Update README

Author: saddam213

OnnxStack.Core/README.md

@@ -1,105 +1,63 @@
 # OnnxStack.Core - Onnx Services for .NET Applications
 
-OnnxStack.Core is a library that provides higher-level ONNX services for use in .NET applications. It offers extensive support for features such as dependency injection, .NET configuration implementations, ASP.NET Core integration, and IHostedService support.
-
-You can configure a model set for runtime, offloading individual models to different devices to make better use of resources or run on lower-end hardware. The first use-case is StableDiffusion; however, it will be expanded, and other model sets, such as object detection and classification, will be added.
+OnnxStack.Core is a library that provides simplified wrappers for OnnxRuntime
 
 ## Getting Started
 
-
 OnnxStack.Core can be found via the nuget package manager, download and install it.
 ```
 PM> Install-Package OnnxStack.Core
 ```
 
-
-### .NET Core Registration
-
-You can easily integrate `OnnxStack.Core` into your application services layer. This registration process sets up the necessary services and loads the `appsettings.json` configuration.
-
-Example: Registering OnnxStack
-```csharp
-builder.Services.AddOnnxStack();
-```
-
 ## Dependencies
-Video processing support requires FFMPEG and FFPROBE binaries, files must be present in your output folder or the destinations configured in the `appsettings.json`
+Video processing support requires FFMPEG and FFPROBE binaries, files must be present in your output folder or the destinations configured at runtime
 ```
 https://ffbinaries.com/downloads
 https://github.com/ffbinaries/ffbinaries-prebuilt/releases/download/v6.1/ffmpeg-6.1-win-64.zip
 https://github.com/ffbinaries/ffbinaries-prebuilt/releases/download/v6.1/ffprobe-6.1-win-64.zip
 ```
 
-## Configuration example
-The `appsettings.json` is the easiest option for configuring model sets. Below is an example of `clip tokenizer`.
 
-```json
-{
-	"Logging": {
-		"LogLevel": {
-			"Default": "Information",
-			"Microsoft.AspNetCore": "Warning"
-		}
-	},
-	"AllowedHosts": "*",
-
-	"OnnxStackConfig": {
-		"OnnxModelSets": [
-			{
-				"Name": "ClipTokenizer",
-				"IsEnabled": true,
-				"DeviceId": 0,
-				"InterOpNumThreads": 0,
-				"IntraOpNumThreads": 0,
-				"ExecutionMode": "ORT_SEQUENTIAL",
-				"ExecutionProvider": "DirectML",
-				"ModelConfigurations": [
-					{
-						"Type": "Tokenizer",
-						"OnnxModelPath": "cliptokenizer.onnx"
-					},
-				]
-			}
-		]
-	}
-}
-```
-
-
-
-### Basic C# Example
+### OnnxModelSession Example
 ```csharp
 
-// Tokenizer model Example
+// CLIP Tokenizer Example
 //----------------------//
 
-// From DI
-OnnxStackConfig _onnxStackConfig;
-IOnnxModelService _onnxModelService;
+// Model Configuration
+var config = new OnnxModelConfig
+{
+    DeviceId = 0,
+    InterOpNumThreads = 0,
+    IntraOpNumThreads = 0,
+    ExecutionMode = ExecutionMode.ORT_SEQUENTIAL,
+    ExecutionProvider = ExecutionProvider.DirectML,
+    OnnxModelPath = "cliptokenizer.onnx"
+};
 
-// Get Model
-var model = _onnxStackConfig.OnnxModelSets.First();
+// Create Model Session
+var modelSession = new OnnxModelSession(config);
 
-// Get Model Metadata
-var metadata = _onnxModelService.GetModelMetadata(model, OnnxModelType.Tokenizer);
+// Get Metatdata
+var modelMetadata = await modelSession.GetMetadataAsync();
 
-// Create Input
+// Create Input Tensor
 var text = "Text To Tokenize";
 var inputTensor = new DenseTensor<string>(new string[] { text }, new int[] { 1 });
 
-// Create  Inference Parameters container
-using (var inferenceParameters = new OnnxInferenceParameters(metadata))
+// Create Inference Parameters
+using (var inferenceParameters = new OnnxInferenceParameters(modelMetadata))
 {
-	// Set Inputs and Outputs
-	inferenceParameters.AddInputTensor(inputTensor);
-	inferenceParameters.AddOutputBuffer();
-
-	// Run Inference
-	using (var results = _onnxModelService.RunInference(model, OnnxModelType.Tokenizer, inferenceParameters))
-	{
-		// Extract Result
-		var resultData = results[0].ToDenseTensor();
-	}
+    // Set Inputs and Outputs
+    inferenceParameters.AddInputTensor(inputTensor);
+    inferenceParameters.AddOutputBuffer();
+
+    // Run Inference
+    using (var results = modelSession.RunInference(inferenceParameters))
+    {
+        // Extract Result Tokens
+        var resultData = results[0].ToArray<long>();
+    }
 }
 
 ```