import gradio as gr import torch import requests import socket from torchvision import transforms import os 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()