Create app.py

app.py

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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 = "s"
+
+    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: {}   prob: {:.3}".format(class_indict[str(predict_cla)],
+                                                 predict[predict_cla].numpy())
+    return print_res
+
+import gradio as gr
+
+gr.Interface(fn=predict, 
+             inputs=gr.inputs.Image(type="pil"),
+             outputs=gr.outputs.Label(num_top_classes=5),
+             theme="default").launch()