File size: 1,594 Bytes
caf6be1 70e5abd 98af2c3 d5f7570 70e5abd 8129089 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | import torch
model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()
import requests
from PIL import Image
from torchvision import transforms
# Download human-readable labels for ImageNet.
response = requests.get("https://git.io/JJkYN")
labels = response.text.split("\n")
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
import gradio as gr
with gr.Blocks(title="Image Classification for 1000 Objects", css=".gradio-container {background:mintcream;}") as demo:
gr.HTML("""<div style="font-family:'Times New Roman', 'Serif'; font-size:16pt; font-weight:bold; text-align:center; color:royalblue;">Image Classification for 1000 Objects</div>""")
with gr.Row():
input_image = gr.Image(type="filepath", image_mode="L")
output_label = gr.Label(label="Probabilities", num_top_classes=3)
send_btn = gr.Button("Infer")
send_btn.click(fn=predict, inputs=input_image, outputs=output_label)
with gr.Row():
gr.Examples(['./lion.jpg'] , label='Sample images : Lion', inputs=input_image)
gr.Examples(['./cheetah.jpg'], label='Cheetah' , inputs=input_image)
# gr.Interface(fn=predict,
# inputs=gr.Image(type="pil"),
# outputs=gr.Label(num_top_classes=3),
# examples=["lion.jpg", "cheetah.jpg"]).launch()
demo.launch(debug=True, share=True)
|