app.py

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