app.py

import torch
import requests
import gradio as gr

from PIL import Image
from torchvision import transforms

model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()
response = requests.get("https://git.io/JJkYN")
labels = response.text.split("\n")

title = "Image Classifier Two -- PyTorch Resnet-18"
description = """This machine has vision. It can see objects and concepts in an image. To test the machine, upload or drop an image, submit and read the results. The results comprise a list of words that the machine sees in the image. Beside a word, the length of the bar indicates the confidence with which the machine sees the word. The longer the bar, the more confident the machine is.
"""
article = "This app was made by following [this Gradio guide](https://gradio.app/image_classification_in_pytorch/)."


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
  
gr.Interface(fn=predict, 
             inputs = gr.inputs.Image(type="pil"), 
             outputs = gr.outputs.Label(num_top_classes=5),
             title = title, 
             description = description, 
             article = article).launch()