app.py

import gradio as gr
import torch
from torch import nn
from torch.nn import functional as F
import torchvision
from torchvision import transforms

model= torch.jit.load('model_scripted108.pt')
data_transform1=transforms.Compose([
    transforms.Resize((224,224)),
    transforms.ToTensor(),
    transforms.Normalize((0.485,0.456,0.406),(0.229,0.224,0.225))
])

title = " Flower Image Classifier"

examples=[['pexels-photo-5008663.jpeg'],['sunflower-5171348__340.webp'],['pexels-photo-7928375.jpeg']]

classes=['Hibiscus','Lotus','Other','Rose','Sunflower']
def predict(img):
  imag=data_transform1(img)
  inp =imag.unsqueeze(0)
  outputs=model(inp)
  pred=F.softmax(outputs[0], dim=0).cpu().data.numpy()
  confidences = {classes[i]:(float(pred[i])) for i in range(5)}    
  return confidences

gr.Interface(predict,gr.inputs.Image(type='pil'),title=title,examples=examples,outputs='label').launch(debug=True)