app.py

import torchvision
import torch
from torch import nn
from PIL import Image
from torchvision import transforms
import onnxruntime
import numpy as np
import torch.nn.functional as F
from safetensors.torch import save_file,load_file,safe_open
import numpy as np

def predict(img_path,model=None):
    if model is None:
        pretrained_weights_resnet18=torchvision.models.ResNet18_Weights.DEFAULT
        model=torchvision.models.resnet18(weights=pretrained_weights_resnet18)
        class_names=pretrained_weights_resnet18.meta["categories"]
    transform=transforms.Compose([transforms.Resize((64,64)),transforms.ToTensor(),transforms.Normalize(mean=[0.485,0.456,0.406],std=[0.229,0.224,0.225])])
    if isinstance(img_path,np.ndarray):
        image=Image.fromarray(img_path).convert("RGB")
    else:
        image=Image.open(img_path).convert("RGB")
    img_transform=transform(image).unsqueeze(0)

    model.eval()
    with torch.inference_mode():
        logit=model(img_transform)
        pred_prob=torch.softmax(logit,dim=1).squeeze().numpy()
        predict_dict={}
        for i in range(len(class_names)):
            predict_dict[class_names[i]]=float(pred_prob[i])
        
    return predict_dict


import numpy as np
import gradio as gr

demo = gr.Interface(predict, gr.Image(), outputs=gr.Label(num_top_classes=3))
if __name__ == "__main__":
    demo.launch()