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()