78 errors in return value of models json example

app.py

@@ -373,7 +373,7 @@ async def inference_request():
             cache_json_result[-1], imageDims, area_ratio, color_format
         )
 
-        result_json_string = await record_model(pipeline, processed_result_json)
+        result_json_string = await record_model(pipeline_name, pipeline, processed_result_json)
 
         # upload the inference results to the user's container as async task
         app.add_background_task(
@@ -387,7 +387,7 @@ async def inference_request():
         print(f"Took: {'{:10.4f}'.format(time.perf_counter() - seconds)} seconds") # TODO: Transform into logging
         return jsonify(processed_result_json), 200
 
-    except (KeyError, InferenceRequestError) as error:
+    except (KeyError, InferenceRequestError, inference.ProcessInferenceResultError) as error:
         print(error)
         return jsonify(["InferenceRequestError: " + error.args[0]]), 400
 
@@ -456,10 +456,24 @@ async def test():
 
     return CACHE["endpoints"], 200
 
-
-async def record_model(pipeline: namedtuple, result: list):
+
+async def record_model(name: str, pipeline: namedtuple, result: list):
+    """
+    Records the models in the pipeline to the result list.
+
+    Args:
+        name (str): The name of the pipeline.
+        pipeline (namedtuple): The pipeline containing the models.
+        result (list): The result list to update.
+
+    Returns:
+        str: The updated result list as a JSON string.
+    """
     new_entry = [{"name": model.name, "version": model.version} for model in pipeline]
-    result[0]["models"] = new_entry
+    result[0].update({
+        "pipeline": name,
+        "models": new_entry
+    })
     return json.dumps(result, indent=4)
 
 
@@ -494,7 +508,6 @@ async def get_pipelines(connection_string, pipeline_blob_name, pipeline_version,
         app.config["BLOB_CLIENT"] = await azure_storage_api.get_blob_client(connection_string)
         result_json = await azure_storage_api.get_pipeline_info(app.config["BLOB_CLIENT"], pipeline_blob_name, pipeline_version)
     except (azure_storage_api.AzureAPIErrors) as error:
-        print(error)
         raise ServerError("server errror: could not retrieve the pipelines") from error
 
     models = ()

docs/nachet-inference-documentation.md

@@ -34,10 +34,10 @@ output"|G>"return
 result.json to user"]
 
 subgraph SB1[In pipeline]
-    direction TB
+  direction TB
 
-    B(model 01)-->|"model output
-    send to the next one"|C(model 02)-->|can have n models|D(n models)
+  B(model 01)-->|"model output
+  send to the next one"|C(model 02)-->|can have n models|D(n models)
 
 end
 ```
@@ -61,80 +61,83 @@ to a model and receive the result.
 
 ```mermaid
 sequenceDiagram
-  title: Sequence Diagram for inference request 1.2.1
-    actor Client
-    participant Frontend
-    participant Backend
-    participant Blob storage
-    participant Model
-
-    Backend-)+Backend: run()
-    Note over Backend,Blob storage: initialisation
-    Backend-)Backend: before_serving()
-    Backend-)Backend: get_pipelines()
-    alt
-    Backend-)+Blob storage: HTTP POST req.
-    Blob storage--)-Backend: return pipelines_models.json
-    else
-    Backend-)Frontend: error 500 Failed to retrieve data from the repository
-    end
-    Note over Backend,Blob storage: end of initialisation
-
-    Client->>+Frontend: applicationStart()
-    Frontend-)Backend: HTTP POST req.
-    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 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
-    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
+ title: Sequence Diagram for inference request 1.2.1
+  actor Client
+  participant Frontend
+  participant Backend
+  participant Blob storage
+  participant Model
+
+  Backend-)+Backend: run()
+  Note over Backend, Blob storage: initialization
+  Backend-)Backend: before_serving()
+  Backend-)Backend: get_pipelines()
+  alt
+  Backend-)+Blob storage: HTTP POST req.
+  Blob storage--)-Backend: return pipelines_models.json
+  else
+  Backend-)Frontend: error 500 Failed to retrieve data from the repository
+  end
+  Note over Backend, Blob storage: end of initialization
+
+  Client->>+Frontend: applicationStart()
+  Frontend-)Backend: HTTP POST req.
+  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 asks actions from a specific pipeline
+  Frontend-)Backend: HTTP POST req.
+  Backend-)Backend: inference_request(pipeline_name, folder_name, container_name, imageDims, image)
+  alt missing arguments
+  Backend-)Frontend: Error 400 missing arguments
+  end
+  alt wrong pipeline name
+  Backend-)Frontend: Error 400 wrong pipeline name
+  end
+  alt wrong image
+  Backend-)Frontend: Error 400 this picture was not validated
+  end
+
+  Backend-)Backend: mount_container(blob_service_client, 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)
+
+  loop for every model in pipeline
+    Backend-)Backend: model.request_function(model, previous_result)
+    note over Backend, Blob storage: Every model has its own request_function
+    alt Get inference from model
+      alt seed detector or nachet-6seeds model
+        Backend->>+Model: urlopen(Request(model.endpoint, body, headers))
+        Model-->>-Backend: Result res.
+      else seed-detector model has a process function
+        Backend->>Backend: process(images_bytes, inf_result)
+      end
+    else swin model inference is requested
+      loop for every image in previous_result
+        note over Backend, Model: Swin can only accept 1 seed per images
+        Backend->>+Model: urlopen(Request(model.endpoint, body, headers))
+        Model-->>-Backend: Result res.
+        Backend->>Backend: results.append(Result)
+      end
+      Backend->>Backend: process(previous_result, results)
+    end
+  end
+  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)
@@ -146,13 +149,13 @@ 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.
+and if the image sent for analysis has been validated.
 
