main.py

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, *args, **kwargs):
    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 calculate(*args, **kwargs) -> str:
    output_file_path = "main_output.txt"
    with open(output_file_path, "w") as fi:
        fi.write(f"args: {args}\n")
        fi.write(f"kwargs: {kwargs}\n")
    return output_file_path


def run():
    iface = gr.Interface(
        fn=calculate,
        inputs=[
            gr.File(label="Protein PDB", file_types=[".pdb"]),
            gr.File(label="Ligand SDF", file_types=[".sdf"]),
            gr.Number(label="Samples Per Complex", value=4, minimum=1, maximum=100, precision=0),
            gr.Checkbox(label="Keep Local Structures", value=True),
            gr.Checkbox(label="Save Visualization", value=True)
        ],
        outputs=gr.File(label="Result")
    )

    iface.launch(server_name="0.0.0.0", server_port=7860)


if __name__ == "__main__":
    run()