fix upload problem (#1)

- fix upload problem (70af086601b9d64eb9887378716cefc3e6ccad06)

app.py

@@ -1,17 +1,12 @@
 import gradio as gr
 from fastai.vision.all import *
-import skimage
-
-# Define the functions to get the x and y values from the input dictionary - in this case, the x value is the image and the y value is the diagnosis
-# needed to load the model since we defined them during training
-def get_x(r): return ""
-        
-def get_y(r): return r['diagnosis']
+import os
 
+# Load model
 learn = load_learner('model.pkl')
 labels = learn.dls.vocab
 
-# Define the mapping from label numbers to descriptions
+# Define label descriptions
 label_descriptions = {
     0: "No DR",
     1: "Mild",
@@ -20,41 +15,27 @@ label_descriptions = {
     4: "Proliferative DR"
 }
 
+# Prediction function
 def predict(img):
     img = PILImage.create(img)
-    pred,pred_idx,probs = learn.predict(img)
-    # Use the label_descriptions dictionary to return descriptions instead of numbers
+    pred, pred_idx, probs = learn.predict(img)
     return {label_descriptions[labels[i]]: float(probs[i]) for i in range(len(labels))}
 
+# Gradio Interface
 title = "Diabetic Retinopathy Detection"
-description = """Detects severity of diabetic retinopathy from a given retina image taken using fundus photography -
-
-    0 - No DR
-
-    1 - Mild
-
-    2 - Moderate
-
-    3 - Severe
+description = """Detects severity of diabetic retinopathy from a given retina image."""
+article = "<p style='text-align: center'>Example article or instructions here</p>"
 
-    4 - Proliferative DR
-"""
-article = """
-<p style='text-align: center'>
-    <a href='https://www.kaggle.com/code/josemauriciodelgado/proliferative-retinopathy' target='_blank'>Kaggle Training Notebook</a> | 
-    <a href='https://huggingface.co/jdelgado2002/diabetic_retinopathy_detection' target='_blank'>Model Card</a>
-</p>
-"""
-# Get a list of all image paths in the test folder
-test_folder = "test"  # replace with the actual path to your test folder
+# Prepare examples if available
+test_folder = "test"
 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(),
+    inputs=gr.Image(type="filepath"),
     outputs=gr.Label(num_top_classes=5),
-    examples=image_paths,  # set the examples parameter to the list of image paths
+    examples=image_paths,
     article=article,
     title=title,
     description=description,
-).launch()
+).launch()