs(audio))

        # Convert to 16-bit
        audio_int = np.int16(audio * 32767)

        wavfile.write(filename, self.sample_rate, audio_int)

"""
Podcast Producer Agent
Generates podcast scripts and produces audio from text-to-speech and audio synthesis.
"""

import numpy as np


class PodcastScriptGenerator:
    """Generates podcast scripts with intro, content segments, and outro."""

    def __init__(self):
        self.script_model = self.load_script_model()
        self.tts_engine = self.load_tts_engine()

    def generate_episode(self, topic, duration_minutes=30, num_hosts=2):
        """Generate complete podcast episode with script and audio.

        Args:
            topic: Podcast topic
            duration_minutes: Desired duration in minutes
            num_hosts: Number of hosts

        Returns:
            dict: Contains script, audio, and duration
        """
        script = self.generate_script(topic, duration_minutes, num_hosts)
        audio = self.convert_to_audio(script)

        return {
            "script": script,
            "audio": audio,
            "duration": duration_minutes
        }

    def generate_script(self, topic, duration_minutes, num_hosts):
        """Generate podcast script with structure.

        Args:
            topic: Episode topic
            duration_minutes: Script duration
            num_hosts: Number of hosts

        Returns:
            str: Complete script
        """
        script_parts = []

        # Generate intro
        intro = self.generate_intro(topic, num_hosts)
        script_parts.append(intro)

        # Generate main content segments
        content_duration = duration_minutes - 5  # Subtract intro/outro
        segments = self.generate_content_segments(topic, content_duration, num_hosts)
        script_parts.extend(segments)

        # Generate outro
        outro = self.generate_outro()
        script_parts.append(outro)

        return '\n\n'.join(script_parts)

    def generate_intro(self, topic, num_hosts):
        """Generate podcast introduction.

        Args:
            topic: Episode topic
            num_hosts: Number of hosts

        Returns:
            str: Intro script
        """
        prompt = f"""Generate a podcast intro for a {num_hosts}-host show about: {topic}

        Format:
        HOST 1: [greeting and topic introduction]
        HOST 2: [add enthusiasm and context]

        Keep it engaging and conversational."""

        return self.script_model.generate(prompt, max_tokens=200)

    def generate_content_segments(self, topic, duration_minutes, num_hosts):
        """Generate main discussion content segments.

        Args:
            topic: Episode topic
            duration_minutes: Content duration
            num_hosts: Number of hosts

        Returns:
            list: List of content segments
        """
        words_per_minute = 150
        total_words = duration_minutes * words_per_minute

        segments = []
        words_written = 0

        # Generate 3-4 main points
        points = self.identify_key_points(topic, 3)

        for point in points:
            segment_words = total_words // len(points)
            segment = self.generate_discussion_segment(point, segment_words, num_hosts)
            segments.append(segment)
            words_written += len(segment.split())

        return segments

    def generate_discussion_segment(self, point, target_words, num_hosts):
        """Generate back-and-forth discussion segment.

        Args:
            point: Discussion topic
            target_words: Target word count
            num_hosts: Number of hosts

        Returns:
            str: Discussion segment
        """
        prompt = f"""Generate a podcast discussion segment about: {point}

        Target length: ~{target_words} words
        Number of hosts: {num_hosts}

        Format as natural back-and-forth conversation between hosts.
        Use natural speech patterns and interruptions."""

        return self.script_model.generate(prompt, max_tokens=target_words // 4)

    def generate_outro(self):
        """Generate podcast outro/closing.

        Returns:
            str: Outro script
        """
        prompt = """Generate a podcast outro that:
        1. Summarizes key points
        2. Thanks listeners
        3. Previews next episode
        4. Includes call-to-action"""

        return self.script_model.generate(prompt, max_tokens=150)

    def identify_key_points(self, topic, num_points):
        """Identify key discussion points for topic."""
        pass

    def load_script_model(self):
        """Load script generation model."""
        pass

    def load_tts_engine(self):
        """Load text-to-speech engine."""
        pass

    def convert_to_audio(self, script):
        """Convert script to audio."""
        pass


class PodcastAudioProducer:
    """Produces high-quality audio from podcast scripts."""

    def __init__(self):
        self.tts_engine = self.load_tts_engine()
        from music_generation import AudioSynthesisAgent
        self.audio_processor = AudioSynthesisAgent()

    def produce_podcast(self, script):
        """Produce audio from podcast script.

