# Conditional pipeline with pandas query model

The model is defined as an MLServer custom runtime and allows the user to pass in a custom pandas query as a parameter defined at model creation to be used to filter the data passed to the model.

```python
from mlserver import MLModel
from mlserver.types import InferenceRequest, InferenceResponse
from mlserver.codecs import PandasCodec
from mlserver.errors import MLServerError
import pandas as pd
from fastapi import status
from mlserver.logging import logger

QUERY_KEY = "query"


class ModelParametersMissing(MLServerError):
  def __init__(self, model_name: str, reason: str):
    super().__init__(
      f"Parameters missing for model {model_name} {reason}", status.HTTP_400_BAD_REQUEST
    )

class PandasQueryRuntime(MLModel):

  async def load(self) -> bool:
    logger.info("Loading with settings %s", self.settings)
    if self.settings.parameters is None or \
      self.settings.parameters.extra is None:
      raise ModelParametersMissing(self.name, "no settings.parameters.extra found")
    self.query = self.settings.parameters.extra[QUERY_KEY]
    if self.query is None:
      raise ModelParametersMissing(self.name, "no settings.parameters.extra.query found")
    self.ready = True

    return self.ready

  async def predict(self, payload: InferenceRequest) -> InferenceResponse:
    input_df: pd.DataFrame = PandasCodec.decode_request(payload)
    # run query on input_df and save in output_df
    output_df = input_df.query(self.query)
    if output_df.empty:
      output_df = pd.DataFrame({'status':["no rows satisfied " + self.query]})
    else:
      output_df["status"] = "row satisfied " + self.query
    return PandasCodec.encode_response(self.name, output_df, self.version)
```

## Conditional Pipeline using PandasQuery

```bash
cat ../../models/choice1.yaml
echo "---"
cat ../../models/choice2.yaml
echo "---"
cat ../../models/add10.yaml
echo "---"
cat ../../models/mul10.yaml
```

```yaml
apiVersion: mlops.seldon.io/v1alpha1
kind: Model
metadata:
  name: choice-is-one
spec:
  storageUri: "gs://seldon-models/scv2/examples/pandasquery"
  requirements:
  - mlserver
  - python
  parameters:
  - name: query
    value: "choice == 1"
---
apiVersion: mlops.seldon.io/v1alpha1
kind: Model
metadata:
  name: choice-is-two
spec:
  storageUri: "gs://seldon-models/scv2/examples/pandasquery"
  requirements:
  - mlserver
  - python
  parameters:
  - name: query
    value: "choice == 2"
---
apiVersion: mlops.seldon.io/v1alpha1
kind: Model
metadata:
  name: add10
spec:
  storageUri: "gs://seldon-models/scv2/samples/triton_23-03/add10"
  requirements:
  - triton
  - python
---
apiVersion: mlops.seldon.io/v1alpha1
kind: Model
metadata:
  name: mul10
spec:
  storageUri: "gs://seldon-models/scv2/samples/triton_23-03/mul10"
  requirements:
  - triton
  - python

```

{% tabs %}
{% tab title="kubectl" %}

```bash
kubectl apply -f ../../models/choice1.yaml -n ${NAMESPACE}
kubectl apply -f ../../models/choice2.yaml -n ${NAMESPACE}
kubectl apply -f ../../models/add10.yaml -n ${NAMESPACE}
kubectl apply -f ../../models/mul10.yaml -n ${NAMESPACE}
```

```bash
model.mlops.seldon.io/choice1 created
model.mlops.seldon.io/choice2 created
model.mlops.seldon.io/add10 created
model.mlops.seldon.io/mul10 created
```

{% endtab %}

{% tab title="seldon-cli" %}

```bash
seldon model load -f ../../models/choice1.yaml
seldon model load -f ../../models/choice2.yaml
seldon model load -f ../../models/add10.yaml
seldon model load -f ../../models/mul10.yaml
```

```json
{}
{}
{}
{}
```

{% endtab %}
{% endtabs %}

{% tabs %}
{% tab title="kubectl" %}

```bash
kubectl wait --for condition=ready --timeout=300s model choice-is-one -n ${NAMESPACE}
kubectl wait --for condition=ready --timeout=300s model choice-is-two -n ${NAMESPACE}
kubectl wait --for condition=ready --timeout=300s model add10 -n ${NAMESPACE}
kubectl wait --for condition=ready --timeout=300s model mul10 -n ${NAMESPACE}
```

```bash
model.mlops.seldon.io/choice-is-one condition met
model.mlops.seldon.io/choice-is-two condition met
model.mlops.seldon.io/add10 condition met
model.mlops.seldon.io/mul10 condition met
```

