TillCyrill
container
a3a5fc1
raw
history blame
776 Bytes
import gradio as gr
import torch
import requests
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")
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
def run():
demo = gr.Interface(
fn=predict,
inputs=gr.inputs.Image(type="pil"),
outputs=gr.outputs.Label(num_top_classes=3),
)
demo.launch(server_name="0.0.0.0", server_port=7860)
if __name__ == "__main__":
run()