        Args:
            script: Podcast script text

        Returns:
            np.ndarray: Audio samples
        """
        audio_segments = []

        # Parse script into speaker segments
        segments = self.parse_script(script)

        for speaker, text in segments:
            # Generate audio for segment
            audio = self.text_to_speech(text, speaker)
            audio_segments.append(audio)

        # Combine segments
        full_audio = self.combine_audio_segments(audio_segments)

        # Add background music and effects
        full_audio = self.add_background_music(full_audio)
        full_audio = self.add_transitions(full_audio)

        return full_audio

    def parse_script(self, script):
        """Parse script into speaker segments.

        Args:
            script: Script text

        Returns:
            list: List of (speaker, text) tuples
        """
        segments = []

        for line in script.split('\n'):
            if ':' in line:
                speaker, text = line.split(':', 1)
                segments.append((speaker.strip(), text.strip()))

        return segments

    def text_to_speech(self, text, speaker="HOST1", speed=1.0, pitch=1.0):
        """Convert text to speech audio.

        Args:
            text: Text to synthesize
            speaker: Speaker voice
            speed: Playback speed
            pitch: Pitch adjustment

        Returns:
            np.ndarray: Audio samples
        """
        audio = self.tts_engine.synthesize(text, voice=speaker)

        # Adjust speed and pitch
        audio = self.adjust_speed(audio, speed)
        audio = self.adjust_pitch(audio, pitch)

        return audio

    def combine_audio_segments(self, segments):
        """Combine audio segments with gaps.

        Args:
            segments: List of audio arrays

        Returns:
            np.ndarray: Combined audio
        """
        combined = np.concatenate(segments)
        return combined

    def add_background_music(self, audio):
        """Add background music to podcast.

        Args:
            audio: Main audio track

        Returns:
            np.ndarray: Audio with background music
        """
        music = self.generate_background_music(len(audio))
        music = music * 0.2  # Reduce volume

        return audio + music

    def add_transitions(self, audio):
        """Add transition sounds between segments.

        Args:
            audio: Audio data

        Returns:
            np.ndarray: Audio with transitions
        """
        pass

    def adjust_speed(self, audio, speed_factor):
        """Adjust playback speed of audio.

        Args:
            audio: Audio samples
            speed_factor: Speed multiplier

        Returns:
            np.ndarray: Speed-adjusted audio
        """
        if speed_factor == 1.0:
            return audio

        indices = np.arange(0, len(audio)) / speed_factor
        return np.interp(indices, np.arange(len(audio)), audio)

    def adjust_pitch(self, audio, pitch_factor):
        """Adjust pitch of audio.

        Args:
            audio: Audio samples
            pitch_factor: Pitch multiplier

        Returns:
            np.ndarray: Pitch-adjusted audio
        """
        pass

    def generate_background_music(self, duration_samples):
        """Generate ambient background music.

        Args:
            duration_samples: Length in samples

        Returns:
            np.ndarray: Generated music
        """
        pass

    def load_tts_engine(self):
        """Load text-to-speech engine."""
        pass

"""
Style Transfer Agent
Transfers artistic styles between images using neural style transfer techniques.
"""

import cv2


class StyleTransferAgent:
    """Applies artistic style from one image to another using neural networks."""

    def __init__(self, model_path="models/style_transfer.pth"):
        self.model = self.load_model(model_path)

    def transfer_style(self, content_image, style_image):
        """Transfer style from one image to another.

