Update app.py

app.py

@@ -1,55 +1,55 @@
-import torch
-import gradio as gr
-from src.model import DRModel
-from torchvision import transforms as T
-
-CHECKPOINT_PATH = "artifacts/dr-model.ckpt"
-model = DRModel.load_from_checkpoint(CHECKPOINT_PATH, map_location="cpu")
-model.eval()
-
-labels = {
-    0: "No DR",
-    1: "Mild",
-    2: "Moderate",
-    3: "Severe",
-    4: "Proliferative DR",
-}
-
-transform = T.Compose(
-    [
-        T.Resize((224, 224)),
-        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 App",
-    description="Welcome to our Diabetic Retinopathy Detection App! \
-        This app utilizes deep learning models to detect diabetic retinopathy in retinal images.\
-        Diabetic retinopathy is a common complication of diabetes and early detection is crucial for effective treatment.",
-    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()
+import torch
+import gradio as gr
+from src.model import DRModel
+from torchvision import transforms as T
+
+CHECKPOINT_PATH = "dr-model.ckpt"
+model = DRModel.load_from_checkpoint(CHECKPOINT_PATH, map_location="cpu")
+model.eval()
+
+labels = {
+    0: "No DR",
+    1: "Mild",
+    2: "Moderate",
+    3: "Severe",
+    4: "Proliferative DR",
+}
+
+transform = T.Compose(
+    [
+        T.Resize((224, 224)),
+        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 App",
+    description="Welcome to our Diabetic Retinopathy Detection App! \
+        This app utilizes deep learning models to detect diabetic retinopathy in retinal images.\
+        Diabetic retinopathy is a common complication of diabetes and early detection is crucial for effective treatment.",
+    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()