ADD pipeline training and inference UI

tools/pipeline/Dockerfile

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+FROM python:3.8-slim
+WORKDIR /anomaly
+
+RUN apt-get update
+
+COPY requirements.txt requirements.txt
+RUN pip install -r requirements.txt
+
+RUN apt-get update
+RUN apt-get install ffmpeg libsm6 libxext6  -y
+
+COPY . /anomaly
+
+EXPOSE 5000
+CMD ["python", "./app.py"]

tools/pipeline/README.md

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+# Anomaly Detection in Industry Manufacturing
+
+## Introduction
+
+Anomalib is a deep learning library that aims to collect state-of-the-art anomaly detection algorithms for benchmarking on both public and private datasets. Anomalib provides several ready-to-use implementations of anomaly detection algorithms described in the recent literature, as well as a set of tools that facilitate the development and implementation of custom models. The library has a strong focus on image-based anomaly detection, where the goal of the algorithm is to identify anomalous images, or anomalous pixel regions within images in a dataset. Anomalib is constantly updated with new algorithms and training/inference extensions, so keep checking!
+
+## Dataset
+THE MVTEC ANOMALY DETECTION DATASET (MVTEC AD)
+
+MVTec AD is a dataset for benchmarking anomaly detection methods with a focus on industrial inspection. It contains over 5000 high-resolution images divided into fifteen different object and texture categories. Each category comprises a set of defect-free training images and a test set of images with various kinds of defects as well as images without defects.
+
+<img src='static/mvtec_dataset.jpg'>
+Link dataset: <u>https://www.mvtec.com/company/research/datasets/mvtec-ad</u>
+
+## Install packages
+```
+conda create -n anomaly-detection python=3.8
+conda activate anomaly-detection
+pip install -r requirements.txt
+```
+## Flow model
+<img src='static/flow-app.jpg'>
+
+## Custom dataset
+For each new dataset, the data consist of three folders:
+- train, which contains the (defect-free) training images
+- test, which contains the test images
+- ground_truth, which contains the pixel-precise annotations of anomalous regions
+<img src='static/data_structure.jpg'>
+
+Data custom sample: <a
+            href="https://drive.google.com/drive/folders/1mUhEG-eHZetGabc9tC8YhvI8KIAu7AAn?usp=share_link">Link
+            Drive</a>
+## Train
+```
+python train.py --config "configs/patchcore.yaml" --model "patchcore"
+```
+
+## Evaluation (Coming soon)
+## Inference
+```
+python script_inference.py --config "configs/patchcore_resnet18_aqa.yaml" --weight "models/patchcore_resnet18_aqa.ckpt" --image "static/aqa.png"
+```
+or just simple:
+```
+python script_inference.py
+```
+## App Flask
+```
+python app.py
+```
+Open local URL: http://127.0.0.1:5000
+
+Default account login:
+- Username: anomalib
+- Password: anomalib
+
+Login:
+<img src='static/login_app.jpg'>
+
+Signup:
+<img src='static/signup_app.jpg'>
+
+Homepage:
+<img src='static/home_app.jpg'>
+
+Train:
+<img src='static/train_app.jpg'>
+
+Inference:
+<img src='static/inference_app.jpg'>
+
+Database:
+<img src='static/database_app.jpg'>
+
+## Container
+```
+docker build -t anomaly:v1 .
+docker run anomaly:v1
+```
+
+or just simple
+
+```
+docker-compose up
+```
+## Deploy AWS
+First: Create EC2 instance 
+
+Second: git clone and install related packages
+```
+git clone https://github.com/vnk8071/anomaly-detection-in-industry-manufacturing.git
+```
+Next: install Miniconda and Docker engine
+
+- Miniconda: Follow link https://varhowto.com/install-miniconda-ubuntu-18-04/
+- Docker engine: Follow link https://docs.docker.com/engine/install/ubuntu/
+
+```
+docker-compose up
+```
+
+Final: access link http://user-IPv4-public-ec2-aws

tools/pipeline/__init__.py

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+import os
+
+from flask import Flask
+from flask_login import LoginManager
+
+from database import db
+from models import User
+
+def create_app():
+    """
+    Init app with config and create database
+    Return app <object>
+    """
+    app = Flask(__name__, instance_relative_config=False)
+    basedir = os.path.abspath(os.path.dirname(__file__))
+    app.config.from_pyfile("config.py", silent=True)
+    app.config['SQLALCHEMY_DATABASE_URI'] =\
+        'sqlite:///' + os.path.join(basedir, 'static', 'uploads', 'AD.sqlite3')
+    db.init_app(app)
+    # Login manager
+    login_manager = LoginManager()
+    login_manager.login_view = 'auth.login'
+    login_manager.init_app(app)
+    @login_manager.user_loader
+    def load_user(user_id):
+        """
+        Return current user login
+        """
+        return User.query.get(int(user_id))
+    return app
+