import boto3
import json
import base64
from PIL import Image
import io

# Initialize Bedrock client
bedrock = boto3.client(
    service_name='bedrock-runtime',
    region_name='us-west-2'
)

def generate_image_with_bedrock(prompt, model_id="stability.stable-diffusion-xl", height=1024, width=1024):
    """Generate an image using Amazon Bedrock with Stability AI model"""
    
    # Request body for Stability AI models
    request_body = {
        "text_prompts": [{"text": prompt}],
        "cfg_scale": 7,
        "steps": 50,
        "seed": 42,
        "style_preset": "photographic",
        "height": height,
        "width": width
    }
    
    # Invoke the model
    response = bedrock.invoke_model(
        modelId=model_id,
        body=json.dumps(request_body)
    )
    
    # Parse the response
    response_body = json.loads(response.get('body').read())
    
    # For Stability AI models, the image is base64 encoded
    image_b64 = response_body.get('artifacts')[0].get('base64')
    
    # Decode the image
    image_data = base64.b64decode(image_b64)
    image = Image.open(io.BytesIO(image_data))
    
    return image

# Example usage
if __name__ == "__main__":
    prompt = "A futuristic cityscape with flying cars and neon lights"
    image = generate_image_with_bedrock(prompt)
    image.save("aws_bedrock_generated_image.png")
    print("Image generated and saved successfully!")

import boto3
import sagemaker
from sagemaker.huggingface import HuggingFaceModel
import json

# Initialize SageMaker session
sagemaker_session = sagemaker.Session()
role = "arn:aws:iam::123456789012:role/SageMakerExecutionRole"
bucket = "your-s3-bucket"

# Define Hugging Face model
huggingface_model = HuggingFaceModel(
    model_data="s3://your-bucket/stable-diffusion-model.tar.gz",
    role=role,
    transformers_version="4.26.0",
    pytorch_version="1.13.1",
    py_version="py39",
)

# Deploy the model
predictor = huggingface_model.deploy(
    initial_instance_count=1,
    instance_type="ml.g4dn.xlarge",
    endpoint_name="stable-diffusion-endpoint"
)

def generate_image(prompt):
    """Generate an image using the deployed Stable Diffusion model"""
    payload = {
        "inputs": prompt,
        "parameters": {
            "height": 512,
            "width": 512,
            "num_inference_steps": 50,
            "guidance_scale": 7.5
        }
    }
    
    # Invoke the endpoint
    response = predictor.predict(json.dumps(payload))
    
    # Parse and save the response
    # Implementation depends on your model's output format
    
    return response

# Clean up resources when done
def cleanup():
    predictor.delete_endpoint()
    predictor.delete_model()

import vertexai
from vertexai.preview.vision_models import Image, ImageGenerationModel
import os

# Initialize Vertex AI
vertexai.init(project="your-project-id", location="us-central1")

def generate_image_with_imagen(prompt, output_file="gcp_imagen_output.png"):
    """Generate an image using Vertex AI's Imagen model"""
    
    # Load the image generation model
    model = ImageGenerationModel.from_pretrained("imagegeneration@002")
    
    # Generate the image
    response = model.generate_images(
        prompt=prompt,
        # Optional parameters
        guidance_scale=7.0,
        negative_prompt="blurry, bad quality, unrealistic",
        samples=1,
        seed=42,
        # Image dimensions - see API docs for available options
        resolution="1024x1024",
    )
    
    # Save the generated image
    image = response[0]
    image.save(output_file)
    print(f"Image saved to {output_file}")
    return image

# Example usage
if __name__ == "__main__":
    prompt = "An astronaut riding a horse on Mars, photorealistic"
    generate_image_with_imagen(prompt)

import vertexai
from vertexai.preview.generative_models import GenerativeModel
import time
import os

# Initialize Vertex AI
vertexai.init(project="your-project-id", location="us-central1")

def generate_video_with_vertex_ai(prompt, output_file="gcp_generated_video.mp4"):
    """Generate a video using Vertex AI's video generation capabilities"""
    
