# CSV Inference Example

Process CSV data with a pre-trained model using Valohai's execution system. This example uses TensorFlow 2.5.0 to run predictions on tabular data.

Batch inference runs as a standard Valohai execution, which means you can schedule it, trigger it via API, or chain it in pipelines.

### What you'll need

Two files are available in public storage:

* **Model:** `s3://valohai-public-files/tutorials/batch-inference/csv-batch-inference/model.zip`
* **Data:** `s3://valohai-public-files/tutorials/batch-inference/csv-batch-inference/data.csv`

You don't need to download these manually. Valohai will fetch them when the job runs.

### Inference code

This script loads a zipped model, processes CSV data, and outputs predictions as JSON metadata and a results file.

```python
import json
from zipfile import ZipFile

import pandas as pd
import tensorflow as tf

# Extract and load the model from Valohai inputs
with ZipFile("/valohai/inputs/model/model.zip", "r") as f:
    f.extractall()

model = tf.keras.models.load_model("model")

# Load CSV data from Valohai inputs
csv = pd.read_csv("/valohai/inputs/data/data.csv")
labels = csv.pop("target")
data = tf.data.Dataset.from_tensor_slices((dict(csv), labels))
batch_data = data.batch(batch_size=32)

# Run predictions
results = model.predict(batch_data)

# Build results dictionary: {"1": 0.375, "2": 0.76}
flattened_results = results.flatten()
indexed_results = enumerate(flattened_results, start=1)
metadata = dict(indexed_results)

# Print each result as Valohai metadata for tracking
for value in metadata.values():
    print(json.dumps({"result": str(value)}))

# Save results to Valohai outputs
with open("/valohai/outputs/results.json", "w") as f:
    # NumPy float32 values need stringification for JSON
    json.dump(metadata, f, default=lambda v: str(v))
```

**Key Valohai paths:**

* `/valohai/inputs/model/` - Where input files land
* `/valohai/outputs/` - Where output files are saved and versioned

### Define the step

Add this to your `valohai.yaml`:

```yaml
- step:
    name: csv-inference
    image: tensorflow/tensorflow:2.5.0
    command:
      - pip install pandas
      - python batch_inference_csv.py
    inputs:
      - name: model
        default: s3://valohai-public-files/tutorials/batch-inference/csv-batch-inference/model.zip
      - name: data
        default: s3://valohai-public-files/tutorials/batch-inference/csv-batch-inference/data.csv
```

**Why these inputs work:**

* `default` provides a starting point, but you can override with any S3, GCS, or Azure URL
* Input names (`model`, `data`) map to `/valohai/inputs/{name}/` directories

### Run the inference

Execute from your terminal:

```shell
vh execution run csv-inference --adhoc --watch
```

This streams logs to your terminal in real-time.

### Check your results

Once complete, find your outputs in the Outputs tab of the execution. You'll see:

* `results.json` with all predictions
* Execution metadata showing individual prediction values

**Next:** Learn how to [schedule recurring inference runs](https://github.com/valohai/dokuhai/blob/main/pipelines/trigger-pipeline.md) or [trigger via API](https://github.com/valohai/dokuhai/blob/main/api/trigger-execution.md).


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