fixes: docs, integration's compose files, start.py (#327)

* fix dockerhub build error by changing filenames and paths

* docs: fixes and adjusts

Author: eshaan7

docs/source/Advanced-Usage.md

@@ -73,12 +73,12 @@ table, th, td {
 
 
 To enable all the optional analyzers you can add the option `--all_analyzers` when starting the project. Example:
-```
+```bash
 python3 start.py prod --all_analyzers up
 ```
 
 Otherwise you can enable just one of the cited integration by using the related option. Example:
-```
+```bash
 python3 start.py prod --qiling up
 ```
 
@@ -136,19 +136,18 @@ List of some of the analyzers with optional configuration:
   * Every Chrome-Extension has a unique alpha=numerc identifier. That's the only Input necessary. Eg: `Norton Safe Search Enhanced`'s identifier is `eoigllimhcllmhedfbmahegmoakcdakd`.
 
 
-
 There are two ways to do this:
 
-#### from the GUI
+##### from the GUI
 You can click on "**Custom analyzer configuration**" button and add the runtime configuration in the form of a dictionary.
 Example:
-```
+```javascript
 "VirusTotal_v3_Get_File": {
     "force_active_scan_if_old": true
 }
 ```
 
-#### from [Pyintelowl](https://github.com/intelowlproject/pyintelowl)
+##### from [Pyintelowl](https://github.com/intelowlproject/pyintelowl)
 While using `send_observable_analysis_request` or `send_file_analysis_request` endpoints, you can pass the parameter `runtime_configuration` with the optional values.
 Example:
 ```python
@@ -231,17 +230,17 @@ Refer to the following blog post for an example on how to deploy IntelOwl on Goo
 [Deploying Intel-Owl on GKE](https://mostwanted002.cf/post/intel-owl-gke/) by [Mayank Malik](https://twitter.com/_mostwanted002_).
 
 ## Multi Queue
-IntelOwl provides an additional `docker-compose` file,  [multi-queue.override.yaml](https://github.com/intelowlproject/IntelOwl/blob/master/docker/multi-queue.override.yml) file, allowing IntelOwl users to better scale with the performance of their own architecture.
+IntelOwl provides an additional [multi-queue.override.yml](https://github.com/intelowlproject/IntelOwl/blob/master/docker/multi-queue.override.yml) compose file allowing IntelOwl users to better scale with the performance of their own architecture.
 