    # Load the generative model for video
    model = GenerativeModel("gemini-1.5-flash")
    
    # Generate the video
    response = model.generate_content(
        [prompt],
        generation_config={
            "temperature": 0.9,
            "max_output_tokens": 2048,
            "top_p": 1.0,
            "top_k": 32,
        },
        # Video generation specific parameters
        video_dimensions="1280x720",
        video_length_seconds=5,
    )
    
    # Process the response
    if hasattr(response, 'video'):
        with open(output_file, 'wb') as f:
            f.write(response.video)
        print(f"Video saved to {output_file}")
        return output_file
    else:
        print("No video was generated.")
        return None

# Example usage
if __name__ == "__main__":
    prompt = "Generate a 5-second video of a spaceship landing on an alien planet with a sunset in the background"
    generate_video_with_vertex_ai(prompt)

import os
import requests
import json
from PIL import Image
import io
import base64

def generate_image_with_azure_openai(prompt, size="1024x1024", output_file="azure_dalle_output.png"):
    """Generate an image using Azure OpenAI Service with DALL-E model"""
    
    # Azure OpenAI configuration
    api_key = os.environ.get("AZURE_OPENAI_API_KEY")
    api_base = os.environ.get("AZURE_OPENAI_ENDPOINT")
    api_version = "2023-12-01-preview"
    deployment_name = "dall-e-3"  # Your DALL-E deployment name
    
    # Prepare the request URL
    url = f"{api_base}/openai/deployments/{deployment_name}/images/generations?api-version={api_version}"
    
    # Prepare the request headers
    headers = {
        "Content-Type": "application/json",
        "api-key": api_key
    }
    
    # Prepare the request body
    body = {
        "prompt": prompt,
        "size": size,
        "n": 1,
        "quality": "standard",  # or "hd" for higher quality
        "style": "natural"  # or "vivid" for more vibrant images
    }
    
    # Make the request
    response = requests.post(url, headers=headers, json=body)
    
    if response.status_code == 200:
        # Extract the image URL from the response
        response_data = response.json()
        image_url = response_data["data"][0]["url"]
        
        # Download the image
        image_response = requests.get(image_url)
        image = Image.open(io.BytesIO(image_response.content))
        image.save(output_file)
        
        return image
    else:
        print(f"Error: {response.status_code}")
        print(response.text)
        return None

# Example usage
if __name__ == "__main__":
    prompt = "A serene Japanese garden with a koi pond, cherry blossoms, and a small wooden bridge"
    generate_image_with_azure_openai(prompt)

import os
from azure.ai.ml import MLClient
from azure.ai.ml.entities import Environment, BuildContext
from azure.ai.ml.entities import ManagedOnlineEndpoint, ManagedOnlineDeployment
from azure.identity import DefaultAzureCredential

def deploy_video_synthesis_model():
    """Deploy a custom video synthesis model on Azure Machine Learning"""
    
    # Initialize ML client
    credential = DefaultAzureCredential()
    ml_client = MLClient(
        credential=credential,
        subscription_id="your-subscription-id",
        resource_group_name="your-resource-group",
        workspace_name="your-workspace"
    )
    
    # Create a compute cluster if needed
    if "gpu-cluster" not in ml_client.compute.list():
        from azure.ai.ml.entities import AmlCompute
        gpu_compute = AmlCompute(
            name="gpu-cluster",
            size="Standard_NC6s_v3",
            min_instances=0,
            max_instances=4,
            tier="Dedicated"
        )
        ml_client.begin_create_or_update(gpu_compute).result()
    
    # Create a custom environment for video synthesis
    env = Environment(
        name="video-synthesis-env",
        description="Environment for video synthesis models",
        build=BuildContext(
            path="./dockerfile",
        ),
        image="mcr.microsoft.com/azureml/curated/pytorch-1.10-cuda11.3:latest",
    )
    ml_client.environments.create_or_update(env)
    
