import os
import socket
import requests
from fastapi import FastAPI, File, UploadFile, Form
from fastapi.responses import FileResponse
from PIL import Image
import torch
from diffusers import (
    DiffusionPipeline,
    AutoencoderKL,
    StableDiffusionControlNetPipeline,
    ControlNetModel,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionImg2ImgPipeline,
    StableDiffusionControlNetImg2ImgPipeline,
    DPMSolverMultistepScheduler,
    EulerDiscreteScheduler
)
import random
import time
import tempfile

app = FastAPI()

BASE_MODEL = "SG161222/Realistic_Vision_V5.1_noVAE"

# Initialize both pipelines
vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", torch_dtype=torch.float16)
controlnet = ControlNetModel.from_pretrained("monster-labs/control_v1p_sd15_qrcode_monster", torch_dtype=torch.float16)
main_pipe = StableDiffusionControlNetPipeline.from_pretrained(
    BASE_MODEL,
    controlnet=controlnet,
    vae=vae,
    safety_checker=None,
    torch_dtype=torch.float16,
).to("cuda")
image_pipe = StableDiffusionControlNetImg2ImgPipeline(**main_pipe.components)

# Sampler map
SAMPLER_MAP = {
    "DPM++ Karras SDE": lambda config: DPMSolverMultistepScheduler.from_config(config, use_karras=True, algorithm_type="sde-dpmsolver++"),
    "Euler": lambda config: EulerDiscreteScheduler.from_config(config),
}

def center_crop_resize(img, output_size=(512, 512)):
    width, height = img.size

    # Calculate dimensions to crop to the center
    new_dimension = min(width, height)
    left = (width - new_dimension)/2
    top = (height - new_dimension)/2
    right = (width + new_dimension)/2
    bottom = (height + new_dimension)/2

    # Crop and resize
    img = img.crop((left, top, right, bottom))
    img = img.resize(output_size)

    return img

def common_upscale(samples, width, height, upscale_method, crop=False):
        if crop == "center":
            old_width = samples.shape[3]
            old_height = samples.shape[2]
            old_aspect = old_width / old_height
            new_aspect = width / height
            x = 0
            y = 0
            if old_aspect > new_aspect:
                x = round((old_width - old_width * (new_aspect / old_aspect)) / 2)
            elif old_aspect < new_aspect:
                y = round((old_height - old_height * (old_aspect / new_aspect)) / 2)
            s = samples[:,:,y:old_height-y,x:old_width-x]
        else:
            s = samples

        return torch.nn.functional.interpolate(s, size=(height, width), mode=upscale_method)

def upscale(samples, upscale_method, scale_by):
        #s = samples.copy()
        width = round(samples["images"].shape[3] * scale_by)
        height = round(samples["images"].shape[2] * scale_by)
        s = common_upscale(samples["images"], width, height, upscale_method, "disabled")
        return (s)

#

def convert_to_pil(base64_image):
    pil_image = processing_utils.decode_base64_to_image(base64_image)
    return pil_image

def convert_to_base64(pil_image):
    base64_image = processing_utils.encode_pil_to_base64(pil_image)
    return base64_image

# Inference function
def inference(
    control_image: Image.Image,
    prompt: str,
    negative_prompt: str,
    guidance_scale: float = 8.0,
    controlnet_conditioning_scale: float = 1,
    control_guidance_start: float = 1,    
    control_guidance_end: float = 1,
    upscaler_strength: float = 0.5,
    seed: int = -1,
    sampler = "DPM++ Karras SDE",
    #profile: gr.OAuthProfile | None = None,
):
    start_time = time.time()
    start_time_struct = time.localtime(start_time)
    start_time_formatted = time.strftime("%H:%M:%S", start_time_struct)
    print(f"Inference started at {start_time_formatted}")
    
    # Generate the initial image
    #init_image = init_pipe(prompt).images[0]

    # Rest of your existing code
    control_image_small = center_crop_resize(control_image)
    control_image_large = center_crop_resize(control_image, (1024, 1024))

    main_pipe.scheduler = SAMPLER_MAP[sampler](main_pipe.scheduler.config)
    my_seed = random.randint(0, 2**32 - 1) if seed == -1 else seed
    generator = torch.Generator(device="cuda").manual_seed(my_seed)
    
    out = main_pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        image=control_image_small,
        guidance_scale=float(guidance_scale),
        controlnet_conditioning_scale=float(controlnet_conditioning_scale),
        generator=generator,
        control_guidance_start=float(control_guidance_start),
        control_guidance_end=float(control_guidance_end),
        num_inference_steps=15,
        output_type="latent"
    )
    upscaled_latents = upscale(out, "nearest-exact", 2)
    out_image = image_pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        control_image=control_image_large,        
        image=upscaled_latents,
        guidance_scale=float(guidance_scale),
        generator=generator,
        num_inference_steps=20,
        strength=upscaler_strength,
        control_guidance_start=float(control_guidance_start),
        control_guidance_end=float(control_guidance_end),
        controlnet_conditioning_scale=float(controlnet_conditioning_scale)
    )
    end_time = time.time()
    end_time_struct = time.localtime(end_time)
    end_time_formatted = time.strftime("%H:%M:%S", end_time_struct)
    print(f"Inference ended at {end_time_formatted}, taking {end_time-start_time}s")

