test images, metadata

app.py

@@ -2,17 +2,27 @@ import gradio as gr
 from fastai.vision.all import *
 import skimage
 
-def get_x(r): return "/kaggle/input/aptos2019/train_images/train_images/1ae8c165fd53.png"
+def get_x(r): return ""
         
 def get_y(r): return r['diagnosis']
 
 learn = load_learner('model.pkl')
 labels = learn.dls.vocab
 
+# Define the mapping from label numbers to descriptions
+label_descriptions = {
+    0: "No DR",
+    1: "Mild",
+    2: "Moderate",
+    3: "Severe",
+    4: "Proliferative DR"
+}
+
 def predict(img):
     img = PILImage.create(img)
     pred,pred_idx,probs = learn.predict(img)
-    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+    # Use the label_descriptions dictionary to return descriptions instead of numbers
+    return {label_descriptions[labels[i]]: float(probs[i]) for i in range(len(labels))}
 
 title = "Proliferative Retinopathy Detection"
 description = """Detects severity of diabetic retinopathy -
@@ -28,11 +38,17 @@ description = """Detects severity of diabetic retinopathy -
     4 - Proliferative DR
 """
 article="<p style='text-align: center'><a href='https://www.kaggle.com/code/josemauriciodelgado/proliferative-retinopathy' target='_blank'>Notebook</a></p>"
-interpretation='default'
-enable_queue=True
+
+# Get a list of all image paths in the test folder
+test_folder = "test"  # replace with the actual path to your test folder
+image_paths = [os.path.join(test_folder, img) for img in os.listdir(test_folder) if img.endswith(('.png', '.jpg', '.jpeg'))]
 
 gr.Interface(
     fn=predict,
     inputs=gr.Image(),
     outputs=gr.Label(num_top_classes=5),
+    examples=image_paths,  # set the examples parameter to the list of image paths
+    article=article,
+    title=title,
+    description=description,
 ).launch()