        Args:
            content_image: Path to content image
            style_image: Path to style reference image

        Returns:
            np.ndarray: Stylized image
        """
        # Preprocess images
        content = self.preprocess_image(content_image)
        style = self.preprocess_image(style_image)

        # Transfer style
        stylized = self.model(content, style)

        # Postprocess
        output = self.postprocess_image(stylized)

        return output

    def preprocess_image(self, image_path):
        """Load and preprocess image for style transfer.

        Args:
            image_path: Path to image file

        Returns:
            np.ndarray: Preprocessed image
        """
        image = cv2.imread(image_path)
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        image = image.astype('float32') / 255.0

        return image

    def postprocess_image(self, image):
        """Postprocess style-transferred image.

        Args:
            image: Output image from model

        Returns:
            np.ndarray: Clipped and scaled image
        """
        image = (image.clip(0, 1) * 255).astype('uint8')
        return image

    def load_model(self, model_path):
        """Load the style transfer model.

        Args:
            model_path: Path to model weights

        Returns:
            Model instance
        """
        pass

"""
Audio Effects Utilities
Provides various audio effect implementations for sound processing and enhancement.
"""

import numpy as np


class AudioEffects:
    """Provides common audio effects for music and podcast production."""

    def __init__(self, sample_rate=44100):
        self.sample_rate = sample_rate

    def apply_effect(self, audio, effect_type="reverb", **kwargs):
        """Apply audio effect to signal.

        Args:
            audio: Audio samples
            effect_type: Type of effect to apply
            **kwargs: Effect-specific parameters

        Returns:
            np.ndarray: Processed audio
        """
        effects = {
            "reverb": self.reverb,
            "echo": self.echo,
            "chorus": self.chorus,
            "delay": self.delay,
            "compression": self.compression,
            "normalization": self.normalize,
            "fade_in": self.fade_in,
            "fade_out": self.fade_out,
        }

        if effect_type not in effects:
            raise ValueError(f"Unknown effect: {effect_type}")

        return effects[effect_type](audio, **kwargs)

    def reverb(self, audio, room_size=0.5, delay_ms=50, decay=0.5):
        """Add reverb effect to audio.

        Args:
            audio: Input audio
            room_size: Room size parameter (0-1)
            delay_ms: Delay time in milliseconds
            decay: Decay amount (0-1)

        Returns:
            np.ndarray: Audio with reverb
        """
        delay_samples = int(delay_ms * self.sample_rate / 1000)
        reverb = audio.copy()

        for i in range(1, 5):
            delayed = np.zeros_like(audio)
            delayed[delay_samples * i:] = audio[:-delay_samples * i]
            reverb += delayed * (decay ** i)

        return reverb / np.max(np.abs(reverb))

    def echo(self, audio, delay_ms=500, decay=0.6, repeats=2):
        """Add echo/repetition effect.

        Args:
            audio: Input audio
            delay_ms: Delay time in milliseconds
            decay: Decay per repetition
            repeats: Number of repetitions

        Returns:
            np.ndarray: Audio with echo
        """
        delay_samples = int(delay_ms * self.sample_rate / 1000)
        output = audio.copy()

        for i in range(repeats):
            delayed = np.zeros_like(audio)
            shift = delay_samples * (i + 1)
            delayed[shift:] = audio[:-shift] * (decay ** (i + 1))
            output += delayed

        return output / np.max(np.abs(output))

    def chorus(self, audio, rate_hz=1.5, depth=0.02):
        """Add chorus effect (pitch modulation).

        Args:
            audio: Input audio
            rate_hz: LFO rate in Hz
            depth: Modulation depth

        Returns:
            np.ndarray: Audio with chorus effect
        """
        samples = np.arange(len(audio))
        delay_samples = 10  # Base delay
        lfo = depth * np.sin(2 * np.pi * rate_hz * samples / self.sample_rate)
        varied_delay = delay_samples * (1 + lfo)

        chorus_output = np.interp(
            samples - varied_delay,
            np.arange(len(audio)),
            audio
        )

        return (audio + chorus_output) / 2

    def delay(self, audio, delay_ms=200, feedback=0.6, num_repeats=1):
        """Add delay effect.

