fixes #70: Refactor the README Sequence Diagram in Mermaid (#72)

* Update Sequence Diagram README.md

![SD_1 drawio (2)](https://github.com/ai-cfia/nachet-backend/assets/19809069/272f37dc-f4ec-449b-ba82-950c54b9f856)

issue #51: Fixing Markdown lint

issue #51: fixing MD lint

README.md

@@ -1,8 +1,34 @@
-# nachet-backend
+# :microscope: nachet-backend 🌱
 
 ## High level sequence diagram
 
-![SD_1 drawio (2)](https://github.com/ai-cfia/nachet-backend/assets/19809069/272f37dc-f4ec-449b-ba82-950c54b9f856)
+```mermaid
+sequenceDiagram
+
+  title: High Level Sequence Diagram 1.0.0
+  actor Client
+  participant frontend
+  participant backend
+  participant EndpointAPI
+  participant AzureStorageAPI
+
+Client->>+frontend: getDirectoriesList()
+frontend->>+backend: HTTP POST req.
+backend->>+AzureStorageAPI: get_blobs()
+AzureStorageAPI-->>-backend: blobListObject
+backend-->>frontend: directories list res.
+frontend-->>Client: display directories
+Client->>frontend: handleInference()
+frontend->>backend: HTTP POST req.
+backend->>+AzureStorageAPI: upload_image(image)
+AzureStorageAPI-->>-backend: imageBlobObject
+backend->>+EndpointAPI: get_inference_result(image)
+EndpointAPI-->>-backend: inference res.
+backend->>backend: process inf. result
+backend-->>frontend: inference res.
+frontend-->>-Client: display inference res.
+backend->>+AzureStorageAPI: (async) upload_inference_result(json)
+```
 
 ### Details
 

docs/nachet-inference-documentation.md

@@ -20,9 +20,9 @@ selected by a parameter.
 
 ### Pipelines
 
-Pipelines are defined as a set of models that follow each other, where the output of
-one model is used as input for the next models, and so on. A pipeline contains from 1 to n
-models.
+Pipelines are defined as a set of models that follow each other, where the
+output of one model is used as input for the next models, and so on. A pipeline
+contains from 1 to n models.
 
 #### Pipelines flowchart 1.0.0
 
@@ -141,18 +141,18 @@ sequenceDiagram
 
 ### Inference Request function
 
-The inference request function plays a crucial role in Nachet Interactive's backend.
-It requests actions from selected models or pipelines based on certain checks.
-These checks include verifying that all arguments required to find or initialize
-the blob container and process the image have been transmitted to the function.
-It also checks if the selected pipeline is recognized by the system and if the image sent for analysis
-has a valid header.
-
-If all the above checks pass, the function initializes or finds the user blob container
-and uploads the image. Next, it requests an inference from every model in the pipeline.
-Each model specifies their `entry_function` (how to call and retrieve data) and whether
-they have a `process_inference` function. Based on these indications, the results are returned
-and stored in the cache.
+The inference request function plays a crucial role in Nachet Interactive's
+backend. It requests actions from selected models or pipelines based on certain
+checks. These checks include verifying that all arguments required to find or
+initialize the blob container and process the image have been transmitted to the
+function. It also checks if the selected pipeline is recognized by the system
+and if the image sent for analysis has a valid header.
+
+If all the above checks pass, the function initializes or finds the user blob
+container and uploads the image. Next, it requests an inference from every model
+in the pipeline. Each model specifies their `entry_function` (how to call and
+retrieve data) and whether they have a `process_inference` function. Based on
+these indications, the results are returned and stored in the cache.
 
 If no other model is called, the last result is then processed and sent to the frontend.
 

docs/nachet-model-documentation.md

@@ -31,7 +31,12 @@ Nachet Interactive models' perform the following tasks:
 
 ### Request Inference Function
 
-The request inference functions request a prediction from the specified model (such as Swin, Nachet-6seeds, etc.). If needed, the function will process the data to be readable by the next model in the pipeline. For instance, the Seed-detector only returns "seed" as a label, and its inference needs to be processed and passed to the next model which assigns the correct label to the seeds.
+The request inference functions request a prediction from the specified model
+(such as Swin, Nachet-6seeds, etc.). If needed, the function will process the
+data to be readable by the next model in the pipeline. For instance, the
+Seed-detector only returns "seed" as a label, and its inference needs to be
+processed and passed to the next model which assigns the correct label to the
+seeds.
 
 ## Return value of models
 
@@ -82,16 +87,21 @@ The request inference functions request a prediction from the specified model (s
 
 ### Why topN
 
-We decided to named the top results property top N because this value can return n predictions. Usually in AI, the top 5 result are use to measure the accuracy of a model. If the correct result is the top 5, then it is considered that the prediction was true.
+We decided to named the top results property top N because this value can return
+n predictions. Usually in AI, the top 5 result are use to measure the accuracy
+of a model. If the correct result is the top 5, then it is considered that the
+prediction was true.
 
 This is useful in case were the user have is attention on more then 1 result.
 
- > "Top N accuracy — Top N accuracy is when you measure how often your predicted class falls in the top N values of your softmax distribution."
+ > "Top N accuracy — Top N accuracy is when you measure how often your predicted
+ > class falls in the top N values of your softmax distribution."
  [Nagda, R. (2019-11-08) *Evaluating models using the Top N accuracy metrics*. Medium](https://medium.com/nanonets/evaluating-models-using-the-top-n-accuracy-metrics-c0355b36f91b)
 
 ### Box around seed
 
-The `box` key stores the value for a specific box around a seed. This helps the frontend application build a red rectangle around every seed on the image.
+The `box` key stores the value for a specific box around a seed. This helps the
+frontend application build a red rectangle around every seed on the image.
 
 ![image](https://github.com/ai-cfia/nachet-backend/assets/96267006/469add8d-f40a-483f-b090-0ebcb7a8396b)
 
@@ -149,7 +159,9 @@ A list of common error models returns to the backend.
 ## Pipeline and model data
 
 In order to dynamically build the pipeline in the backend from the model, the
-following data structure was designed. For now, the pipelines will have two keys for their names (`model_name`, `piepline_name`) to support the frontend code until it is changed to get the name of the pipeline with the correct key.
+following data structure was designed. For now, the pipelines will have two keys
+for their names (`model_name`, `piepline_name`) to support the frontend code
+until it is changed to get the name of the pipeline with the correct key.
 
 ```yaml
 version: