feat: publishing infernet-container-starter v0.2.0

projects/prompt-to-nft/container/.gitignore

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+wallet
+config.json
+**/keyfile-arweave.json

projects/prompt-to-nft/container/Dockerfile

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+FROM python:3.11-slim as builder
+
+WORKDIR /app
+
+ENV PYTHONUNBUFFERED 1
+ENV PYTHONDONTWRITEBYTECODE 1
+ENV PIP_NO_CACHE_DIR 1
+ENV RUNTIME docker
+ENV PYTHONPATH src
+
+RUN apt-get update
+RUN apt-get install -y git curl
+
+# install uv
+ADD --chmod=755 https://astral.sh/uv/install.sh /install.sh
+RUN /install.sh && rm /install.sh
+
+COPY src/requirements.txt .
+
+RUN /root/.cargo/bin/uv pip install --system --no-cache -r requirements.txt
+
+copy wallet wallet
+
+COPY src src
+
+ENTRYPOINT ["hypercorn", "app:create_app()"]
+CMD ["-b", "0.0.0.0:3000"]

projects/prompt-to-nft/container/Makefile

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+DOCKER_ORG := ritualnetwork
+EXAMPLE_NAME := prompt-to-nft
+TAG := $(DOCKER_ORG)/example-$(EXAMPLE_NAME)-infernet:latest
+
+.phony: build run build-multiplatform
+
+build:
+ifdef CI
+	mkdir -p wallet # in CI we don't have a wallet directory. This enables to bypass that and ensure that the image
+	# is built successfully
+endif
+	@docker build -t $(TAG) .
+
+wallet_dir ?= /app/wallet
+
+run:
+	docker run -p 3000:3000 -v ./wallet:$(wallet_dir) --env-file prompt_to_nft.env $(TAG)
+
+# You may need to set up a docker builder, to do so run:
+# docker buildx create --name mybuilder --bootstrap --use
+# refer to https://docs.docker.com/build/building/multi-platform/#building-multi-platform-images for more info
+build-multiplatform:
+	docker buildx build --platform linux/amd64,linux/arm64 -t $(TAG) --push .

projects/prompt-to-nft/container/README.md

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+# Prompt-to-NFT Container
+
+
+## Overview
+
+
+## Building & Running the Container in Isolation
+
+Note that this container is meant to be started by the infernet-node. For development &
+Testing purposes, you can run the container in isolation using the following commands.
+
+### Building the Container
+
+Simply run the following command to build the container.
+
+```bash
+make build
+```
+
+Consult the [Makefile](./Makefile) for the build command.
+
+### Adding Arweave File
+Add your arweave wallet file
+
+
+
+### Running the Container
+
+To run the container, you can use the following command:
+
+```bash
+make run
+```
+
+## Testing the Container
+
+Run the following command to run an inference:
+
+```bash
+curl -X POST http://127.0.0.1:3000/service_output \
+     -H "Content-Type: application/json" \
+     -d '{"source":1, "data": {"prompt": "a golden retriever skiing"}}'
+```
+
+#### Note Regarding the Input
+
+The inputs provided above correspond to an iris flower with the following
+characteristics. Refer to the
+
+1. Sepal Length: `5.5cm`
+2. Sepal Width: `2.4cm`
+3. Petal Length: `3.8cm`
+4. Petal Width: `1.1cm`
+
+Putting this input into a vector and scaling it, we get the following scaled input:
+
+```python
+[1.0380048, 0.5586108, 1.1037828, 1.712096]
+```
+
+Refer
+to [this function in the model's repository](https://github.com/ritual-net/simple-ml-models/blob/03ebc6fb15d33efe20b7782505b1a65ce3975222/iris_classification/iris_inference_pytorch.py#L13)
+for more information on how the input is scaled.
+
+For more context on the Iris dataset, refer to
+the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/iris).
+
+### Output
+
+By running the above command, you should get a response similar to the following:
+
+```json
+[
+  [
+    [
+      0.0010151526657864451,
+      0.014391022734344006,
+      0.9845937490463257
+    ]
+  ]
+]
+```
+
+The response corresponds to the model's prediction for each of the classes:
+
+```python
+['setosa', 'versicolor', 'virginica']
+```
+
+In this case, the model predicts that the input corresponds to the class `virginica`with
+a probability of `0.9845937490463257`(~98.5%).