        Args:
            audio: Input audio
            delay_ms: Delay time in milliseconds
            feedback: Feedback amount (0-1)
            num_repeats: Number of repeats

        Returns:
            np.ndarray: Audio with delay
        """
        delay_samples = int(delay_ms * self.sample_rate / 1000)
        output = audio.copy()

        for _ in range(num_repeats):
            delayed = np.zeros_like(audio)
            delayed[delay_samples:] = output[:-delay_samples] * feedback
            output = output + delayed

        return output / np.max(np.abs(output))

    def compression(self, audio, threshold=0.5, ratio=4, attack_ms=5, release_ms=50):
        """Apply dynamic range compression.

        Args:
            audio: Input audio
            threshold: Compression threshold
            ratio: Compression ratio
            attack_ms: Attack time in milliseconds
            release_ms: Release time in milliseconds

        Returns:
            np.ndarray: Compressed audio
        """
        attack_samples = int(attack_ms * self.sample_rate / 1000)
        release_samples = int(release_ms * self.sample_rate / 1000)

        output = audio.copy()
        envelope = np.zeros_like(audio)

        for i in range(len(audio)):
            if i == 0:
                envelope[i] = abs(audio[i])
            else:
                level = abs(audio[i])
                if level > envelope[i - 1]:
                    # Attack
                    envelope[i] = envelope[i - 1] + (level - envelope[i - 1]) / attack_samples
                else:
                    # Release
                    envelope[i] = envelope[i - 1] - (envelope[i - 1] - level) / release_samples

        # Apply compression
        gain = np.ones_like(audio)
        above_threshold = envelope > threshold

        if np.any(above_threshold):
            gain[above_threshold] = (threshold + (envelope[above_threshold] - threshold) / ratio) / envelope[above_threshold]

        return output * gain

    def normalize(self, audio, target_level=0.9):
        """Normalize audio to target level.

        Args:
            audio: Input audio
            target_level: Target amplitude level (0-1)

        Returns:
            np.ndarray: Normalized audio
        """
        max_val = np.max(np.abs(audio))
        if max_val > 0:
            return audio * (target_level / max_val)
        return audio

    def fade_in(self, audio, duration_ms=1000):
        """Apply fade-in effect.

        Args:
            audio: Input audio
            duration_ms: Fade duration in milliseconds

        Returns:
            np.ndarray: Audio with fade-in
        """
        fade_samples = int(duration_ms * self.sample_rate / 1000)
        fade_samples = min(fade_samples, len(audio))

        output = audio.copy()
        envelope = np.ones(len(audio))
        envelope[:fade_samples] = np.linspace(0, 1, fade_samples)

        return output * envelope

    def fade_out(self, audio, duration_ms=1000):
        """Apply fade-out effect.

        Args:
            audio: Input audio
            duration_ms: Fade duration in milliseconds

        Returns:
            np.ndarray: Audio with fade-out
        """
        fade_samples = int(duration_ms * self.sample_rate / 1000)
        fade_samples = min(fade_samples, len(audio))

        output = audio.copy()
        envelope = np.ones(len(audio))
        envelope[-fade_samples:] = np.linspace(1, 0, fade_samples)

        return output * envelope

    def equalize(self, audio, gain_low=0, gain_mid=0, gain_high=0, frequencies=None):
        """Apply parametric equalization.

        Args:
            audio: Input audio
            gain_low: Gain adjustment for low frequencies (dB)
            gain_mid: Gain adjustment for mid frequencies (dB)
            gain_high: Gain adjustment for high frequencies (dB)
            frequencies: Custom frequency settings

        Returns:
            np.ndarray: Equalized audio
        """
        # Simplified EQ implementation
        from scipy.signal import butter, sosfilt

        output = audio.copy()

        # Low shelf (< 200 Hz)
        if gain_low != 0:
            sos = butter(2, 200, 'low', fs=self.sample_rate, output='sos')
            low_band = sosfilt(sos, audio)
            gain_factor = 10 ** (gain_low / 20)
            output += (low_band - audio) * (gain_factor - 1)

        return output

    def mix(self, *audio_tracks, volumes=None):
        """Mix multiple audio tracks.

