serve

#!/usr/bin/env python3

from share import *
import config

import cv2
import einops
import numpy as np
import torch
import random, logging, json, base64, boto3, uuid, zipfile, urllib
from botocore.exceptions import ClientError
from io import BytesIO
from PIL import Image, ImageOps
import gc
from flask import Flask
from flask import Response
from flask import request

from pytorch_lightning import seed_everything
from annotator.util import HWC3
from annotator.canny import CannyDetector
from cldm.model import create_model, load_state_dict
from cldm.ddim_hacked import DDIMSampler

logging.basicConfig()
logging.getLogger().setLevel(logging.INFO)


class Predictor:

    def __init__(self, config_path, weight_path):
        self.apply_canny = CannyDetector()
        model = create_model(str(config_path)).cpu()
        model.load_state_dict(load_state_dict(str(weight_path), location='cuda'))
        self.model = model.cuda()
        logging.info("Model loaded")
        self.ddim_sampler = DDIMSampler(self.model)
        logging.info("DDIM sampler loaded")
        with open('prompts.txt', 'r') as f:
            prompts = f.readlines()
            self.prompts = [p.strip() for p in prompts]
        #self.prompts = random.sample(self.prompts, 3)
        logging.info(f"Running with {len(self.prompts)} prompts")


    def gen(self,
            img,
            num_samples,
            a_prompt='',
            n_prompt='',
            ddim_steps=20,
            guess_mode=False,
            strength=1.0,
            low_threshold=100,
            high_threshold=200,
            scale=9.0,
            seed=-1,
            eta=0.0):
        for i, prompt in enumerate(self.prompts):
            logging.info(f"Processing prompt N {i}: {prompt}")
            yield self.process(img=img,
                               prompt=prompt,
                               a_prompt=a_prompt,
                               n_prompt=n_prompt,
                               num_samples=num_samples,
                               ddim_steps=ddim_steps,
                               guess_mode=guess_mode,
                               strength=strength,
                               low_threshold=low_threshold,
                               high_threshold=high_threshold,
                               scale=scale,
                               seed=seed,
                               eta=eta)



    def process(self,
                img: np.ndarray,
                prompt: str,
                a_prompt='',
                n_prompt='',
                num_samples=1,
                ddim_steps=20,
                guess_mode=False,
                strength=1.0,
                low_threshold=100,
                high_threshold=200,
                scale=9.0,
                seed=42,
                eta=0.0):
        logging.info(f"Processing with seed: {seed}")
        config.save_memory = True
        with torch.no_grad():
            H, W, _ = img.shape

            detected_map = self.apply_canny(img, low_threshold, high_threshold)
            detected_map = HWC3(detected_map)
            logging.info(f"Edges detected with Canny on img size {H}x{W}")

            control = torch.from_numpy(detected_map.copy()).float().cuda() / 255.0
            control = torch.stack([control for _ in range(num_samples)], dim=0)
            control = einops.rearrange(control, 'b h w c -> b c h w').clone()

            if seed == -1:
                seed = random.randint(0, 65535)
            seed_everything(seed)

            if config.save_memory:
                self.model.low_vram_shift(is_diffusing=False)

            cond = {"c_concat": [control], "c_crossattn": [self.model.get_learned_conditioning([prompt + ', ' + a_prompt] * num_samples)]}
            un_cond = {"c_concat": None if guess_mode else [control], "c_crossattn": [self.model.get_learned_conditioning([n_prompt] * num_samples)]}
            shape = (4, H // 8, W // 8)

            if config.save_memory:
                self.model.low_vram_shift(is_diffusing=True)

            self.model.control_scales = [strength * (0.825 ** float(12 - i)) for i in range(13)] if guess_mode else ([strength] * 13)  # Magic number. IDK why. Perhaps because 0.825**12<0.01 but 0.826**12>0.01
            samples, _ = self.ddim_sampler.sample(ddim_steps, num_samples,
                                                  shape, cond, verbose=False, eta=eta,
                                                  unconditional_guidance_scale=scale,
                                                  unconditional_conditioning=un_cond)
            logging.info("ControlNet samples generated")
            if config.save_memory:
                self.model.low_vram_shift(is_diffusing=False)

            x_samples = self.model.decode_first_stage(samples)
            x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c') * 127.5 + 127.5).cpu().numpy().clip(0, 255).astype(np.uint8)

            results = [x_samples[i] for i in range(num_samples)]
        
        del samples, x_samples, control, cond, un_cond, detected_map
        torch.cuda.empty_cache()
        return results


def resize_image(input_image, resolution):
    H, W, C = input_image.shape
    H = float(H)
    W = float(W)
    max_HW = max(H, W) 
    k = float(resolution) / max_HW
    H *= k
    W *= k
    H = int(np.round(H / 64.0)) * 64
    W = int(np.round(W / 64.0)) * 64
    img = cv2.resize(input_image, (W, H), interpolation=cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA)
    return img


def parse_request(req: request):
    content = req.get_json()
    img_id = content['img_id']