    # Create an online endpoint
    endpoint_name = "video-synthesis-endpoint"
    endpoint = ManagedOnlineEndpoint(
        name=endpoint_name,
        description="Endpoint for video synthesis",
        auth_mode="key",
    )
    ml_client.begin_create_or_update(endpoint).result()
    
    # Create a deployment
    deployment = ManagedOnlineDeployment(
        name="video-synthesis-deployment",
        endpoint_name=endpoint_name,
        model="azureml:video-synthesis-model:1",
        environment="azureml:video-synthesis-env:1",
        code_configuration=CodeConfiguration(
            code="./src",
            scoring_script="score.py"
        ),
        instance_type="Standard_NC6s_v3",
        instance_count=1
    )
    ml_client.begin_create_or_update(deployment).result()
    
    return endpoint_name

# Example scoring script for the deployment (would be in ./src/score.py)
"""
import os
import torch
import torchvision
import tempfile
import json
import numpy as np
from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler

def init():
    global model
    model = DiffusionPipeline.from_pretrained(
        "damo-vilab/text-to-video-ms-1.7b",
        torch_dtype=torch.float16,
        variant="fp16"
    )
    model.scheduler = DPMSolverMultistepScheduler.from_config(model.scheduler.config)
    model = model.to("cuda")

def run(raw_data):
    try:
        request_data = json.loads(raw_data)
        prompt = request_data.get("prompt", "A spaceship flying through a nebula")
        num_frames = request_data.get("num_frames", 16)
        
        # Generate the video frames
        with torch.autocast("cuda"):
            video_frames = model(prompt, num_inference_steps=25, num_frames=num_frames).frames
        
        # Save frames to a video file
        temp_dir = tempfile.mkdtemp()
        video_path = os.path.join(temp_dir, "output.mp4")
        torchvision.io.write_video(video_path, video_frames, fps=8)
        
        # Return the video file path
        return {"video_path": video_path}
    except Exception as e:
        return {"error": str(e)}
"""

import torch
from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline
from diffusers import DPMSolverMultistepScheduler
import numpy as np
from PIL import Image

def setup_stable_diffusion(device="cuda"):
    """Set up the Stable Diffusion pipeline"""
    
    # Initialize text-to-image pipeline
    txt2img_pipe = StableDiffusionPipeline.from_pretrained(
        "runwayml/stable-diffusion-v1-5",
        torch_dtype=torch.float16,
        safety_checker=None  # Remove safety checker for faster inference (use responsibly)
    )
    txt2img_pipe.scheduler = DPMSolverMultistepScheduler.from_config(txt2img_pipe.scheduler.config)
    txt2img_pipe = txt2img_pipe.to(device)
    
    # Initialize image-to-image pipeline (for modifications)
    img2img_pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
        "runwayml/stable-diffusion-v1-5",
        torch_dtype=torch.float16,
        safety_checker=None
    )
    img2img_pipe.scheduler = DPMSolverMultistepScheduler.from_config(img2img_pipe.scheduler.config)
    img2img_pipe = img2img_pipe.to(device)
    
    return txt2img_pipe, img2img_pipe

def generate_image(prompt, pipe, height=512, width=512, num_inference_steps=30, guidance_scale=7.5):
    """Generate an image from a text prompt"""
    
    with torch.no_grad():
        image = pipe(
            prompt=prompt,
            height=height,
            width=width,
            num_inference_steps=num_inference_steps,
            guidance_scale=guidance_scale
        ).images[0]
    
    return image

def modify_image(prompt, init_image, pipe, strength=0.75, num_inference_steps=30, guidance_scale=7.5):
    """Modify an existing image with a text prompt"""
    
    with torch.no_grad():
        image = pipe(
            prompt=prompt,
            image=init_image,
            strength=strength,
            num_inference_steps=num_inference_steps,
            guidance_scale=guidance_scale
        ).images[0]
    
    return image

# Example usage
if __name__ == "__main__":
    # Check if CUDA is available
    device = "cuda" if torch.cuda.is_available() else "cpu"
    print(f"Using device: {device}")
    