projects/prompt-to-nft/container/config.sample.json

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+{
+    "log_path": "infernet_node.log",
+    "server": {
+        "port": 4000
+    },
+    "chain": {
+        "enabled": true,
+        "trail_head_blocks": 0,
+        "rpc_url": "http://host.docker.internal:8545",
+        "coordinator_address": "0x5FbDB2315678afecb367f032d93F642f64180aa3",
+        "wallet": {
+            "max_gas_limit": 4000000,
+            "private_key": "0x59c6995e998f97a5a0044966f0945389dc9e86dae88c7a8412f4603b6b78690d"
+        }
+    },
+    "startup_wait": 1.0,
+    "docker": {
+        "username": "your-username",
+        "password": ""
+    },
+    "redis": {
+        "host": "redis",
+        "port": 6379
+    },
+    "forward_stats": true,
+    "containers": [
+        {
+            "id": "prompt-to-nft",
+            "image": "ritualnetwork/example-prompt-to-nft-infernet:latest",
+            "external": true,
+            "port": "3000",
+            "allowed_delegate_addresses": [],
+            "allowed_addresses": [],
+            "allowed_ips": [],
+            "command": "--bind=0.0.0.0:3000 --workers=2",
+            "env": {
+                "ARWEAVE_WALLET_FILE_PATH": "wallet/keyfile-arweave.json",
+                "IMAGE_GEN_SERVICE_URL": "http://your.services.ip:port"
+            }
+        },
+        {
+            "id": "anvil-node",
+            "image": "ritualnetwork/infernet-anvil:0.0.0",
+            "external": true,
+            "port": "8545",
+            "allowed_delegate_addresses": [],
+            "allowed_addresses": [],
+            "allowed_ips": [],
+            "command": "",
+            "env": {}
+        }
+    ]
+}

projects/prompt-to-nft/container/prompt_to_nft.env.sample

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+ARWEAVE_WALLET_FILE_PATH=
+IMAGE_GEN_SERVICE_URL=

projects/prompt-to-nft/container/src/app.py

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+import logging
+import os
+from pathlib import Path
+from typing import Any, cast
+
+import aiohttp
+from eth_abi import decode, encode  # type: ignore
+from infernet_ml.utils.arweave import upload, load_wallet
+from infernet_ml.utils.service_models import InfernetInput, InfernetInputSource
+from quart import Quart, request
+
+log = logging.getLogger(__name__)
+
+
+async def run_inference(prompt: str, output_path: str) -> None:
+    async with aiohttp.ClientSession() as session:
+        app_url = os.getenv("IMAGE_GEN_SERVICE_URL")
+        async with session.post(
+            f"{app_url}/service_output",
+            json={
+                "prompt": prompt,
+            },
+        ) as response:
+            image_bytes = await response.read()
+            with open(output_path, "wb") as f:
+                f.write(image_bytes)
+
+
+def ensure_env_vars() -> None:
+    if not os.getenv("IMAGE_GEN_SERVICE_URL"):
+        raise ValueError("IMAGE_GEN_SERVICE_URL environment variable not set")
+    load_wallet()
+
+
+def create_app() -> Quart:
+    app = Quart(__name__)
+    ensure_env_vars()
+
+    @app.route("/")
+    def index() -> str:
+        """
+        Utility endpoint to check if the service is running.
+        """
+        return "Stable Diffusion 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)
+        temp_file = "image.png"
+
+        if infernet_input.source == InfernetInputSource.OFFCHAIN:
+            prompt: str = cast(dict[str, str], infernet_input.data)["prompt"]
+        else:
+            # On-chain requests are sent as a generalized hex-string which we will
+            # decode to the appropriate format.
+            (prompt, mintTo) = decode(
+                ["string", "address"], bytes.fromhex(cast(str, infernet_input.data))
+            )
+            log.info("mintTo: %s", mintTo)
+            log.info("prompt: %s", prompt)
+
+        # run the inference and download the image to a temp file
+        await run_inference(prompt, temp_file)
+
+        tx = upload(Path(temp_file), {"Content-Type": "image/png"})
+
+        if infernet_input.source == InfernetInputSource.OFFCHAIN:
+            """
+            In case of an off-chain request, the result is returned as is.
+            """
+            return {
+                "prompt": prompt,
+                "hash": tx.id,
+                "image_url": f"https://arweave.net/{tx.id}",
+            }
+        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": infernet_input.data,
+                "processed_input": "",
+                "raw_output": encode(["string"], [tx.id]).hex(),
+                "processed_output": "",
+                "proof": "",
+            }
+
+    return app
+
+
+if __name__ == "__main__":
+    """
+    Utility to run the app locally. For development purposes only.
+    """
+    create_app().run(host="0.0.0.0", port=3000)

projects/prompt-to-nft/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
+tqdm==4.66.1