fixes #51: Update sequence diagram to reflect change in code

fixes #51: Update sequence diagram

fixes #51: Update doc string

fixes #51: update README and TESTING

README.md

@@ -1,6 +1,7 @@
 # nachet-backend
 
 ## High level sequence diagram
+
 ![SD_1 drawio (2)](https://github.com/ai-cfia/nachet-backend/assets/19809069/272f37dc-f4ec-449b-ba82-950c54b9f856)
 
 ### Details
@@ -12,41 +13,57 @@
 - Inference results from model endpoint are directly handled in `model_inference/inference.py`
 
 ****
- 
+
 ### RUNNING NACHET-BACKEND FROM DEVCONTAINER
+
 When you are developping, you can run the program while in the devcontainer by
 using this command:
+
 ```bash
 hypercorn -b :8080 app:app
 ```
 
 ### RUNNING NACHET-BACKEND AS A DOCKER CONTAINER
-If you want to run the program as a Docker container (e.g., for production), use: 
+
+If you want to run the program as a Docker container (e.g., for production), use:
+
 ```bash
 docker build -t nachet-backend .
 docker run -p 8080:8080 -v $(pwd):/app nachet-backend
 ```
 
 ### TESTING NACHET-BACKEND
-To test the program, use this command: 
+
+To test the program, use this command:
+
 ```bash
 python -m unittest discover -s tests
 ```
 
 ****
+
 ### ENVIRONMENT VARIABLES
+
 Start by making a copy of `.env.template` and renaming it `.env`. For the
 backend to function, you will need to add the missing values:
 
-* **NACHET_AZURE_STORAGE_CONNECTION_STRING**: Connection string to access
+- **NACHET_AZURE_STORAGE_CONNECTION_STRING**: Connection string to access
   external storage (Azure Blob Storage).
-* **NACHET_MODEL_ENDPOINT_REST_URL**: Endpoint to communicate with deployed
-  model for inferencing. 
-* **NACHET_MODEL_ENDPOINT_ACCESS_KEY**: Key used when consuming online endpoint.
-* **NACHET_DATA**: Url to access nachet-data repository
-* **NACHET_HEALTH_MESSAGE**: Health check message for the server.
+- **NACHET_MODEL_ENDPOINT_REST_URL**: Endpoint to communicate with deployed
+  model for inferencing.
+- **NACHET_MODEL_ENDPOINT_ACCESS_KEY**: Key used when consuming online endpoint.
+- **NACHET_DATA**: Url to access nachet-data repository
+- **NACHET_SUBSCRIPTION_ID**
+- **NACHET_RESOURCE_GROUP**
+- **NACHET_WORKSPACE**
+- **NACHET_MODEL**
+- **NACHET_BLOB_PIPELINE_NAME**
+- **NACHET_BLOB_PIPELINE_VERSION**
+- **NACHET_BLOB_PIPELINE_DECRYPTION_KEY**
 
 ****
-### DEPLOYING NACHET 
+
+### DEPLOYING NACHET
+
 If you need help deploying Nachet for your own needs, please contact us at
-[email protected].
+<[email protected]>.

TESTING.md

@@ -1,6 +1,12 @@
 # Testing documentation
 
-To test the backend, you can either use the automatic test in run_test.py or
+To start the automatic test, you can use the following command:
+
+```bash
+python -m unittest discover -s tests
+```
+
+You also have the option to run automatic test in run_test.py or
 manually test the functionality with the frontend. [See frontend testing
 documentation](https://github.com/ai-cfia/nachet-frontend/blob/main/TESTING.md)
 

docs/nachet-inference-documentation.md

@@ -54,7 +54,7 @@ to a model and receive the result.
 
