Create app.py

app.py

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+import cv2
+import torch
+import gradio as gr
+import skimage
+from model import FullModel
+from albumentations.pytorch import ToTensorV2
+from albumentations import *
+PATH = r"C:\Users\huumi\Desktop\Subjects\HK8\DOAN_THUCTAP\BreastCancer\July_convnext_labelsmoothing=0.1-benign-0-224_labelsmoothing_300rsna_b16.pt"
+
+
+transform = Compose([Resize(height=224,width=224,always_apply=True),
+                    ToTensorV2(),
+                    ])
+
+def load_model(PATH):
+    model = FullModel()
+    PATH = PATH
+    model.load_state_dict(torch.load(PATH))
+    return model
+
+model = load_model(PATH)
+
+def read_img(img):
+    # print(path_img.shape)
+    # img = cv2.imread(path_img, 1)
+    img_trans = transform(image=img)['image']
+    img_trans = img_trans.unsqueeze(0).float()
+    return img_trans
+    # print(img_trans.unsqueeze(0).shape)
+    # print(model(img_trans.unsqueeze(0).float()))
+
+def predict(img):
+    result = "Malignant"
+    images = read_img(img)
+    print(images.shape)
+    # images = images.reshape((-1, 3, 224, 224))
+    # images = torch.from_numpy(images)#.permute(0, 2, 3, 1)
+    outputs= model(images)
+    print(outputs)
+    _, predicted = torch.max(outputs, 1)
+    if(predicted.item() == 0):
+        result = "Normal/Benign"
+        
+    return result
+
+
+title = "Breast cancer detection with Deep Learning (ConvNext)"
+description = "<p style='text-align: center'><b>As a radiologist or oncologist, it is crucial to know what is wrong with a breast x-ray image.<b><br><b>Upload the breast X-ray image to know what is wrong with a patients breast with or without inplant<b><p>"
+article="<p style='text-align: center'>Web app is built and managed by IUH team></p>"
+examples = [r'C:\Users\huumi\Desktop\Subjects\HK8\DOAN_THUCTAP\BreastCancer\D1-0001_1-1.png', 
+            r'C:\Users\huumi\Desktop\Subjects\HK8\DOAN_THUCTAP\BreastCancer\D1-0005_1-2.png',
+            r'C:\Users\huumi\Desktop\Subjects\HK8\DOAN_THUCTAP\BreastCancer\mdb028.png']
+enable_queue=True
+#interpretation='default'
+
+gr.Interface(fn=predict,
+             inputs=gr.Image(label="Image (png file)"),
+             outputs='text',
+             title=title,
+             description=description,
+             article=article,
+             examples=examples,
+             enable_queue=enable_queue
+             ).launch(share=True)