Feat/GitHub action

.github/workflows/test.yml

@@ -0,0 +1,34 @@
+# .github/workflows/app.yaml
+name: Tests
+on:
+  workflow_dispatch:
+  pull_request:
+  push:
+    branches:
+      - master
+      - staging
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    timeout-minutes: 5
+
+    steps:
+      - name: Check out repository code
+        uses: actions/checkout@v3
+
+      - name: Setup Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.8"
+
+      - name: Install dependencies
+        run: |
+          sudo apt-get update
+          sudo apt-get install ffmpeg -y
+          pip install -r requirements.txt
+      - name: Set Env variables
+        run: "echo TEST_SCOPE=CICD-OPENSOURCE >> $GITHUB_ENV"
+      - name: Run Test suite
+        run: |
+          pytest -vvv -s -n auto --maxprocesses=8

README.md

@@ -1,29 +1,33 @@
-# EdenAI - Providers Connectors
+[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) 	[![Website](https://img.shields.io/website-up-down-green-red/http/monip.org.svg)](https://www.edenai.co/) 
 
-Eden AI aims to simplify the use and deployment of AI technologies by providing a unique API (application programming interface) that connects to the best possible AI engines. These engines are either proprietary or Open source AI engines, and can be used for different purposes, e.g, face
-detection, OCR (receipt, invoice, table...), keyword extraction, sentiment analysis, face detection, and much more. These technologies are provided by the best suppliers in the market. We can cite briefly some of them: Amazon, Google, Microsoft, Dataleon, and Mindee and many others. Eden AI take care of providing to it’s clients the best AI engine suited to their projects, and this, with the goal of keeping AI light and easy to any developer. 
+[![License](https://img.shields.io/badge/Discord-7289DA?style=for-the-badge&logo=discord&logoColor=white)](https://discord.com/invite/VYwTbMQc8u) ![Python](https://img.shields.io/badge/Python-3776AB?style=for-the-badge&logo=python&logoColor=white) [![Linkedin](https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/company/edenai/)
+
+![Eden AI Logo](EdenAI-WrittenLogo(1).png)
 
-[Join us on discord !](https://discord.gg/baX77uwa)
+# EdenAI - AI APIs
 
-![EdenAI_logo](https://media-exp1.licdn.com/dms/image/D4E3DAQELmESPpY1T7w/image-scale_191_1128/0/1664541418002?e=1666281600&v=beta&t=VE98W7VHQGF-wYGFiv4erS-V74R6l4UMrA7aGjYGeYw)
+[Eden AI](https://www.edenai.co/) aims to simplify the use and deployment of AI technologies by providing a unique API (application programming interface) that connects to the best possible AI engines. These engines are either proprietary or Open source AI engines, and can be used for different purposes, e.g, face
+detection, OCR (receipt, invoice, table...), keyword extraction, sentiment analysis, face detection, and much more. These technologies are provided by the best suppliers in the market. We can cite briefly some of them: Amazon, Google, Microsoft, Dataleon, and Mindee and many others. Eden AI take care of providing to it’s clients the best AI engine suited to their projects, and this, with the goal of keeping AI light and easy to any developer. 
+
+![EdenAI Gif](ezgif.com-gif-maker(1).gif)
 
 ## Introduction to the package
 
 Eden AI's Providers Connectors is the open source package including necessary methods for using AI technologies from different [providers](#provider). The package contains principally `five packages` and `one interface module`:
 
 ### **interface**
 
-The module interface is responsable of linking providers subfeature methods to our back-end project (which is, on the other hand, private). It containts principally five functions that you can use to interact with providers' subfeatures. We list the methods hereafter:
+The module interface is responsible of linking providers subfeature methods to our back-end project (which is, on the other hand, private). It contains principally five functions that you can use to interact with providers' subfeatures. We list the methods hereafter:
 
 * #### list_features
 
-  Returns possible combinations of  (`provider`, `feature`, `subfeature`) or (`provider`, `feature`, `subfeature`, `phase`) given a provider, feature and subfeature as a list if **as_dict** is set to False, otherwise returns the resutls as a dictionary. If neither the provider, feature or subfeature are passed withing the function arguments, it returns the list of all possible combinations.
+  Returns possible combinations of  (`provider`, `feature`, `subfeature`) or (`provider`, `feature`, `subfeature`, `phase`) given a provider, feature and subfeature as a list if **as_dict** is set to False, otherwise returns the results as a dictionary. If neither the provider, feature or subfeature are passed withing the function arguments, it returns the list of all possible combinations.
 
