Deploying Gradio on Ubuntu 22.04

Gradio is a Python library for wrapping any ML model in a web interface, ready to deploy and scale as an app. This guide builds a GFPGAN-powered face-restoration demo with Gradio on Ubuntu 22.04, runs it as a systemd service, and exposes it through Nginx with TLS. Prerequisites: a GPU-enabled Ubuntu 22.04 server, a domain A record (e.g. gradio.example.com ), non-root sudo access, Nginx installed. Set Up the Server 1. Install dependencies: $ pip3 install realesrgan gfpgan basicsr gradio realesrgan — background restoration gfpgan — face restoration basicsr — provides RRDBNet , the super-resolution architecture GFPGAN relies on gradio — the web interface 2. GFPGAN's pandas dependency needs jinja2 >= 3.1.2: $ pip show jinja2 Upgrade if it's older: $ pip install --upgrade jinja2 3. Create the project directory: $ sudo mkdir -p /opt/gradio-webapp/ $ sudo chown -R : $( id -gn ) /opt/gradio-webapp/ $ sudo chmod -R 775 /opt/gradio-webapp/ Build the Gradio App Uploads a face image and returns two enhanced outputs. $ cd /opt/gradio-webapp/ $ nano app.py import gradio as gr from gfpgan import GFPGANer from basicsr.archs.rrdbnet_arch import RRDBNet from realesrgan import RealESRGANer import numpy as np import cv2 import requests def enhance_image ( input_image ): arch = ' clean ' model_name = ' GFPGANv1.4 ' gfpgan_checkpoint = ' https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth ' realersgan_checkpoint = ' https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth ' rrdbnet = RRDBNet ( num_in_ch = 3 , num_out_ch = 3 , num_feat = 64 , num_block = 23 , num_grow_ch = 32 , scale = 2 ) bg_upsampler = RealESRGANer ( scale = 2 , model_path = realersgan_checkpoint , model = rrdbnet , tile = 400 , tile_pad = 10 , pre_pad = 0 , half = True ) restorer = GFPGANer ( model_path = gfpgan_checkpoint , upscale = 2 , arch = arch , channel_multiplier = 2 , bg_upsampler = bg_upsampler ) input_image = input_image . astype ( np . uint8 ) cropped_fa