app.py

import gradio as gr
import numpy as np
import torch
from model import Net


model = Net(100, 50, 10)
model.load_state_dict(torch.load('model.pth'))
model.eval()

def infer(file_obj):
    results = ""
    for file in file_obj:
        data = np.load(file.name)
        output = model(torch.from_numpy(data).float()).detach().numpy()
        results += np.array_str(output) + "\n"
    return results

with gr.Blocks("Test") as demo:
    gr.Markdown("## Test")
    output = gr.Textbox(label="Output")
    data = gr.UploadButton(
        label="Upload a .npy",
        file_count="multiple", 
        file_types=[".npy"])
    data.upload(fn=infer, inputs=data, outputs=output)

if __name__ == "__main__":
    demo.launch()