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import gradio as gr | |
import torch | |
import requests | |
from torchvision import transforms | |
torch.hub._validate_not_a_forked_repo=lambda a,b,c: True | |
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") | |
INITIAL_PORT_VALUE = int(os.getenv("GRADIO_SERVER_PORT", "7860")) | |
TRY_NUM_PORTS = int(os.getenv("GRADIO_NUM_PORTS", "100")) | |
LOCALHOST_NAME = os.getenv("GRADIO_SERVER_NAME", "127.0.0.1") | |
GRADIO_API_SERVER = "https://api.gradio.app/v1/tunnel-request" | |
def get_first_available_port(initial: int, final: int) -> int: | |
""" | |
Gets the first open port in a specified range of port numbers | |
Parameters: | |
initial: the initial value in the range of port numbers | |
final: final (exclusive) value in the range of port numbers, should be greater than `initial` | |
Returns: | |
port: the first open port in the range | |
""" | |
for port in range(initial, final): | |
try: | |
s = socket.socket() # create a socket object | |
s.bind((LOCALHOST_NAME, port)) # Bind to the port | |
s.close() | |
return port | |
except OSError: | |
pass | |
raise OSError( | |
"All ports from {} to {} are in use. Please close a port.".format( | |
initial, final | |
) | |
) | |
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=LOCALHOST_NAME, server_port=get_first_available_port( | |
INITIAL_PORT_VALUE, INITIAL_PORT_VALUE + TRY_NUM_PORTS | |
) | |
#demo.launch(server_name="0.0.0.0", server_port=7861) | |
if __name__ == "__main__": | |
run() | |