    # Set up pipelines
    txt2img_pipe, img2img_pipe = setup_stable_diffusion(device)
    
    # Generate an image
    prompt = "A cyberpunk city at night with neon signs and flying cars"
    image = generate_image(prompt, txt2img_pipe)
    image.save("independent_sd_image.png")
    
    # Modify the generated image
    new_prompt = "A cyberpunk city at sunset with neon signs and flying cars"
    modified_image = modify_image(new_prompt, image, img2img_pipe, strength=0.5)
    modified_image.save("independent_sd_modified.png")

import torch
from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
import imageio
import numpy as np
import tempfile
import os

def setup_video_pipeline(device="cuda"):
    """Set up the text-to-video pipeline"""
    
    # Load the pipeline
    pipe = DiffusionPipeline.from_pretrained(
        "damo-vilab/text-to-video-ms-1.7b",
        torch_dtype=torch.float16,
        variant="fp16"
    )
    pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
    pipe = pipe.to(device)
    
    return pipe

def generate_video(prompt, pipe, num_frames=16, num_inference_steps=25, fps=8, output_file="independent_video.mp4"):
    """Generate a video from a text prompt"""
    
    # Generate video frames
    with torch.autocast(device_type="cuda"):
        video_frames = pipe(
            prompt,
            num_inference_steps=num_inference_steps,
            num_frames=num_frames
        ).frames
    
    # Convert frames to numpy arrays
    video_frames = [frame.permute(1, 2, 0).cpu().numpy() for frame in video_frames]
    
    # Normalize to 0-255 and convert to uint8
    video_frames = [(frame * 255).astype(np.uint8) for frame in video_frames]
    
    # Save as video
    imageio.mimsave(output_file, video_frames, fps=fps)
    print(f"Video saved to {output_file}")
    
    return output_file

# Example usage
if __name__ == "__main__":
    # Check if CUDA is available
    device = "cuda" if torch.cuda.is_available() else "cpu"
    print(f"Using device: {device}")
    
    # Set up pipeline
    pipe = setup_video_pipeline(device)
    
    # Generate a video
    prompt = "A spaceship taking off from Earth and flying to Mars"
    output_file = generate_video(prompt, pipe)

# product_visualization_system.py
import os
import json
import time
import boto3
import vertexai
import requests
from PIL import Image
import io
import threading
import queue

# Cloud provider selection utility
class CloudProviderSelector:
    def __init__(self, available_providers=["aws", "gcp", "azure"]):
        self.providers = available_providers
        self.metrics = {provider: {"latency": [], "cost": [], "quality": []} for provider in available_providers}
    
    def select_provider(self, task_type, resolution, priority="balanced"):
        """Select the best provider based on task type and priority"""
        if priority == "cost":
            # Return the cheapest option
            if task_type == "image" and "gcp" in self.providers:
                return "gcp"  # GCP generally has lower per-image costs
            elif task_type == "video" and "aws" in self.providers:
                return "aws"  # Using custom implementation on SageMaker
        elif priority == "quality":
            # Return the highest quality option
            if task_type == "image" and "azure" in self.providers:
                return "azure"  # DALL-E models often produce high quality
            elif task_type == "video" and "gcp" in self.providers:
                return "gcp"  # Better native video support
        elif priority == "speed":
            # Return the fastest option based on metrics
            fastest = min(self.providers, key=lambda p: sum(self.metrics[p]["latency"])/max(len(self.metrics[p]["latency"]), 1))
            return fastest
        
        # Balanced approach - default
        if task_type == "image":
            return "gcp"  # Good balance of cost/quality for images
        else:
            return "azure"  # Good balance for videos

# AWS Implementation
class AWSProvider:
    def __init__(self, region="us-west-2"):
        self.bedrock = boto3.client('bedrock-runtime', region_name=region)
    
    def generate_image(self, prompt, width=1024, height=1024):
        """Generate an image using AWS Bedrock with Stability AI"""
        start_time = time.time()
        
        request_body = {
            "text_prompts": [{"text": prompt}],
            "cfg_scale": 7,
            "