 *Suggestion: we could call the pipeline a method if we don't want to mix terms.*
 
-# Sequence Diagram for inference request 1.1.0
+# Sequence Diagram for inference request 1.2.1
 
 ```mermaid
 sequenceDiagram
@@ -65,72 +65,73 @@ sequenceDiagram
     participant Blob storage
     participant Model
 
+    Backend-)+Backend: run()
     Note over Backend,Blob storage: initialisation
     Backend-)Backend: before_serving()
-    Backend-)Backend: get_pipelines_models()
+    Backend-)Backend: get_pipelines()
     alt
-    Backend-)Blob storage: HTTP POST req.
-    Blob storage--)Backend: return pipelines_models.json
+    Backend-)+Blob storage: HTTP POST req.
+    Blob storage--)-Backend: return pipelines_models.json
     else
-    Backend-)Frontend: error 400 No pipeline found
+    Backend-)Frontend: error 500 Failed to retrieve data from the repository
     end
     Note over Backend,Blob storage: end of initialisation
    
-    Client->>Frontend: applicationStart()
+    Client->>+Frontend: applicationStart()
     Frontend-)Backend: HTTP POST req.
-    Backend-)Backend: get_pipelines_names()
+    Backend-)Backend: get_model_endpoints_metadata()
     Backend--)Frontend: Pipelines names res.
     Note left of Backend: return pipelines names and metadata
 
     Frontend->>Client: application is ready
     Client-->>Frontend: client ask action from specific pipeline
     Frontend-)Backend: HTTP POST req.
     Backend-)Backend: inference_request(pipeline_name, folder_name, container_name, imageDims, image)
-    alt missing argument and image and pipeline validation
-        Backend--)Frontend: Error 400 missing arguments
-        Backend--)Frontend: Error 400 Model not found
-        Backend--)Frontend: Error 400 Invalid image header
-    else no missing argument and validation pass
-        Backend-)Backend: mount_container(connection_string(Environnement Variable, container_name))
-        Backend-)Blob storage: HTTP POST req.
-        Blob storage--)Backend: container_client
-        
-        Backend-)Backend: upload_image(container_client, folder_name, image_bytes, hash_value)
-        Backend-)Blob storage: HTTP POST req.
-        Blob storage--)Backend: blob_name
-
-        Backend-)Backend: get_blob(container_client, blob_name)
-        Backend-)Blob storage: HTTP POST req.
-        Blob storage--)Backend: blob
-
-        loop for every model in pipeline
-            Backend-)Backend: model.entry_function(model, previous_result)
-            note over Backend, Blob storage: Every model has is own entry_function
-            Backend-)Backend: request_factory(previous_result, model)
-            Backend-)Backend: urllib.request.Request(endpoint_url, body, header)
-            Backend-)Model: HTTP POST req.
-            Model--)Backend: Result res.
-            alt if model has process_inference_function
-                Backend-) Backend: model.inference_function(previous_result, result_json)
-            end
-            alt next model is not None
-                note over Backend, Blob storage: restart the loop process
-                Backend-)Backend: record_result(model, result)
-                Backend-)Blob storage: HTTP POST req.
-                note over Backend, Blob storage: record the result produced by the model
-
-            end
-        end
-        
-        par Backend to Frontend
-            Backend-)Backend: inference.process_inference_results(data, imageDims)
-            Backend--)Frontend: Processed result res.
-        and Backend to Blob storage
-            Backend-)Backend: upload_inference_result(container_client, folder_name, result_json_string, hash_value)
-            Backend-)Blob storage: HTTP POST req.
+    alt missing arguments
+    Backend-)Frontend: Error 400 missing arguments
+    end
+    alt wrong pipeline name
+    Backend-)Frontend: Error 400 wrong pipeline name
+    end
+    alt wrong header
+    Backend-)Frontend: Error 400 wrong header on file
+    end
+
+    Backend-)Backend: mount_container(connection_string(Environnement Variable, container_name))
+    Backend-)+Blob storage: HTTP POST req.
+    Blob storage--)-Backend: container_client
+    
+    Backend-)Backend: Generate Hash(image_bytes)
+
+    Backend-)Backend: upload_image(container_client, folder_name, image_bytes, hash_value)
+    Backend-)+Blob storage: HTTP POST req.
+    Blob storage--)-Backend: blob_name
+
+    Backend-)Backend: get_blob(container_client, blob_name)
+    Backend-)+Blob storage: HTTP POST req.
+    Blob storage--)-Backend: blob
+
+    loop for every model in pipeline
+        Backend-)Backend: model.entry_function(model, previous_result)
+        note over Backend, Blob storage: Every model has is own entry_function
+        Backend-)Backend: request_factory(previous_result, model)
+        Backend-)Backend: urllib.request.Request(endpoint_url, body, header)
+        Backend-)+Model: HTTP POST req.
+        Model--)-Backend: Result res.
+        alt if model has process_inference_function
+            Backend-) Backend: model.inference_function(previous_result, result_json)
         end
     end
-    Frontend--)Client: display result
+    note over Backend, Blob storage: End of the loop
+    par Backend to Frontend
+        Backend-)Backend: inference.process_inference_results(result_json, imageDims)
+        Backend--)Frontend: Processed result res.
+        Frontend--)-Client: display result
+    and Backend to Blob storage
+        note over Backend, Blob storage: record the result produced by the model
+        Backend-)Backend: upload_inference_result(container_client, folder_name, result_json_string, hash_value)
+        Backend-)-Blob storage: HTTP POST req.
+    end
 ```
 
 ![footer_for_diagram](https://github.com/ai-cfia/nachet-backend/assets/96267006/cf378d6f-5b20-4e1d-8665-2ba65ed54f8e)

model_request/model_request.py

@@ -6,10 +6,8 @@ async def request_factory(img_bytes: str | bytes, model: namedtuple) -> Request:
     """
     Args:
         img_bytes (str | bytes): The image data as either a string or bytes.
-        endpoint_url (str): The URL of the AI model endpoint.
-        api_key (str): The API key for accessing the AI model.
-        model_name (str): The name of the AI model.
-
+         model: (namedtuple): A tuple containing all the information necessary
+         to get the model inference.
     Returns:
         Request: The request object for calling the AI model.
     """