add application files

app.py

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+import torch
+import torch.nn.functional as F
+import torchvision.transforms as transforms
+import numpy as np
+import gradio as gr
+from torch import nn
+from gradio import components
+from PIL import Image
+
+class BrainTumorClassifier(nn.Module):
+    def __init__(self, num_classes):
+        super(BrainTumorClassifier, self).__init__()
+        self.features = nn.Sequential(
+            nn.Conv2d(3, 20, kernel_size=3, padding=1),
+            nn.ReLU(),
+            nn.MaxPool2d(2, 2),
+            nn.Conv2d(20, 32, kernel_size=3, padding=1),
+            nn.ReLU(),
+            nn.MaxPool2d(2, 2)
+        )
+        self.classifier = nn.Sequential(
+            nn.Linear(32 * 56 * 56, 128),  # Adjust input size based on image size
+            nn.ReLU(),
+            nn.Linear(128, num_classes)
+        )
+
+    def forward(self, x):
+        x = self.features(x)
+        x = x.view(-1, 32 * 56 * 56)
+        x = self.classifier(x)
+        return x
+    
+def predict(image):
+    image = Image.fromarray(np.uint8(image)).convert('RGB')
+    ## give the weights trained
+    model_path = 'cnn_tumorbrain_classifier_self.pth'
+    model_load = BrainTumorClassifier(4)
+    model_load.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
+
+    ## put the model in evaluation mode
+    model_load.eval()
+
+    transform_pipeline = transforms.Compose([
+        transforms.Resize((224,224)),
+        transforms.ToTensor(),
+        transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))
+    ])
+
+    ## transform the img like the training image
+    input_img = transform_pipeline(image).unsqueeze(0)
+    # input_img
+
+    ## define the label by index
+    class_to_label = {0: 'glioma', 1: 'meningioma', 2: 'notumor', 3: 'pituitary'}
+
+    ## run the model
+    with torch.no_grad():
+        output = model_load(input_img)
+
+    ## convert to the softmax for getting percent each label
+    probabilities = F.softmax(output, dim=1)
+
+    ## get predicted label with highest value
+    _, predicted_label = torch.max(probabilities,1)
+    # confidence_percent = probabilities[0].tolist()[predicted_label.item()]
+    conf, _ = torch.max(probabilities, 1)
+
+    result = "{}, with confidence level in {}%".format(class_to_label[predicted_label.item()], conf.item()*100)
+    return result
+
+iface = gr.Interface(fn=predict,
+                        inputs=gr.Image(),
+                        outputs="textbox")
+
+iface.launch(share=True)

requirements.txt

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+torch
+torchvision
+numpy