        Args:
            *audio_tracks: Audio arrays to mix
            volumes: Volume levels for each track (0-1)

        Returns:
            np.ndarray: Mixed audio
        """
        if volumes is None:
            volumes = [1.0] * len(audio_tracks)

        # Pad to same length
        max_length = max(len(track) for track in audio_tracks)
        mixed = np.zeros(max_length)

        for track, volume in zip(audio_tracks, volumes):
            padded = np.zeros(max_length)
            padded[:len(track)] = track
            mixed += padded * volume

        # Normalize to prevent clipping
        max_val = np.max(np.abs(mixed))
        if max_val > 1.0:
            mixed = mixed / max_val

        return mixed

"""
Content Moderation
Filters and validates generated creative content for safety and quality.
"""


class ContentModerator:
    """Moderates generated content for safety, compliance, and quality."""

    def __init__(self):
        self.filter_list = self.load_filter_list()
        self.bias_checker = self.load_bias_checker()
        self.copyright_db = self.load_copyright_database()

    def moderate_content(self, content, content_type="text", strict=False):
        """Moderate generated content.

        Args:
            content: Content to moderate
            content_type: Type of content ('text', 'image', 'audio', 'meme')
            strict: Use strict filtering if True

        Returns:
            dict: Moderation results with pass/fail and issues found
        """
        results = {
            "content": content,
            "content_type": content_type,
            "passed": True,
            "issues": [],
            "scores": {}
        }

        if content_type == "text":
            self._moderate_text(content, results, strict)
        elif content_type == "image":
            self._moderate_image(content, results, strict)
        elif content_type == "audio":
            self._moderate_audio(content, results, strict)
        elif content_type == "meme":
            self._moderate_meme(content, results, strict)

        return results

    def _moderate_text(self, text, results, strict):
        """Moderate text content.

        Args:
            text: Text to moderate
            results: Results dictionary to update
            strict: Use strict filtering
        """
        # Check for inappropriate content
        inappropriate_score = self.detect_inappropriate_language(text)
        results["scores"]["inappropriate"] = inappropriate_score

        if strict and inappropriate_score > 0.3:
            results["passed"] = False
            results["issues"].append("Contains potentially inappropriate language")
        elif inappropriate_score > 0.7:
            results["passed"] = False
            results["issues"].append("Contains inappropriate language")

        # Check for bias
        bias_score = self.detect_bias(text)
        results["scores"]["bias"] = bias_score

        if bias_score > 0.5:
            results["passed"] = False
            results["issues"].append("Content may contain biased language")

        # Check factual accuracy
        accuracy = self.verify_factual_accuracy(text)
        results["scores"]["factual_accuracy"] = accuracy

        if accuracy < 0.5:
            results["issues"].append("Content may contain inaccuracies")

    def _moderate_image(self, image, results, strict):
        """Moderate image content.

        Args:
            image: Image to moderate
            results: Results dictionary to update
            strict: Use strict filtering
        """
        # Check for inappropriate visual content
        nsfw_score = self.detect_nsfw_content(image)
        results["scores"]["nsfw"] = nsfw_score

        if nsfw_score > 0.5:
            results["passed"] = False
            results["issues"].append("Image contains inappropriate content")

        # Check for violence
        violence_score = self.detect_violence(image)
        results["scores"]["violence"] = violence_score

        if violence_score > 0.5:
            results["passed"] = False
            results["issues"].append("Image may contain violent content")

        # Check for copyright
        copyright_match = self.check_copyright(image)
        if copyright_match:
            results["passed"] = False
            results["issues"].append(f"Image matches copyrighted content: {copyright_match}")

    def _moderate_audio(self, audio, results, strict):
        """Moderate audio content.

