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