 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.
+in the pipeline. Each model specifies their `request_function` (how to call and
+retrieve data). 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.
 
@@ -163,10 +166,13 @@ The inference request will process the following parameters:
 |Key parameters | Expected Value|
 |--|--|
 |model_name | The name of the pipeline|
+|vallidator| A hash that has been returning to the frontend when the image is valid|
 |folder_name | The folder where the image is uploaded in the user's container|
 |container_name | The user's container|
 |imageDims | The dimension of the image|
 |image | The image encoded in b64 (ASCII)|
+|area_ratio| The ratio specified by the user, default = 0.5|
+|color_format| The ccolor format specified by the frontend, default = "hex"|
 
 Note that since the information is received from the frontend, the model_name is
 an abstraction for a pipeline.
@@ -178,20 +184,23 @@ The inference request will return a list with the following information:
 |Boxes | 0 | Contains all the boxes returned by the inference request|
 |labelOccurence | 0 | Contains the number of label occurence|
 |totalBoxes | 0 | Boxes total number|
+|pipeline| 0 | Contains the name of the pipeline that produced the inference|
+|models| 0 | Contains the models used by the pipeline|
 |Box | 1 | Contains all the information of one seed in the image|
 |label | 1 | Contains the top label for the seed|
 |score | 1 | Contains the top score for the seed|
 |topN | 1 | Contains the top N scores for the seed|
-|overlapping | 1 | Contains a boolean to tell if the box overlap with another one|
+|overlapping | 1 | Contains a boolean to tell if the box overlaps with another one|
 |overlappingIndices | 1 | Contains the index of the overlapping box|
 |topX | 2 | The top x value of the box around a seed|
 |topY | 2 | The top y value of the box around a seed|
 |bottomX | 2 | The bottom x value of the box around a seed|
 |bottomY| 2 | The bottom y value of the box around a seed|
 
-*for more look at [nachet-model-documentation](https://github.com/ai-cfia/nachet-backend/blob/51-implementing-2-models/docs/nachet-model-documentation.md#return-value-of-models)*
+*for more look at [nachet-model-documentation](nachet-model-documentation.md#return-value-of-models)*
 
-**topN** contains the top 5 predictions of the models:
+**topN** contains the top n predictions of a given model. For now, only `Swin`
+returns a `topN` key with 5 results:
 
 ```json
 "topN": [
@@ -220,19 +229,22 @@ The inference request will return a list with the following information:
 
 ### Blob storage and Pipeline versioning
 
+:warning: **This section will need adjustment after the implementation of the
+datastore into Nachet**
+
 To keep track of the various pipeline iterations and versions, JSON files are
-stored in the blob storage. Users can add the JSON to the blob storage
+stored in the blob storage. Datascientists can add the JSON to the blob storage
 using the `pipelines_version_insertion.py` script. This allows for easy
 management of model and pipeline history.
 
 To use the script, 3 environment variables are necessary:
 
-* NACHET_BLOB_PIPELINE_NAME
-  * Containing the blob name where the pipelines are stored
-* NACHET_BLOB_PIPELINE_VERSION
-  * Containing the version the user wants to select
-* NACHET_BLOB_PIPELINE_DECRYPTION_KEY
-  * The key to decrypt sensible data such as the API key and the endpoint of a model.
+* **NACHET_BLOB_PIPELINE_NAME**
+ * Containing the blob name where the pipelines are stored
+* **NACHET_BLOB_PIPELINE_VERSION**
+ * Containing the version the user wants to select
+* **NACHET_BLOB_PIPELINE_DECRYPTION_KEY**
+ * The key to decrypt sensible data such as the API key and the endpoint of a model.
 
 #### In the code
 
@@ -245,7 +257,6 @@ information and metadata to the frontend.
 async def get_pipelines(connection_string, pipeline_blob_name, pipeline_version, cipher_suite):
     """
     Retrieves the pipelines from the Azure storage API.
-
     Returns:
     - list: A list of dictionaries representing the pipelines.
     """
@@ -280,6 +291,8 @@ async def get_pipelines(connection_string, pipeline_blob_name, pipeline_version,
 
 ### Available Version of the JSON file
 
+***Careful as major update might change pipeline and model attributes***
+
 |Version|Creation Date| Pipelines|
 |--|--|--|
 |0.1.3 | 2024-03-26 | Swin Transformer and 6 Seeds Detector|