        Args:
            audio: Audio to moderate
            results: Results dictionary to update
            strict: Use strict filtering
        """
        # Speech-to-text for audio analysis
        transcribed = self.transcribe_audio(audio)

        # Moderate transcription
        self._moderate_text(transcribed, results, strict)

    def _moderate_meme(self, meme, results, strict):
        """Moderate meme content.

        Args:
            meme: Meme to moderate
            results: Results dictionary to update
            strict: Use strict filtering
        """
        # Check for hate speech
        hate_score = self.detect_hate_speech(meme)
        results["scores"]["hate_speech"] = hate_score

        if hate_score > 0.5:
            results["passed"] = False
            results["issues"].append("Meme may contain hate speech")

        # Check for offensive content
        offensive_score = self.detect_offensive_content(meme)
        results["scores"]["offensive"] = offensive_score

        if strict and offensive_score > 0.3:
            results["passed"] = False
            results["issues"].append("Meme contains potentially offensive content")
        elif offensive_score > 0.7:
            results["passed"] = False
            results["issues"].append("Meme contains offensive content")

    def detect_inappropriate_language(self, text):
        """Detect inappropriate language in text.

        Args:
            text: Text to analyze

        Returns:
            float: Inappropriateness score (0-1)
        """
        pass

    def detect_bias(self, text):
        """Detect bias in text.

        Args:
            text: Text to analyze

        Returns:
            float: Bias score (0-1)
        """
        pass

    def verify_factual_accuracy(self, text):
        """Verify factual accuracy of claims in text.

        Args:
            text: Text to verify

        Returns:
            float: Accuracy score (0-1)
        """
        pass

    def detect_nsfw_content(self, image):
        """Detect NSFW content in image.

        Args:
            image: Image to analyze

        Returns:
            float: NSFW score (0-1)
        """
        pass

    def detect_violence(self, image):
        """Detect violent content in image.

        Args:
            image: Image to analyze

        Returns:
            float: Violence score (0-1)
        """
        pass

    def check_copyright(self, image):
        """Check if image matches copyrighted content.

        Args:
            image: Image to check

        Returns:
            str: Copyright match details or None
        """
        pass

    def detect_hate_speech(self, content):
        """Detect hate speech in content.

        Args:
            content: Content to analyze

        Returns:
            float: Hate speech score (0-1)
        """
        pass

    def detect_offensive_content(self, content):
        """Detect offensive content.

        Args:
            content: Content to analyze

        Returns:
            float: Offensiveness score (0-1)
        """
        pass

    def transcribe_audio(self, audio):
        """Transcribe audio to text for analysis.

        Args:
            audio: Audio data

        Returns:
            str: Transcribed text
        """
        pass

    def load_filter_list(self):
        """Load inappropriate content filter list."""
        pass

    def load_bias_checker(self):
        """Load bias detection model."""
        pass

    def load_copyright_database(self):
        """Load copyright verification database."""
        pass

    def get_moderation_report(self, moderation_results, format="text"):
        """Generate moderation report.

        Args:
            moderation_results: Results from moderate_content()
            format: Report format ('text', 'json', 'html')

        Returns:
            str: Formatted moderation report
        """
        if format == "text":
            report = f"Moderation Report\n"
            report += f"Content Type: {moderation_results['content_type']}\n"
            report += f"Status: {'PASS' if moderation_results['passed'] else 'FAIL'}\n"

            if moderation_results['issues']:
                report += "\nIssues Found:\n"
                for issue in moderation_results['issues']:
                    report += f"  - {issue}\n"

            report += "\nScores:\n"
            for key, score in moderation_results['scores'].items():
                report += f"  {key}: {score:.2f}\n"

            return report

        return str(moderation_results)

"""
Creative Quality Assessment
Evaluates the quality of generated creative content across multiple dimensions.
"""


class CreativeQualityAssessor:
    """Assesses quality of generated creative content."""

    def assess_content_quality(self, content, content_type="music"):
        """Assess quality of generated content based on type.

        Args:
            content: Generated content to assess
            content_type: Type of content ('music', 'meme', 'image', etc.)

