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import os
import json
import torch
from PIL import Image
from torchvision import transforms
import matplotlib.pyplot as plt
from model import efficientnetv2_m as create_model
def predict(img):
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
img_size = {"s": [300, 384], # train_size, val_size
"m": [384, 480],
"l": [384, 480]}
num_model = "m"
data_transform = transforms.Compose(
[transforms.Resize(img_size[num_model][1]),
transforms.CenterCrop(img_size[num_model][1]),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
img = data_transform(img)
# expand batch dimension
img = torch.unsqueeze(img, dim=0)
json_path = './class_indices.json'
json_file = open(json_path, "r")
class_indict = json.load(json_file)
model = create_model(num_classes=5).to(device)
model_weight_path = "./weights/model-20.pth"
model.load_state_dict(torch.load(model_weight_path, map_location=device))
model.eval()
with torch.no_grad():
# predict class
output = torch.squeeze(model(img.to(device))).cpu()
predict = torch.softmax(output, dim=0)
predict_cla = torch.argmax(predict).numpy()
print_res = "class: {} \n prob: {:.3}".format(class_indict[str(predict_cla)],
predict[predict_cla].numpy())
return print_res
import gradio as gr
examples = ['d.jpg', 'rose.jpg', 'rose2.jpg', 'images.jpg']
inter = gr.Interface(fn=predict,
inputs=gr.inputs.Image(type="pil"),
outputs=gr.outputs.Label(num_top_classes=5),
title = 'Five types of flower Detection',
description= 'This program can be used to detect five types of flowers: "daisy", "dandelion", "roses", "sunflowers", "tulips", and the program will give the classification results along with a confidence score.', theme = 'huggingface')
inter.launch(inline=False,debug=True)
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