Adds gradio app

app.py

@@ -0,0 +1,50 @@
+import torch
+import gradio as gr
+from src.model import DRModel
+from torchvision import transforms as T
+
+CHECKPOINT_PATH = "checkpoints/epoch=19-step=8800.ckpt"
+model = DRModel.load_from_checkpoint(CHECKPOINT_PATH)
+
+labels = {
+    0: "No DR",
+    1: "Mild",
+    2: "Moderate",
+    3: "Severe",
+    4: "Proliferative DR",
+}
+
+transform = T.Compose(
+    [
+        T.Resize((192, 192)),
+        T.ToTensor(),
+        T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+    ]
+)
+
+# Define the prediction function
+def predict(input_img):
+    input_img = transform(input_img).unsqueeze(0)
+    with torch.no_grad():
+        prediction = torch.nn.functional.softmax(model(input_img)[0], dim=0)
+        confidences = {labels[i]: float(prediction[i]) for i in labels}
+    return confidences
+
+
+# Set up the Gradio app interface
+dr_app = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(),
+    title="Diabetic Retinopathy Detection",
+    examples=[
+        "data/sample/10_left.jpeg",
+        "data/sample/10_right.jpeg",
+        "data/sample/15_left.jpeg",
+        "data/sample/16_right.jpeg",
+    ],
+)
+
+# Run the Gradio app
+if __name__ == "__main__":
+    dr_app.launch()