feat: publishing infernet-container-starter v0.2.0

projects/gpt4/container/src/app.py

@@ -0,0 +1,90 @@
+import logging
+from typing import Any, cast
+
+from eth_abi import decode, encode  # type: ignore
+from infernet_ml.utils.service_models import InfernetInput, InfernetInputSource
+from infernet_ml.workflows.inference.css_inference_workflow import CSSInferenceWorkflow
+from quart import Quart, request
+
+log = logging.getLogger(__name__)
+
+
+def create_app() -> Quart:
+    app = Quart(__name__)
+
+    workflow = CSSInferenceWorkflow(provider="OPENAI", endpoint="completions")
+
+    workflow.setup()
+
+    @app.route("/")
+    def index() -> str:
+        """
+        Utility endpoint to check if the service is running.
+        """
+        return "GPT4 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:
+            prompt = cast(dict[str, Any], infernet_input.data).get("prompt")
+        else:
+            # On-chain requests are sent as a generalized hex-string which we will
+            # decode to the appropriate format.
+            (prompt,) = decode(
+                ["string"], bytes.fromhex(cast(str, infernet_input.data))
+            )
+
+        result: dict[str, Any] = workflow.inference(
+            {
+                "model": "gpt-4-0613",
+                "params": {
+                    "endpoint": "completions",
+                    "messages": [
+                        {"role": "system", "content": "You are a helpful assistant."},
+                        {"role": "user", "content": prompt},
+                    ],
+                },
+            }
+        )
+
+        if infernet_input.source == InfernetInputSource.OFFCHAIN:
+            """
+            In case of an off-chain request, the result is returned as is.
+            """
+            return {"message": 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.
+            """
+            return {
+                "raw_input": "",
+                "processed_input": "",
+                "raw_output": encode(["string"], [result]).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/gpt4/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
+retry2==0.9.5