feat: Add Gradio interface and integrate trained CNN model

app.py

@@ -1,7 +1,62 @@
+import torch
+from torchvision import transforms
+from PIL import Image
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Define the CNN model architecture
+class CNNModel(torch.nn.Module):
+    def __init__(self):
+        super(CNNModel, self).__init__()
+        self.conv1 = torch.nn.Conv2d(3, 12, kernel_size=5, padding=2)
+        self.pool = torch.nn.MaxPool2d(2, 2)
+        self.conv2 = torch.nn.Conv2d(12, 8, kernel_size=5, padding=2)
+        self.conv3 = torch.nn.Conv2d(8, 4, kernel_size=5, padding=2)
+        self._to_linear = None
+        self.convs(torch.randn(1, 3, 224, 224))
+        self.fc1 = torch.nn.Linear(self._to_linear, 1)
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+    def convs(self, x):
+        x = self.pool(F.relu(self.conv1(x)))
+        x = self.pool(F.relu(self.conv2(x)))
+        x = self.pool(F.relu(self.conv3(x)))
+        if self._to_linear is None:
+            self._to_linear = x.view(-1).size(0)
+        return x
+
+    def forward(self, x):
+        x = self.convs(x)
+        x = x.view(-1, self._to_linear)
+        x = self.fc1(x)
+        return x
+
+# Load the model
+model = CNNModel()
+model.load_state_dict(torch.load('pcos_cnn_model.pth', map_location=torch.device('cpu')))
+model.eval()
+
+# Define the image transforms
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+])
+
+# Define the prediction function
+def predict(image):
+    image = transform(image).unsqueeze(0)
+    output = model(image)
+    prediction = torch.sigmoid(output).item()
+    return "Infected" if prediction == 0 else "Not Infected"
+
+# Define the Gradio interface
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.inputs.Image(type="pil"),
+    outputs="text",
+    title="PCOS Diagnosis",
+    description="Upload an ultrasound image to predict if it is infected or not."
+)
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    interface.launch()

pcos_cnn_model.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:00c4fc9da38e203d0155b672ec7e82c961fa1ccc64a73b1493efa4c73f9f6b1b
+size 32276