# Build a demo with Gradio

In this final section on audio classification, we'll build a [Gradio](https://gradio.app) demo to showcase the music
classification model that we just trained on the [GTZAN](https://huggingface.co/datasets/marsyas/gtzan) dataset. The first
thing to do is load up the fine-tuned checkpoint using the `pipeline()` class - this is very familiar now from the section
on [pre-trained models](classification_models). You can change the `model_id` to the namespace of your fine-tuned model
on the Hugging Face Hub:

```python
from transformers import pipeline

model_id = "sanchit-gandhi/distilhubert-finetuned-gtzan"
pipe = pipeline("audio-classification", model=model_id)
```

Secondly, we'll define a function that takes the filepath for an audio input and passes it through the pipeline. Here,
the pipeline automatically takes care of loading the audio file, resampling it to the correct sampling rate, and running
inference with the model. We take the models predictions of `preds` and format them as a dictionary object to be displayed on the
output:

```python
def classify_audio(filepath):
    preds = pipe(filepath)
    outputs = {}
    for p in preds:
        outputs[p["label"]] = p["score"]
    return outputs
```

Finally, we launch the Gradio demo using the function we've just defined:

```python
import gradio as gr

demo = gr.Interface(
    fn=classify_audio, inputs=gr.Audio(type="filepath"), outputs=gr.outputs.Label()
)
demo.launch(debug=True)
```

This will launch a Gradio demo similar to the one running on the Hugging Face Space:

