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import os
os.system("pip uninstall -y gradio")
os.system("pip install --upgrade gradio")
from pathlib import Path
from fastapi import FastAPI
from fastapi.staticfiles import StaticFiles
import uvicorn
import gradio as gr
from datetime import datetime
import sys
gr.set_static_paths(paths=["static/"])
# create a FastAPI app
app = FastAPI()
# create a static directory to store the static files
static_dir = Path('./static')
static_dir.mkdir(parents=True, exist_ok=True)
# mount FastAPI StaticFiles server
app.mount("/static", StaticFiles(directory=static_dir), name="static")
# Gradio stuff
import datamapplot
import numpy as np
import requests
import io
def predict(text_input):
file_name = f"{datetime.utcnow().strftime('%s')}.html"
file_path = static_dir / file_name
print(file_path)
base_url = "https://github.com/TutteInstitute/datamapplot"
data_map_file = requests.get(
f"{base_url}/raw/main/examples/arxiv_ml_data_map.npy"
)
arxivml_data_map = np.load(io.BytesIO(data_map_file.content))
arxivml_label_layers = []
for layer_num in range(5):
label_file = requests.get(
f"{base_url}/raw/interactive/examples/arxiv_ml_layer{layer_num}_cluster_labels.npy"
)
arxivml_label_layers.append(np.load(io.BytesIO(label_file.content), allow_pickle=True))
hover_data_file = requests.get(
f"{base_url}/raw/interactive/examples/arxiv_ml_hover_data.npy"
)
arxiv_hover_data = np.load(io.BytesIO(hover_data_file.content), allow_pickle=True)
plot = datamapplot.create_interactive_plot(
arxivml_data_map,
arxivml_label_layers[0],
arxivml_label_layers[2],
arxivml_label_layers[4],
hover_text = arxiv_hover_data,
font_family="Roboto Condensed",
)
plot.save(file_path)
iframe = f"""<iframe src="/static/{file_name}" width="100%" height="500px"></iframe>"""
link = f'<a href="/static/{file_name}" target="_blank">{file_name}</a>'
return link, iframe
with gr.Blocks() as block:
gr.Markdown("""
## Gradio + FastAPI + Static Server
This is a demo of how to use Gradio with FastAPI and a static server.
The Gradio app generates dynamic HTML files and stores them in a static directory. FastAPI serves the static files.
""")
with gr.Row():
with gr.Column():
text_input = gr.Textbox(label="Name")
markdown = gr.Markdown(label="Output Box")
new_btn = gr.Button("New")
with gr.Column():
html = gr.HTML(label="HTML preview", show_label=True)
new_btn.click(fn=predict, inputs=[text_input], outputs=[markdown, html])
# mount Gradio app to FastAPI app
app = gr.mount_gradio_app(app, block, path="/")
# serve the app
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=7860)
# run the app with
# python app.py
# or
# uvicorn "app:app" --host "0.0.0.0" --port 7860 --reload
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