From 067056629b0ffd7efab5e02b51bb05dcb484dafe Mon Sep 17 00:00:00 2001 From: taphix Date: Fri, 19 Jul 2024 23:10:58 +0300 Subject: [PATCH] first commit --- Dockerfile | 11 ++++ Readme.md | 1 + api/__init__.py | 4 ++ api/__main__.py | 37 +++++++++++ api/functions.py | 20 ++++++ docker-compose.yml | 45 ++++++++++++++ nginx/nginx.conf | 54 ++++++++++++++++ nsfw_detector/__init__.py | 1 + nsfw_detector/nsfw_model.h5 | Bin 0 -> 17721560 bytes nsfw_detector/predict.py | 119 ++++++++++++++++++++++++++++++++++++ requirements.txt | 7 +++ 11 files changed, 299 insertions(+) create mode 100644 Dockerfile create mode 100644 Readme.md create mode 100644 api/__init__.py create mode 100644 api/__main__.py create mode 100644 api/functions.py create mode 100644 docker-compose.yml create mode 100644 nginx/nginx.conf create mode 100644 nsfw_detector/__init__.py create mode 100644 nsfw_detector/nsfw_model.h5 create mode 100644 nsfw_detector/predict.py create mode 100644 requirements.txt diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..c8f7e31 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,11 @@ +FROM python:3.9 + +WORKDIR /app + +COPY requirements.txt . + +RUN pip install -U pip && pip install -r requirements.txt + +COPY . . + +CMD python -m api diff --git a/Readme.md b/Readme.md new file mode 100644 index 0000000..f11a300 --- /dev/null +++ b/Readme.md @@ -0,0 +1 @@ +# TEST API \ No newline at end of file diff --git a/api/__init__.py b/api/__init__.py new file mode 100644 index 0000000..892098d --- /dev/null +++ b/api/__init__.py @@ -0,0 +1,4 @@ +from fastapi import FastAPI +from nsfw_detector import predict + +app = FastAPI() diff --git a/api/__main__.py b/api/__main__.py new file mode 100644 index 0000000..cf66750 --- /dev/null +++ b/api/__main__.py @@ -0,0 +1,37 @@ +from api import predict, app +from api.functions import download_image +import os +import uvicorn + +model = predict.load_model('nsfw_detector/nsfw_model.h5') + + +@app.get("/") +async def detect_nsfw(url: str): + if not url: + return {"ERROR": "URL PARAMETER EMPTY"} + image = await download_image(url) + if not image: + return {"ERROR": "IMAGE SIZE TOO LARGE OR INCORRECT URL"} + results = predict.classify(model, image) + os.remove(image) + hentai = results['data']['hentai'] + sexy = results['data']['sexy'] + porn = results['data']['porn'] + drawings = results['data']['drawings'] + neutral = results['data']['neutral'] + if neutral >= 25: + results['data']['is_nsfw'] = False + return results + elif (sexy + porn + hentai) >= 70: + results['data']['is_nsfw'] = True + return results + elif drawings >= 40: + results['data']['is_nsfw'] = False + return results + else: + results['data']['is_nsfw'] = False + return results + +if __name__ == "__main__": + uvicorn.run("api:app", host="0.0.0.0", port=8000, log_level="info") diff --git a/api/functions.py b/api/functions.py new file mode 100644 index 0000000..81eefd4 --- /dev/null +++ b/api/functions.py @@ -0,0 +1,20 @@ +from random import randint +import aiohttp +import aiofiles + +MAX_IMAGE_SIZE = 5 * 1000000 + + +async def download_image(url): + file_name = f"{randint(6969, 6999)}.jpg" + async with aiohttp.ClientSession() as session: + async with session.get(url) as resp: + if resp.status == 200: + if int(resp.headers['Content-Length']) > MAX_IMAGE_SIZE: + return False + f = await aiofiles.open(file_name, mode='wb') + await f.write(await resp.read()) + await f.close() + else: + return False + return file_name diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 0000000..dcd9f84 --- /dev/null +++ b/docker-compose.yml @@ -0,0 +1,45 @@ +version: '3' +services: + + api: + build: . + ports: + - '8000:8000' + + nginx: + image: nginx + container_name: my-nginx + restart: always + volumes: + - ./nginx/nginx.conf:/etc/nginx/nginx.conf + - ./nginx/robots.txt:/var/www/html/robots.txt + - etc-letsencrypt:/etc/letsencrypt + - www-html:/var/www/html + ports: + - "80:80" + - "443:443" + networks: + - api_default + + certbot: + image: certbot/certbot + container_name: certbot + volumes: + - etc-letsencrypt:/etc/letsencrypt + - www-html:/var/www/html + depends_on: + - nginx + command: certonly --webroot -w /var/www/html --email me@abakuka.ru -d nsfw.wizardstech.ru --cert-name=nsfw.wizardstech.ru --key-type rsa --agree-tos + ports: + - "6000:80" + networks: + - api_default + +volumes: + some_volume: + www-html: + etc-letsencrypt: + +networks: + api_default: + external: true \ No newline at end of file diff --git a/nginx/nginx.conf b/nginx/nginx.conf new file mode 100644 index 0000000..1b0663b --- /dev/null +++ b/nginx/nginx.conf @@ -0,0 +1,54 @@ +user www-data; +worker_processes auto; +pid /run/nginx.pid; +include /etc/nginx/modules-enabled/*.conf; + +events { + worker_connections 1024; +} + +http { + + sendfile on; + tcp_nopush on; + types_hash_max_size 2048; + include /etc/nginx/mime.types; + default_type application/octet-stream; + + server { + listen 80; + server_name nsfw.wizardstech.ru; + location /.well-known/acme-challenge { + allow all; + root /var/www/html; + } + location / { + return 301 https://$host$request_uri; + } + + } + server { + listen 443 ssl; + server_name nsfw.wizardstech.ru; + + root /var/www/html; + + ssl_certificate /etc/letsencrypt/live/certfolder/fullchain.pem; + ssl_certificate_key /etc/letsencrypt/live/certfolder/privkey.pem; + + location / { + proxy_set_header Host $http_host; + proxy_set_header X-Real-IP $remote_addr; + proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; + proxy_set_header X-Forwarded-Proto $scheme; + proxy_pass http://api:8000; + } + location /.well-known/acme-challenge { + allow all; + root /var/www/html; + } + location /robots.txt { + root /var/www/html; + } + } +} diff --git a/nsfw_detector/__init__.py b/nsfw_detector/__init__.py new file mode 100644 index 0000000..a6131c1 --- /dev/null +++ b/nsfw_detector/__init__.py @@ -0,0 +1 @@ +# init diff --git a/nsfw_detector/nsfw_model.h5 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import isfile, join, exists, isdir, abspath + +import numpy as np +import tensorflow as tf +from tensorflow import keras +import tensorflow_hub as hub + + +IMAGE_DIM = 224 # required/default image dimensionality + + +def load_images(image_paths, image_size, verbose=True): + ''' + Function for loading images into numpy arrays for passing to model.predict + inputs: + image_paths: list of image paths to load + image_size: size into which images should be resized + verbose: show all of the image path and sizes loaded + + outputs: + loaded_images: loaded images on which keras model can run predictions + loaded_image_indexes: paths of images which the function is able to process + + ''' + loaded_images = [] + loaded_image_paths = [] + + if isdir(image_paths): + parent = abspath(image_paths) + image_paths = [join(parent, f) for f in listdir( + image_paths) if isfile(join(parent, f))] + elif isfile(image_paths): + image_paths = [image_paths] + + for img_path in image_paths: + try: + if verbose: + print(img_path, "size:", image_size) + image = keras.preprocessing.image.load_img( + img_path, target_size=image_size) + image = keras.preprocessing.image.img_to_array(image) + image /= 255 + loaded_images.append(image) + loaded_image_paths.append(img_path) + except Exception as ex: + print("Image Load Failure: ", img_path, ex) + + return np.asarray(loaded_images), loaded_image_paths + + +def load_model(model_path): + if model_path is None or not exists(model_path): + raise ValueError( + "saved_model_path must be the valid directory of a saved model to load.") + + model = tf.keras.models.load_model(model_path, custom_objects={ + 'KerasLayer': hub.KerasLayer}) + return model + + +def classify(model, input_paths, image_dim=IMAGE_DIM): + """ Classify given a model, input paths (could be single string), and image dimensionality....""" + images, image_paths = load_images(input_paths, (image_dim, image_dim)) + probs = classify_nd(model, images) + return dict(zip(['data'], probs)) + + +def classify_nd(model, nd_images): + """ Classify given a model, image array (numpy)....""" + + model_preds = model.predict(nd_images) + # preds = np.argsort(model_preds, axis = 1).tolist() + + categories = ['drawings', 'hentai', 'neutral', 'porn', 'sexy'] + + probs = [] + for i, single_preds in enumerate(model_preds): + single_probs = {} + for j, pred in enumerate(single_preds): + single_probs[categories[j]] = round(float(pred), 6) * 100 + probs.append(single_probs) + return probs + + +def main(args=None): + parser = argparse.ArgumentParser( + description="""A script to perform NFSW classification of images""", + epilog=""" + Launch with default model and a test image + python nsfw_detector/predict.py --saved_model_path mobilenet_v2_140_224 --image_source test.jpg + """, formatter_class=argparse.RawTextHelpFormatter) + + submain = parser.add_argument_group( + 'main execution and evaluation functionality') + submain.add_argument('--image_source', dest='image_source', type=str, required=True, + help='A directory of images or a single image to classify') + submain.add_argument('--saved_model_path', dest='saved_model_path', type=str, required=True, + help='The model to load') + submain.add_argument('--image_dim', dest='image_dim', type=int, default=IMAGE_DIM, + help="The square dimension of the model's input shape") + if args is not None: + config = vars(parser.parse_args(args)) + else: + config = vars(parser.parse_args()) + + if config['image_source'] is None or not exists(config['image_source']): + raise ValueError( + "image_source must be a valid directory with images or a single image to classify.") + + model = load_model(config['saved_model_path']) + image_preds = classify(model, config['image_source'], config['image_dim']) + print(json.dumps(image_preds, indent=2), '\n') + + +if __name__ == "__main__": + main() diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..4ecf287 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,7 @@ +fastapi +uvicorn +aiohttp +aiofiles +tensorflow +tensorflow-hub +pillow

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