        Returns:
            dict: Quality metrics and overall score
        """
        if content_type == "music":
            return self.assess_music_quality(content)
        elif content_type == "meme":
            return self.assess_meme_quality(content)
        elif content_type == "image":
            return self.assess_image_quality(content)

    def assess_music_quality(self, audio):
        """Assess quality of generated music.

        Args:
            audio: Audio samples

        Returns:
            dict: Music quality metrics
        """
        metrics = {
            "coherence": self.measure_musical_coherence(audio),
            "variety": self.measure_melodic_variety(audio),
            "rhythm_consistency": self.measure_rhythm_consistency(audio),
            "harmonic_quality": self.measure_harmonic_quality(audio),
        }

        overall_score = sum(metrics.values()) / len(metrics)
        return {"metrics": metrics, "overall_score": overall_score}

    def assess_meme_quality(self, meme):
        """Assess quality of generated meme.

        Args:
            meme: Meme content (text or image)

        Returns:
            dict: Meme quality metrics
        """
        metrics = {
            "humor_score": self.assess_humor(meme),
            "clarity": self.assess_visual_clarity(meme),
            "originality": self.assess_originality(meme),
            "relatability": self.assess_relatability(meme),
        }

        overall_score = sum(metrics.values()) / len(metrics)
        return {"metrics": metrics, "overall_score": overall_score}

    def assess_image_quality(self, image):
        """Assess quality of generated image.

        Args:
            image: Image content

        Returns:
            dict: Image quality metrics
        """
        metrics = {
            "clarity": self.measure_image_clarity(image),
            "coherence": self.measure_content_coherence(image),
            "aesthetic": self.measure_aesthetic_quality(image),
            "technical": self.measure_technical_quality(image),
        }

        overall_score = sum(metrics.values()) / len(metrics)
        return {"metrics": metrics, "overall_score": overall_score}

    def measure_musical_coherence(self, audio):
        """Measure how coherent/structured the music is.

        Args:
            audio: Audio samples

        Returns:
            float: Coherence score (0-1)
        """
        pass

    def measure_melodic_variety(self, audio):
        """Measure melodic variety and diversity.

        Args:
            audio: Audio samples

        Returns:
            float: Variety score (0-1)
        """
        pass

    def measure_rhythm_consistency(self, audio):
        """Measure rhythmic consistency and timing.

        Args:
            audio: Audio samples

        Returns:
            float: Consistency score (0-1)
        """
        pass

    def measure_harmonic_quality(self, audio):
        """Measure harmonic quality and chord progression.

        Args:
            audio: Audio samples

        Returns:
            float: Harmonic quality score (0-1)
        """
        pass

    def assess_humor(self, meme):
        """Assess humor level of meme.

        Args:
            meme: Meme content

        Returns:
            float: Humor score (0-1)
        """
        pass

    def assess_visual_clarity(self, meme):
        """Assess visual clarity and readability.

        Args:
            meme: Meme content

        Returns:
            float: Clarity score (0-1)
        """
        pass

    def assess_originality(self, meme):
        """Assess originality of meme.

        Args:
            meme: Meme content

        Returns:
            float: Originality score (0-1)
        """
        pass

    def assess_relatability(self, meme):
        """Assess how relatable the meme is.

        Args:
            meme: Meme content

        Returns:
            float: Relatability score (0-1)
        """
        pass

    def measure_image_clarity(self, image):
        """Measure sharpness and clarity of image.

        Args:
            image: Image data

        Returns:
            float: Clarity score (0-1)
        """
        pass

    def measure_content_coherence(self, image):
        """Measure coherence of visual content.

        Args:
            image: Image data

        Returns:
            float: Coherence score (0-1)
        """
        pass

    def measure_aesthetic_quality(self, image):
        """Measure aesthetic appeal of image.

        Args:
            image: Image data

        Returns:
            float: Aesthetic score (0-1)
        """
        pass

    def measure_technical_quality(self, image):
        """Measure technical quality (colors, contrast, etc.).

        Args:
            image: Image data

        Returns:
            float: Technical quality score (0-1)
        """
        pass