 If you want to leverage it, you should add the option `--multi-queue` when starting the project. Example:
-```
+```bash
 python3 start.py prod --multi-queue up
 ```
 
 This functionality is not enabled by default because this deployment would start 2 more containers so the resource consumption is higher. We suggest to use this option only when leveraging IntelOwl massively.
 
 #### Queue Customization 
-It is possible to define new celery workers: each requires the addition of a new container in the docker-compose file, as shown in the `multi-queue.override.yaml`. 
+It is possible to define new celery workers: each requires the addition of a new container in the docker-compose file, as shown in the `multi-queue.override.yml`. 
 
 Moreover IntelOwl requires that the name of the workers are provided in the `docker-compose` file. This is done through the environment variable `CELERY_QUEUES` inside the `uwsgi` container. Each queue must be separated using the character `,`, as shown in the [example](https://github.com/intelowlproject/IntelOwl/blob/master/docker/multi-queue.override.yml#L6).
 

docs/source/Contribute.md

@@ -13,7 +13,7 @@ Please create a new branch based on the **develop** branch that contains the mos
 `git checkout -b myfeature develop`
 
 Then we strongly suggest to configure [pre-commit](https://github.com/pre-commit/pre-commit) to force linters on every commits you perform:
-```
+```bash
 # create virtualenv to host pre-commit installation
 python3 -m venv intel_owl_test_env
 source intel_owl_test_env/bin/activate
@@ -29,11 +29,11 @@ You may want to look at a few existing examples to start to build a new one, suc
 - [peframe.py](https://github.com/intelowlproject/IntelOwl/blob/develop/api_app/script_analyzers/file_analyzers/peframe.py), if you are creating a [docker based analyzer](#integrating-a-docker-based-analyzer)
 
 After having written the new python module, you have to remember to:
-* Put the module in the `file_analyzers` or `observable_analyzers` directory based on what it can analyze
-* Add a new entry in the [analyzer configuration](https://github.com/intelowlproject/IntelOwl/blob/master/configuration/analyzer_config.json) following alphabetical order:
+1. Put the module in the `file_analyzers` or `observable_analyzers` directory based on what it can analyze
+2. Add a new entry in the [analyzer configuration](https://github.com/intelowlproject/IntelOwl/blob/master/configuration/analyzer_config.json) following alphabetical order:
 
   Example:
-  ```
+  ```javascript
   "Analyzer_Name": {
       "type": "file",
       "external_service": true,
@@ -64,31 +64,28 @@ After having written the new python module, you have to remember to:
   Please see [Analyzers customization section](https://intelowl.readthedocs.io/en/latest/Usage.html#analyzers-customization) to get the explanation of the other available keys.
 
 
-* Add required unit tests in the [tests](https://github.com/intelowlproject/IntelOwl/blob/master/tests) folder.
- 
-  Then follow the [Test](./Tests.md) guide to start testing.
+3. Add required unit tests in the [tests](https://github.com/intelowlproject/IntelOwl/blob/master/tests) folder. Then follow the [Test](./Tests.md) guide to start testing.
 
-* Add the new analyzer/s in the lists in the docs: [Usage](./Usage.md). Also, if the analyzer provides additional optional configuration, add the available options here: [Advanced-Usage](./Advanced-Usage.md)
+4. Add the new analyzer in the lists in the docs: [Usage](./Usage.md). Also, if the analyzer provides additional optional configuration, add the available options here: [Advanced-Usage](./Advanced-Usage.md)
 
-* Ultimately, add the required secrets in the files [env_file_app_template](https://github.com/intelowlproject/IntelOwl/blob/master/env_file_app_template), [env_file_app_ci](https://github.com/certego/IntelOwl/blob/master/env_file_app_travis) and in the docs: [Installation](./Installation.md)
+5. Ultimately, add the required secrets in the files `docker/env_file_app_template`, `docker/env_file_app_ci` and in the `docs/Installation.md`.
 
-* In the Pull Request remember to provide some real world examples (screenshots and raw JSON results) of some successful executions of the analyzer to let us understand how it would work.
+5. In the Pull Request remember to provide some real world examples (screenshots and raw JSON results) of some successful executions of the analyzer to let us understand how it would work.
 
 ### Integrating a docker based analyzer
 If the analyzer you wish to integrate doesn't exist as a callable API online or python package, it should be integrated with its own docker image
 which can be queried from the main Django API.
 
 * It should follow the same design principle as the [Box-Js integration](https://github.com/intelowlproject/IntelOwl/tree/develop/integrations), unless there's very good reason not to.
 * The dockerfile should be placed at `./integrations/<analyzer_name>/Dockerfile`.
-* A docker-compose file should be placed under `./integrations` with the name `docker-compose.<analyzer_name>.yml`
-* If your docker-image uses any environment variables, add them in the [`env_file_integrations_template`](https://github.com/intelowlproject/IntelOwl/blob/develop/env_file_integrations_template)
-* Ultimately, append the name of your docker-compose file in the `COMPOSE_FILE` variables specified in [`.env`](https://github.com/intelowlproject/IntelOwl/blob/develop/.env). The reason for doing this is so that this service remains optional to the end-user.
+* Two docker-compose files `compose.yml` for production and `compose-tests.yml` for testing should be placed under `./integrations/<analyzer_name>`.
+* If your docker-image uses any environment variables, add them in the `docker/env_file_integrations_template`.
 * Rest of the steps remain same as given under "How to add a new analyzer".
 
 ## Create a pull request
 
 ### Install testing requirements
-1. Run `pip install -r test-requirements.txt` to install the requirements to  validate your code. 
+Run `pip install -r test-requirements.txt` to install the requirements to  validate your code. 
 
 #### Pass linting and tests
 1. Run `psf/black` to lint the files automatically and then `flake8` to check:
@@ -140,7 +137,7 @@ Please create pull requests only for the branch **develop**. That code will be p
 Also remember to pull the most recent changes available in the **develop** branch before submitting your PR. If your PR has merge conflicts caused by this behavior, it won't be accepted.
 
 ### Example: add an analyzer configuration for your own Yara signatures
-```
+```json
     "Yara_Scan_Custom_Signatures": {
         "type": "file",
         "python_module": "yara.Yara",

docs/source/Installation.md

@@ -157,7 +157,7 @@ There are 3 options to execute the web server:
 
     Before using it, you should configure the configuration file `docker/traefik.override.yml` by changing the email address and the hostname where the application is served. For a detailed explanation follow the official documentation: [Traefix doc](https://docs.traefik.io/user-guides/docker-compose/acme-http/).
 
-    After the configuration is done, you can add the option `--traefik` while executing the `start.py` [script](https://intelowl.readthedocs.io/en/stable/Advanced-Usage.html#smart-start).
+    After the configuration is done, you can add the option `--traefik` while executing the [`start.py`](#run)
 
 
 ### Analyzers configuration (optional)