diff --git a/README.md b/README.md
index 255dee7..397e12e 100644
--- a/README.md
+++ b/README.md
@@ -3,6 +3,9 @@ Authors: [Yu-Siang Huang](https://remyhuang.github.io/), [Yi-Hsuan Yang](http://
 
 [**Paper (arXiv)**](https://arxiv.org/abs/2002.00212) | [**Blog**](https://ailabs.tw/human-interaction/pop-music-transformer/) | [**Audio demo (Google Drive)**](https://drive.google.com/open?id=1LzPBjHPip4S0CBOLquk5CNapvXSfys54) | [**Online interactive demo**](https://vibertthio.com/transformer/)
 
+## Gradio Demo
+[Gradio Web Demo](https://gradio.app/hub/AK391/remi)
+
 REMI, which stands for `REvamped MIDI-derived events`, is a new event representation we propose for converting MIDI scores into text-like discrete tokens.  Compared to the MIDI-like event representation adopted in exising Transformer-based music composition models, REMI provides sequence models a metrical context for modeling the rhythmic patterns of music. Using REMI as the event representation, we train a Transformer-XL model to generate minute-long Pop piano music with expressive, coherent and clear structure of rhythm and harmony, without needing any post-processing to refine the result. The model also provides controllability of local tempo changes and chord progression.
 
 ## Citation
diff --git a/gradiodemo.py b/gradiodemo.py
new file mode 100644
index 0000000..ba07eb9
--- /dev/null
+++ b/gradiodemo.py
@@ -0,0 +1,55 @@
+from model import PopMusicTransformer
+import os
+os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
+import tensorflow as tf
+tf.compat.v1.disable_eager_execution()
+import gradio as gr
+import requests
+import torchtext
+import zipfile
+
+torchtext.utils.download_from_url("https://drive.google.com/uc?id=1gxuTSkF51NP04JZgTE46Pg4KQsbHQKGo", root=".")
+torchtext.utils.download_from_url("https://drive.google.com/uc?id=1nAKjaeahlzpVAX0F9wjQEG_hL4UosSbo", root=".")
+
+with zipfile.ZipFile("REMI-tempo-checkpoint.zip","r") as zip_ref:
+    zip_ref.extractall(".")
+with zipfile.ZipFile("REMI-tempo-chord-checkpoint.zip","r") as zip_ref:
+    zip_ref.extractall(".")
+
+url = 'https://github.com/AK391/remi/blob/master/input.midi?raw=true'
+r = requests.get(url, allow_redirects=True)
+open("input.midi", 'wb').write(r.content)
+
+
+# declare model
+model = PopMusicTransformer(
+    checkpoint='REMI-tempo-checkpoint',
+    is_training=False)
+
+def inference(midi): 
+    # generate continuation
+    model.generate(
+        n_target_bar=4,
+        temperature=1.2,
+        topk=5,
+        output_path='./result/continuation.midi',
+        prompt=midi.name)
+    return './result/continuation.midi'
+        
+
+title = "Pop Music Transformer"
+description = "demo for Pop Music Transformer. To use it, simply upload your midi file, or click one of the examples to load them. Read more at the links below."
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2002.00212'>Pop Music Transformer: Beat-based Modeling and Generation of Expressive Pop Piano Compositions</a> | <a href='https://github.com/YatingMusic/remi'>Github Repo</a></p>"
+
+examples = [
+    ['input.midi']
+]
+gr.Interface(
+    inference, 
+    gr.inputs.File(label="Input Midi"), 
+    gr.outputs.File(label="Output Midi"),
+    title=title,
+    description=description,
+    article=article,
+    examples=examples
+    ).launch()
diff --git a/input.midi b/input.midi
new file mode 100644
index 0000000..096cab1
Binary files /dev/null and b/input.midi differ
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..a5a3b63
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,4 @@
+miditoolkit
+tensorflow-gpu==1.14.0
+gradio
+torchtext