{% endtab %}

{% tab title="seldon-cli" %}

```bash
seldon model status choice-is-one -w ModelAvailable
seldon model status choice-is-two -w ModelAvailable
seldon model status add10 -w ModelAvailable
seldon model status mul10 -w ModelAvailable
```

```json
{}
{}
{}
{}

```

{% endtab %}
{% endtabs %}

```bash
cat ../../pipelines/choice.yaml
```

```yaml
apiVersion: mlops.seldon.io/v1alpha1
kind: Pipeline
metadata:
  name: choice
spec:
  steps:
  - name: choice-is-one
  - name: mul10
    inputs:
    - choice.inputs.INPUT
    triggers:
    - choice-is-one.outputs.choice
  - name: choice-is-two
  - name: add10
    inputs:
    - choice.inputs.INPUT
    triggers:
    - choice-is-two.outputs.choice
  output:
    steps:
    - mul10
    - add10
    stepsJoin: any

```

{% tabs %}
{% tab title="kubectl" %}

```bash
kubectl apply -f pipelines/choice.yaml
```

```
pipeline.mlops.seldon.io/choice created
```

```bash
kubectl wait --for condition=ready --timeout=300s pipelines choice -n ${NAMESPACE}
```

```
pipeline.mlops.seldon.io/choice condition met
```

{% endtab %}

{% tab title="seldon-cli" %}

```bash
seldon pipeline load -f ../../pipelines/choice.yaml
```

```bash
seldon pipeline status choice -w PipelineReady | jq -M .
```

```json
{
  "pipelineName": "choice",
  "versions": [
    {
      "pipeline": {
        "name": "choice",
        "uid": "cifel9aufmbc73e5intg",
        "version": 1,
        "steps": [
          {
            "name": "add10",
            "inputs": [
              "choice.inputs.INPUT"
            ],
            "triggers": [
              "choice-is-two.outputs.choice"
            ]
          },
          {
            "name": "choice-is-one"
          },
          {
            "name": "choice-is-two"
          },
          {
            "name": "mul10",
            "inputs": [
              "choice.inputs.INPUT"
            ],
            "triggers": [
              "choice-is-one.outputs.choice"
            ]
          }
        ],
        "output": {
          "steps": [
            "mul10.outputs",
            "add10.outputs"
          ],
          "stepsJoin": "ANY"
        },
        "kubernetesMeta": {}
      },
      "state": {
        "pipelineVersion": 1,
        "status": "PipelineReady",
        "reason": "created pipeline",
        "lastChangeTimestamp": "2023-06-30T14:45:57.284684328Z",
        "modelsReady": true
      }
    }
  ]
}
```

{% endtab %}
{% endtabs %}

```bash
seldon pipeline infer choice --inference-mode grpc \
 '{"model_name":"choice","inputs":[{"name":"choice","contents":{"int_contents":[1]},"datatype":"INT32","shape":[1]},{"name":"INPUT","contents":{"fp32_contents":[5,6,7,8]},"datatype":"FP32","shape":[4]}]}' | jq -M .
```

```json
{
  "outputs": [
    {
      "name": "OUTPUT",
      "datatype": "FP32",
      "shape": [
        "4"
      ],
      "contents": {
        "fp32Contents": [
          50,
          60,
          70,
          80
        ]
      }
    }
  ]
}

```

```bash
seldon pipeline infer choice --inference-mode grpc \
 '{"model_name":"choice","inputs":[{"name":"choice","contents":{"int_contents":[2]},"datatype":"INT32","shape":[1]},{"name":"INPUT","contents":{"fp32_contents":[5,6,7,8]},"datatype":"FP32","shape":[4]}]}' | jq -M .
```

```json
{
  "outputs": [
    {
      "name": "OUTPUT",
      "datatype": "FP32",
      "shape": [
        "4"
      ],
      "contents": {
        "fp32Contents": [
          15,
          16,
          17,
          18
        ]
      }
    }
  ]
}

```

{% tabs %}
{% tab title="kubectl" %}

```bash
kubectl delete -f ../../models/choice1.yaml -n ${NAMESPACE}
kubectl delete -f ../../models/choice2.yaml -n ${NAMESPACE}
kubectl delete -f ../../models/add10.yaml -n ${NAMESPACE}
kubectl delete -f ../../models/mul10.yaml -n ${NAMESPACE}
kubectl delete -f ../../pipelines/choice.yaml -n ${NAMESPACE}
```

{% endtab %}

{% tab title="seldon-cli" %}

```bash
seldon model unload choice-is-one
seldon model unload choice-is-two
seldon model unload add10
seldon model unload mul10
seldon pipeline unload choice
```

{% endtab %}
{% endtabs %}


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.seldon.ai/seldon-core-2/v2.9/user-guide/examples/pandasquery.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.