feat: publishing infernet-container-starter v0.2.0

projects/onnx-iris/container/src/app.py

@@ -0,0 +1,107 @@
+import logging
+from typing import Any, cast, List
+
+import numpy as np
+from eth_abi import decode, encode  # type: ignore
+from infernet_ml.utils.model_loader import ModelSource
+from infernet_ml.utils.service_models import InfernetInput, InfernetInputSource
+from infernet_ml.workflows.inference.onnx_inference_workflow import (
+    ONNXInferenceWorkflow,
+)
+from quart import Quart, request
+from quart.json.provider import DefaultJSONProvider
+
+log = logging.getLogger(__name__)
+
+
+class NumpyJsonEncodingProvider(DefaultJSONProvider):
+    @staticmethod
+    def default(obj: Any) -> Any:
+        if isinstance(obj, np.ndarray):
+            # Convert NumPy arrays to list
+            return obj.tolist()
+        # fallback to default JSON encoding
+        return DefaultJSONProvider.default(obj)
+
+
+def create_app() -> Quart:
+    Quart.json_provider_class = NumpyJsonEncodingProvider
+    app = Quart(__name__)
+    # we are downloading the model from the hub.
+    # model repo is located at: https://huggingface.co/Ritual-Net/iris-dataset
+    model_source = ModelSource.HUGGINGFACE_HUB
+    model_args = {"repo_id": "Ritual-Net/iris-dataset", "filename": "iris.onnx"}
+
+    workflow = ONNXInferenceWorkflow(model_source=model_source, model_args=model_args)
+    workflow.setup()
+
+    @app.route("/")
+    def index() -> str:
+        """
+        Utility endpoint to check if the service is running.
+        """
+        return "ONNX Iris Classifier Example Program"
+
+    @app.route("/service_output", methods=["POST"])
+    async def inference() -> dict[str, Any]:
+        req_data = await request.get_json()
+        """
+        InfernetInput has the format:
+            source: (0 on-chain, 1 off-chain)
+            data: dict[str, Any]
+        """
+        infernet_input: InfernetInput = InfernetInput(**req_data)
+
+        if infernet_input.source == InfernetInputSource.OFFCHAIN:
+            web2_input = cast(dict[str, Any], infernet_input.data)
+            values = cast(List[List[float]], web2_input["input"])
+        else:
+            # On-chain requests are sent as a generalized hex-string which we will
+            # decode to the appropriate format.
+            web3_input: List[int] = decode(
+                ["uint256[]"], bytes.fromhex(cast(str, infernet_input.data))
+            )[0]
+            values = [[float(v) / 1e6 for v in web3_input]]
+
+        """
+        The input to the onnx inference workflow needs to conform to ONNX runtime's
+        input_feed format. For more information refer to:
+        https://docs.ritual.net/ml-workflows/inference-workflows/onnx_inference_workflow
+        """
+        result: dict[str, Any] = workflow.inference({"input": values})
+
+        if infernet_input.source == InfernetInputSource.OFFCHAIN:
+            """
+            In case of an off-chain request, the result is returned as is.
+            """
+            return result
+        else:
+            """
+            In case of an on-chain request, the result is returned in the format:
+            {
+                "raw_input": str,
+                "processed_input": str,
+                "raw_output": str,
+                "processed_output": str,
+                "proof": str,
+            }
+            refer to: https://docs.ritual.net/infernet/node/containers for more info.
+            """
+            predictions = cast(List[List[List[float]]], result)
+            predictions_normalized = [int(p * 1e6) for p in predictions[0][0]]
+            return {
+                "raw_input": "",
+                "processed_input": "",
+                "raw_output": encode(["uint256[]"], [predictions_normalized]).hex(),
+                "processed_output": "",
+                "proof": "",
+            }
+
+    return app
+
+
+if __name__ == "__main__":
+    """
+    Utility to run the app locally. For development purposes only.
+    """
+    create_app().run(port=3000)

projects/onnx-iris/container/src/requirements.txt

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+quart==0.19.4
+infernet_ml==0.1.0
+PyArweave @ git+https://github.com/ritual-net/pyarweave.git
+web3==6.15.0
+onnx==1.15.0
+onnxruntime==1.16.3
+torch==2.1.2