issue #51: Implement Ricky's review

issue #51: change mermaid theme

.devcontainer/devcontainer.json

@@ -5,9 +5,7 @@
 	// Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile
 	"image": "mcr.microsoft.com/devcontainers/python:1-3.11-bullseye",
 	"features": {
-		"ghcr.io/devcontainers/features/azure-cli:1": {},
-		"ghcr.io/devcontainers/features/docker-in-docker:2": {},
-		"ghcr.io/devcontainers/features/github-cli:1": {}
+		"ghcr.io/devcontainers/features/azure-cli:1": {}
 	},
 
 	// Features to add to the dev container. More info: https://containers.dev/features.
@@ -27,7 +25,7 @@
 			"GitHub.vscode-pull-request-github",
 			"ms-python.python",
 			"ms-python.black-formatter",
-			"stkb.rewrap",
+			"stkb.rewrap"
 		  ]
 		}
 	  }

app.py

@@ -151,9 +151,9 @@ async def inference_request():
     The image and inference results are uploaded to a folder in the user's container.
     """
 
-    seconds = time.perf_counter() # transform into logging
+    seconds = time.perf_counter() # TODO: transform into logging
     try:
-        print(f"{date.today()} Entering inference request") # Transform into logging
+        print(f"{date.today()} Entering inference request") # TODO: Transform into logging
         data = await request.get_json()
         pipeline_name = data.get("model_name")
         folder_name = data["folder_name"]
@@ -190,16 +190,16 @@ async def inference_request():
         image_bytes = base64.b64encode(blob).decode("utf8")
 
         # Keep track of every output given by the models
-        # TO DO add it to CACHE variable
+        # TODO: add it to CACHE variable
         cache_json_result = [image_bytes]
 
         for idx, model in enumerate(pipelines_endpoints.get(pipeline_name)):
-            print(f"Entering {model.name.upper()} model") # Transform into logging
+            print(f"Entering {model.name.upper()} model") # TODO: Transform into logging
             result_json = await model.entry_function(model, cache_json_result[idx])
             cache_json_result.append(result_json)
 
-        print("End of inference request") # Transform into logging
-        print("Process results") # Transform into logging
+        print("End of inference request") # TODO: Transform into logging
+        print("Process results") # TODO: Transform into logging
 
         processed_result_json = await inference.process_inference_results(
             cache_json_result[-1], imageDims
@@ -216,7 +216,7 @@ async def inference_request():
             hash_value,
         )
         # return the inference results to the client
-        print(f"Took: {'{:10.4f}'.format(time.perf_counter() - seconds)} seconds")
+        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:

docs/nachet-inference-documentation.md

@@ -57,11 +57,9 @@ 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 1.2.1
-
 ```mermaid
 sequenceDiagram
-
+  title: Sequence Diagram for inference request 1.2.1
     actor Client
     participant Frontend
     participant Backend

tests/test_health_request.py

@@ -10,8 +10,6 @@ def test_health(self):
         response = asyncio.run(
             test.get('/health')
         )
-
-        print(response.status_code == 200)
         self.assertEqual(response.status_code, 200)
 
 if __name__ == '__main__':