Google ColabでGUI｜gradioを利用したウェブアプリの作り方を解説

!pip install gradio

import gradio as gr

def test(text) :
  return "input text = " + text

app = gr.Interface(fn = test, inputs="text", outputs="text")

app.launch()

import gradio as gr

def greet(name, is_morning, temperature):
    salutation = "Good morning" if is_morning else "Good evening"
    greeting = f"{salutation} {name}. It is {temperature} degrees today"
    celsius = (temperature - 32) * 5 / 9
    return greeting, round(celsius, 2)

demo = gr.Interface(
    fn=greet,
    inputs=["text", "checkbox", gr.Slider(0, 100)],
    outputs=["text", "number"],
)
demo.launch()

import numpy as np
import gradio as gr

def sepia(input_img):
    sepia_filter = np.array([
        [0.393, 0.769, 0.189], 
        [0.349, 0.686, 0.168], 
        [0.272, 0.534, 0.131]
    ])
    sepia_img = input_img.dot(sepia_filter.T)
    sepia_img /= sepia_img.max()
    return sepia_img

demo = gr.Interface(sepia, gr.Image(), "image")
demo.launch()

import gradio as gr

scores = []

def track_score(score):
    scores.append(score)
    top_scores = sorted(scores, reverse=True)[:3]
    return top_scores

demo = gr.Interface(
    track_score, 
    gr.Number(label="Score"), 
    gr.JSON(label="Top Scores")
)
demo.launch()

import torch
model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()

import json
import requests

# ImageNetのクラスラベルを取得するURL
url = "https://raw.githubusercontent.com/anishathalye/imagenet-simple-labels/master/imagenet-simple-labels.json"

# URLからラベルを取得
response = requests.get(url)
labels = response.json()
labels[:10]

!wget -O cat2.png https://github.com/aruaru0/SAM-TEST/blob/main/cat2.png?raw=true 

from PIL import Image
from torchvision import transforms

def predict(inp):
  inp = transforms.ToTensor()(inp).unsqueeze(0)
  with torch.no_grad():
    prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
    confidences = {labels[i]: float(prediction[i]) for i in range(1000)}
  return confidences

img = Image.open('cat2.png')
conf = predict(img)
l = []
for e in conf:
  l.append((conf[e], e))
l.sort()
l[::-1][:5]

import gradio as gr

gr.Interface(fn=predict,
             inputs=gr.Image(type="pil"),
             outputs=gr.Label(num_top_classes=5),
             examples=["cat2.png"]).launch()