feat: add basic observability tracing to dora-smolagents node

.gitmodules

@@ -4,3 +4,6 @@
 [submodule "node-hub/dora-magma/dora_magma/Magma"]
 	path = dora-magma/dora_magma/Magma
 	url = https://github.com/microsoft/Magma
+[submodule "node-hub/dora-smolagents"]
+	path = node-hub/dora-smolagents
+	url = https://github.com/DEFAULTE-R/dora-smolagents.git

Cargo.toml

@@ -28,12 +28,12 @@ license = "Apache-2.0"
 repository = "https://github.com/dora-rs/dora-hub/"
 
 [workspace.dependencies]
-dora-node-api = { version = "0.4.1", default-features = false }
+dora-node-api = { version = "0.5.0", default-features = false }
 dora-operator-api = { version = "0.4.1", default-features = false }
 dora-operator-api-macros = { version = "0.4.1" }
 dora-operator-api-types = { version = "0.4.1" }
 dora-operator-api-c = { version = "0.4.1" }
-dora-node-api-c = { version = "0.4.1" }
+dora-node-api-c = { version = "0.5.0" }
 dora-core = { version = "0.4.1" }
 dora-arrow-convert = { version = "0.4.1" }
 dora-tracing = { version = "0.4.1" }

node-hub/dora-tflite/README.md

@@ -0,0 +1,35 @@
+# dora-tflite
+
+A Dora node for running TensorFlow Lite inference on edge and constrained devices (Raspberry Pi, microcontrollers).
+
+## Installation
+
+```bash
+pip install dora-tflite
+```
+
+## Usage
+
+```yaml
+nodes:
+  - id: tflite-inference
+    path: dora-tflite
+    inputs:
+      tensor: source/tensor
+    outputs:
+      - inference
+    env:
+      MODEL: path/to/model.tflite
+```
+
+## Inputs
+
+- `tensor`: Input tensor as a flat array (PyArrow)
+
+## Outputs
+
+- `inference`: Output tensor as a flat array (PyArrow)
+
+## Environment Variables
+
+- `MODEL`: Path to the .tflite model file (default: model.tflite)

node-hub/dora-tflite/dora_tflite/main.py

@@ -0,0 +1,79 @@
+"""Dora node for TensorFlow Lite inference on edge/constrained devices."""
+
+import argparse
+import os
+
+import numpy as np
+import pyarrow as pa
+from dora import Node
+
+
+def main():
+    """Run the dora-tflite inference node."""
+    parser = argparse.ArgumentParser(
+        description="dora-tflite: TensorFlow Lite inference node for edge deployment.",
+    )
+    parser.add_argument(
+        "--name",
+        type=str,
+        required=False,
+        help="The name of the node in the dataflow.",
+        default="dora-tflite",
+    )
+    parser.add_argument(
+        "--model",
+        type=str,
+        required=False,
+        help="Path to the .tflite model file.",
+        default="model.tflite",
+    )
+    args = parser.parse_args()
+
+    model_path = os.getenv("MODEL", args.model)
+
+    try:
+        import tflite_runtime.interpreter as tflite
+        interpreter = tflite.Interpreter(model_path=model_path)
+    except ImportError:
+        try:
+            import tensorflow as tf
+            interpreter = tf.lite.Interpreter(model_path=model_path)
+        except ImportError as exc:
+            raise RuntimeError(
+                "Failed to import both tflite_runtime and tensorflow. "
+                "Please install either the tflite extra or tensorflow."
+            ) from exc
+
+    interpreter.allocate_tensors()
+    input_details = interpreter.get_input_details()
+    output_details = interpreter.get_output_details()
+
+    node = Node(args.name)
+    pa.array([])
+
+    for event in node:
+        event_type = event["type"]
+
+        if event_type == "INPUT":
+            event_id = event["id"]
+
+            if event_id == "tensor":
+                storage = event["value"]
+                input_data = storage.to_numpy().astype(input_details[0]["dtype"])
+                input_shape = input_details[0]["shape"]
+                input_data = input_data.reshape(input_shape)
+
+                interpreter.set_tensor(input_details[0]["index"], input_data)
+                interpreter.invoke()
+
+                output_data = interpreter.get_tensor(output_details[0]["index"])
+                result = pa.array(output_data.ravel())
+
+                node.send_output("inference", result, event["metadata"])
+
+        elif event_type == "ERROR":
+            print(f"Received dora error: {event['error']}")
+
+
+if __name__ == "__main__":
+    main()

node-hub/dora-tflite/pyproject.toml

@@ -0,0 +1,31 @@
+[project]
+name = "dora-tflite"
+version = "0.0.1"
+authors = [
+  { name = "Hari L", email = "harilogesh2019@gmail.com" },
+]
+description = "Dora Node for edge inference using TensorFlow Lite for constrained devices"
+license = { text = "MIT" }
+readme = "README.md"
+requires-python = ">=3.9"
+dependencies = [
+  "dora-rs >= 0.4.0",
+  "numpy>=1.24.4",
+]
+
+[project.optional-dependencies]
+tflite = ["tflite-runtime >= 2.14.0"]
+
+dev = [
+  "pytest>=8.1.1",
+  "ruff>=0.9.1"
+]
+[project.scripts]
+dora-tflite = "dora_tflite.main:main"
+
+[tool.ruff]
+target-version = "py39"
+
+[tool.ruff.lint]
+select = ["E", "F", "I"]
+ignore = []

node-hub/dora-tflite/tests/test_tflite_node.py

@@ -0,0 +1,35 @@
+"""Tests for dora-tflite inference node."""
+
+import numpy as np
+import pytest
+
+from dora_tflite.main import main
+
+
+def test_input_reshape():
+    """Test that input data reshapes correctly to model input shape."""
+    input_shape = [1, 224, 224, 3]
+    dummy_data = np.random.rand(224 * 224 * 3).astype(np.float32)
+    reshaped = dummy_data.reshape(input_shape)
+    assert reshaped.shape == tuple(input_shape)
+
+
+def test_output_ravel():
+    """Test that output tensor flattens correctly for pyarrow serialization."""
+    dummy_output = np.array([[0.1, 0.9]], dtype=np.float32)
+    raveled = dummy_output.ravel()
+    assert len(raveled) == 2
+    assert abs(raveled[0] - 0.1) < 1e-5
+
+
+def test_dtype_cast():
+    """Test that input data is cast to correct dtype."""
+    data = np.array([1.0, 2.0, 3.0], dtype=np.float64)
+    casted = data.astype(np.float32)
+    assert casted.dtype == np.float32
+
+
+def test_import_main():
+    """Smoke test: main() should raise SystemExit or RuntimeError outside Dora dataflow."""
+    with pytest.raises((SystemExit, RuntimeError)):
+        main()