Update app.py

app.py

@@ -1,10 +1,11 @@
-import numpy as np
-import gradio as gr
-import tensorflow as tf
-
-models = [    {"name": "my_model_2.h5", "size": 512},    {"name": "my_model.h5", "size": 224},]
-
 from PIL import Image
+import tempfile
+
+def preprocess_image(image):
+    temp_file = tempfile.NamedTemporaryFile(suffix=".png", delete=False)
+    temp_file.write(image.read())
+    temp_file.close()
+    return temp_file.name
 
 def classify_image(image_path, model_name):
     model_config = next(m for m in models if m["name"] == model_name)
@@ -26,19 +27,19 @@ def classify_image(image_path, model_name):
         label = "Not glaucoma"
     return label, probability
 
-
 inputs = [
-    gr.inputs.Image(shape=(224, 224), label="Eye image"),
+    gr.inputs.Image(label="Eye image"),
     gr.inputs.Dropdown(choices=[m["name"] for m in models], label="Model"),
 ]
+
 outputs = [
     gr.outputs.Textbox(label="Predicted label"),
     gr.outputs.Textbox(label="Probability of glaucoma (0-100)"),
 ]
 
 examples = [
-    [np.zeros((224, 224, 3)), "my_model.h5"],
-    [np.ones((224, 224, 3)) * 255, "my_model_2.h5"]
+    [preprocess_image(open("example_image.jpg", "rb")), "my_model.h5"],
+    [preprocess_image(open("example_image_2.jpg", "rb")), "my_model_2.h5"]
 ]
 
 gr.Interface(classify_image, inputs, outputs, examples=examples).launch()