steps.txt

1. Django Project Creation

- created djangoenv environment in anaconda.
- activated djangoenv using ------->  conda activate djangoenv
- installed django in djangoenv environment using -------->  conda install -c anaconda django


- created a new django project using ------->  django-admin startproject ObjectIdentificationApp
- then cd ObjectIdentificationApp
- run the django server using -------> python manage.py runserver


- created new app inside the project using ------> python manage.py startapp imgUpload and 
mentioned it in settings.py inside installed_apps
- created a folder named templates that will have all the html files and
mentioned it in settings.py inside templates -> dirs


- inside the urls.py added the path for imageupload and redirected it to imageUpload.urls
- created a new file in imageUpload folder named urls.py and added path for home
- inside the views.py of the imgUpload app created a home function that renders to the home.html page


2. Backend creation using Pretrained keras model - Resnet50

- Tested the Resnet50 imagenet using jupyter notebook and tested it by giving the pug image for prediction


3. Adding form to django app and uploading image

- created a form in home.html that take a image file as input
and created path for imageprocess 
- created a imageprocess view redirecting to result.html and 
also created a handle_uploaded_file function that will store/save the uploaded image

- created a forms.py inside the imageUpload folder and created a ImageUploadForm class
- then we created the object of that class in views.py and checked it validity


4. Integrating web application with deep learning backend

- we trained the model again inside the views.py just as we did earlier in step2
- then we displayed the result on our result.html page
- after that added some bootstrap for styling
- created the requirements.txt file using ------>  pip list --format=freeze > requirements.txt