    parsed_url = urllib.parse.urlparse(img_id)
    bucket = parsed_url.netloc
    key = parsed_url.path[1:]

    obj = S3.get_object(Bucket=bucket, Key=key)
    img = Image.open(BytesIO(obj['Body'].read())).convert('RGB')
    img = ImageOps.exif_transpose(img)
    img = np.array(img)
    logging.info(f"Image loaded from {img_id} with size {img.shape}")

    img = resize_image(HWC3(img), 768)

    email = content['email']
    s3_location = content['s3_location']
    logging.info(f"Processing image of size {img.shape} for {email} and save results to {s3_location}")

    logging.info(f"Image resized to {img.shape}")
    return img, email, s3_location
    

def upload_to_s3(img, s3_location):
    sm_key = f"{s3_location}/{uuid.uuid4().hex}.jpeg"

    img = cv2.imencode('.png', img)[1].tobytes()
    S3.put_object(
        Bucket=SM_BUCKET,
        Key=sm_key,
        Body=img
    )
    logging.info(f"Uploaded image to s3://{SM_BUCKET}/{sm_key}")
    return f"s3://{SM_BUCKET}/{sm_key}"


def zip_images(s3_key_prefix):
    zip_file_name = f"{uuid.uuid4().hex}.zip"
    zip_buffer = BytesIO()

    with zipfile.ZipFile(zip_buffer, mode='w') as zip_file:
        for s3_object in S3.list_objects(Bucket=SM_BUCKET, Prefix=s3_key_prefix)['Contents']:
            if not s3_object['Key'].endswith('.jpeg'):
                continue

            file_content = S3.get_object(Bucket=SM_BUCKET, Key=s3_object['Key'])['Body'].read()
            zip_file.writestr(f"logo/{s3_object['Key'].split('/')[-1]}", file_content)

    zip_buffer.seek(0)
    with open(zip_file_name, 'wb') as f:
        f.write(zip_buffer.getvalue())

    zip_key = f"{'/'.join(s3_object['Key'].split('/')[:-1])}/{zip_file_name}"
    S3.upload_file(zip_file_name, SM_BUCKET, zip_key)
    S3.upload_file(zip_file_name, SM_BUCKET, f"zipped/{zip_file_name}")
    logging.info(f"Uploaded zip file to s3://{SM_BUCKET}/{zip_key}")
    
    return f"https://s3-eu-west-1.amazonaws.com/{SM_BUCKET}/zipped/{zip_file_name}"


def send_email_to_user(recipient, zip_file_url):

    BODY_HTML = f"""
    <html>

    <head></head>

    <body>
        <h4>Our AI artists finished crunching your logo!</h4>
        <p>
            We asked them to be as creative as possible, and the results are really cool!
            <br>
            You can download your custom logos <a href="{zip_file_url}">here</a>.
            <br>
        </p>
    </body>

    </html>
    """            

    # Try to send the email.
    try:
        #Provide the contents of the email.
        response = SES.send_email(
            Destination={
                'ToAddresses': [
                    recipient,
                ],
            },
            Message={
                'Body': {
                    'Html': {
                        'Charset': CHARSET,
                        'Data': BODY_HTML,
                    },
                    'Text': {
                        'Charset': CHARSET,
                        'Data': BODY_TEXT,
                    },
                },
                'Subject': {
                    'Charset': CHARSET,
                    'Data': SUBJECT,
                },
            },
            Source=SENDER,
        )
    except ClientError as e:
        logging.info(e.response['Error']['Message'])
    else:
        logging.info(f"Email sent! Message ID:{response['MessageId']}")

    return


app = Flask(__name__)
PREDICTOR = Predictor('./models/cldm_v15.yaml', './models/control_sd15_canny.pth')
S3 = boto3.client('s3')
SM_BUCKET = "visualneurons.com-logos-outputs"
SENDER = "fra.pochetti@gmail.com"
CHARSET = "UTF-8"
SES = boto3.client('ses', region_name="eu-west-1")
SUBJECT = "VisualNeurons.com - your personalised logos are ready!"
BODY_TEXT = ("VisualNeurons.com - your personalised logos are ready! \r\n"
             "Our AI artists finished crunching your image, and are quite pleased with the result. \r\n"
             "You can find your custom logos via the provided link."
             ""
            )


@app.route("/invocations", methods=["POST"])
def predict():
    input_image, recipient, s3_location = parse_request(request)
    logging.info("Image loaded. Processing...")
    out_gen = PREDICTOR.gen(img=input_image, num_samples=1)

    for res in out_gen:
        for img in res:
            upload_to_s3(img, s3_location)

    zip_file_name = zip_images(s3_location)
    logging.info(f"Zipped images to {zip_file_name}")
    
    send_email_to_user(recipient, zip_file_name)

    del out_gen, res, img, input_image
    torch.cuda.empty_cache()
    gc.collect()

    return Response(response="Done", status=200)


@app.route("/ping")
def ping(): return Response(response="OK", status=200)


app.run(host="0.0.0.0", port=8080)