    # Save image + metadata
    user_history.save_image(
        label=prompt,
        image=out_image["images"][0],
        profile=profile,
        metadata={
            "prompt": prompt,
            "negative_prompt": negative_prompt,
            "guidance_scale": guidance_scale,
            "controlnet_conditioning_scale": controlnet_conditioning_scale,
            "control_guidance_start": control_guidance_start,
            "control_guidance_end": control_guidance_end,
            "upscaler_strength": upscaler_strength,
            "seed": seed,
            "sampler": sampler,
        },
    )

    return out_image["images"][0], my_seed
        
import os

def generate_image_from_parameters(prompt, guidance_scale, controlnet_scale, controlnet_end, upscaler_strength, seed, sampler_type, image):
    try:
        # Save the uploaded image to a temporary file
        temp_image_path = f"/tmp/{int(time.time())}_{image.filename}"
        with open(temp_image_path, "wb") as temp_image:
            temp_image.write(image.file.read())

        # Open the uploaded image using PIL
        control_image = Image.open(temp_image_path)

        # Call existing inference function with the provided parameters
        generated_image, _, _, _ = inference(control_image, prompt, "", guidance_scale, controlnet_scale, 0, controlnet_end, upscaler_strength, seed, sampler_type)

        # Specify the desired output directory for saving generated images
        output_directory = "/home/user/app/generated_files"

        # Create the output directory if it doesn't exist
        os.makedirs(output_directory, exist_ok=True)

        # Generate a unique filename for the saved image
        filename = f"generated_image_{int(time.time())}.png"

        # Save the generated image to the permanent location
        output_path = os.path.join(output_directory, filename)
        generated_image.save(output_path, format="PNG")

        # Return the generated image path
        return output_path

    except Exception as e:
        # Handle exceptions and return an error message if something goes wrong
        return str(e)

@app.post("/generate_image")
async def generate_image(
    prompt: str = Form(...),
    guidance_scale: float = Form(...),
    controlnet_scale: float = Form(...),
    controlnet_end: float = Form(...),
    upscaler_strength: float = Form(...),
    seed: int = Form(...),
    sampler_type: str = Form(...),
    image: UploadFile = File(...)
):
    try:
        # Save the uploaded image to a temporary file
        temp_image_path = f"/tmp/{int(time.time())}_{image.filename}"
        with open(temp_image_path, "wb") as temp_image:
            temp_image.write(image.file.read())

        # Open the uploaded image using PIL
        control_image = Image.open(temp_image_path)

        # Call existing inference function with the provided parameters
        generated_image, _, _, _ = inference(control_image, prompt, "", guidance_scale, controlnet_scale, 0, controlnet_end, upscaler_strength, seed, sampler_type)

        # Specify the desired output directory for saving generated images
        output_directory = "/home/user/app/generated_files"

        # Create the output directory if it doesn't exist
        os.makedirs(output_directory, exist_ok=True)

        # Generate a unique filename for the saved image
        filename = f"generated_image_{int(time.time())}.png"

        # Save the generated image to the permanent location
        output_path = os.path.join(output_directory, filename)
        generated_image.save(output_path, format="PNG")

        # Return the generated image path
        return output_path

    except Exception as e:
        # Handle exceptions and return an error message if something goes wrong
        return str(e)

if __name__ == "__main__":
    import uvicorn

    # Get internal IP address
    internal_ip = socket.gethostbyname(socket.gethostname())

    # Get public IP address using a public API (this may not work if you are behind a router/NAT)
    try:
        public_ip = requests.get("http://api.ipify.org").text
    except requests.RequestException:
        public_ip = "Not Available"

    print(f"Internal URL: http://{internal_ip}:8000")
    print(f"Public URL: http://{public_ip}:8000")

    uvicorn.run(app, host="0.0.0.0", port=8000, reload=True)

if __name__ == "__main__":
    import uvicorn
    uvicorn.run("app:app", host="0.0.0.0", port=8000, reload=True)