   ```python
     def list_features( provider_name: str = None, feature: str = None, subfeature: str = None, as_dict: bool = False) -> Union[List, Dict]:
   ```
 
-  *E.g of a dict results:*:
+  *E.g. of a dict results:*
 
   ```python
       {
@@ -61,7 +65,7 @@ The module interface is responsable of linking providers subfeature methods to o
 
 * ### get_async_job_result
 
-  When the computed subfeature using `compute_output` is **asyncronous**, a *`public_job_id`* is returned. Passing this *`public_job_id`* along a given provider, feature, subfeature and phase as arguments for the `get_async_job_result` function returns the result of the asyncronous call.
+  When the computed subfeature using `compute_output` is **asynchronous**, a *`public_job_id`* is returned. Passing this *`public_job_id`* along a given provider, feature, subfeature and phase as arguments for the `get_async_job_result` function returns the result of the asyncronous call.
 
   ```python
     def get_async_job_result(provider_name: str, feature: str, subfeature: str, async_job_id: str,
@@ -82,25 +86,25 @@ In the EdenAI organization, we follow a naming patterns based on `four` principl
 
 ### **Provider**
 
-`Provider` represents names of the suppliers from which we integrate our AI technologies. *E.g,: Amazon, Google, Microsoft, Dateleon and Mindee* and many others. To this time, *Eden AI* have integrated in it's platform more than 25 **providers**.
+`Provider` represents names of the suppliers from which we integrate our AI technologies. *E.g.: Amazon, Google, Microsoft, Dateleon and Mindee* and many others. To this time, *Eden AI* has integrated in it's platform more than 25 **providers**.
 
 ### **Subfeature**
 
 `Subfeauture` represents the naming schema for the integrated `AI Technologies`. *E.g,: automatic_translation, text_to_speech, face_detection, invoice_parser or keyword_extraction*.
 
 ### **Feature**
 
-To better apprehend the use of each of the `subfeatures` available in our platform, we grouped related subfeatures into categories that we named `features`. In other words, each feature encompasses a list of subfeatures. *E,g.:* the feature **audio** contains the two subfeatures: *speech_to_text_*[async](#async-logic) and *text_to_speech*.
+To better apprehend the use of each of the `subfeatures` available in our platform, we grouped related subfeatures into categories that we named `features`. In other words, each feature encompasses a list of subfeatures. *E.g.:* the feature **audio** contains the two subfeatures: *speech_to_text_*[async](#async-logic) and *text_to_speech*.
 
 ### **Phase**
 
-For some subfeatures, the computation may encompass different stages to execute, usually sequentially, but not always. These stages are named `phases`. *E.g,* the subfeature `seach` inside the feature `image` encompass four **phases**: *upload_image*, *get_image*, *get_images*, *launch_similarity*, *delete_image*.
+For some subfeatures, the computation may encompass different stages to execute, usually sequentially, but not always. These stages are named `phases`. *E.g.* the subfeature `seach` inside the feature `image` encompass four **phases**: *upload_image*, *get_image*, *get_images*, *launch_similarity*, *delete_image*.
 
 ### **Async logic**
 
-Some subfeatures can be time consuming, like converting *speech* to *text*, and so, in a logic cycle of (request/response), the computing result can not be sent directly to the user. In this context, we propose an `Asynchrone logic`, in which the final result is differed, but instead, users can check reapedetly for the call result.
+Some subfeatures can be time consuming, like converting *speech* to *text*, and so, in a logic cycle of (request/response), the computing result can not be sent directly to the user. In this context, we propose an `Asynchrone logic`, in which the final result is differed, but instead, users can check repeatedly for the call result.
 
-In order the support this implementation logic, subfeatures that are asynchrone ends with **_async** and are splitted into two methods call with two possible [`suffix`](#suffix): **__launch_job** and **__get_job_result**. *E.g,:*
+In order to support this implementation logic, subfeatures that are asynchronous end with **_async** and are splitted into two methods called with two possible [`suffix`](#suffix): **__launch_job** and **__get_job_result**. *E.g.:*
 
 ```python
   def audio__speech_to_text_async__launch_job(self, file: BufferedReader, language: str) -> AsyncLaunchJobResponseType:
@@ -112,15 +116,26 @@ In order the support this implementation logic, subfeatures that are asynchrone
 
 ### **Suffix**
 
-Used suffixes are usually either **__launch_job** or **__get_job_result**. The **__launch_job** suffix refers to the subfeature method responsable for starting the the asynchronous call. The **__get_job_result** refers to the subfeature method for handling the result of the asynchronous call.
+Used suffixes are usually either **__launch_job** or **__get_job_result**. The **__launch_job** suffix refers to the subfeature method responsible for starting the the asynchronous call. The **__get_job_result** refers to the subfeature method for handling the result of the asynchronous call.
 
-### Standarized response
+### Standardized response
 
-Responses to the same `subfeature` even if called from different providers are all `standarized` to a uniform response.
+Responses to the same `subfeature` even if called from different providers are all `standardized` to a uniform response.
 
 ## Contribute
 
 You are more than welcome to contribute to our project, just `Fork the project`, `push` your modifications into a `branch` and open a `Pull Request`. We will be happy to check and eventually merge your **contributions** into our project.
 
+## Don’t want to create accounts for all providers and host the project?
+You can create an account on [Eden AI](https://app.edenai.run/user/register) and have access to all the AI technologies and providers directly through our API.
+[![Eden AI Logo](EdenAI-WrittenLogo(1).png)](https://app.edenai.run/user/register)
+
+
+## Join the community!
+Join our friendly community to improve your skills, focus on the integration of AI engines, get help to use Eden AI API and much more !
+
+[![](https://dcbadge.vercel.app/api/server/VYwTbMQc8u)](https://discord.com/invite/VYwTbMQc8u)
+
+
 ## License :
 [Apache License 2.0](LICENSE)