Update app.py

app.py

@@ -1,23 +1,12 @@
+import numpy as np
 import gradio as gr
 import tensorflow as tf
-import numpy as np
 
-models = [
-    {"name": "my_model_2.h5", "size": 512},
-    {"name": "my_model.h5", "size": 224},
-]
-def preprocess_image(image):
-    temp_file = tempfile.NamedTemporaryFile(suffix=".png", delete=False)
-    temp_file.write(image.read())
-    temp_file.close()
-    return temp_file.name
+models = [{"name": "my_model_2.h5", "size": 512}, {"name": "my_model.h5", "size": 224}]
 
-def classify_image(image_path, model_name):
+def classify_image(image, model_name):
     model_config = next(m for m in models if m["name"] == model_name)
     model = tf.keras.models.load_model(model_name)
-    image = Image.open(image_path).convert("RGB")
-    image = image.resize((model_config["size"], model_config["size"]))
-    image = np.array(image) / 255.0
     input_image = np.expand_dims(image, axis=0)
     prediction = model.predict(input_image).flatten()
     if len(prediction) > 1:
@@ -36,7 +25,6 @@ inputs = [
     gr.inputs.Image(shape=(224, 224), 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)"),
@@ -44,7 +32,7 @@ outputs = [
 
 examples = [
     [np.zeros((224, 224, 3)), "my_model.h5"],
-    [np.ones((224, 224, 3)) * 255, "my_model_2.h5"],
+    [np.ones((224, 224, 3)) * 255, "my_model_2.h5"]
 ]
 
-gr.Interface(classify_image, inputs, outputs, examples=examples).launch()
+gr.Interface(classify_image, inputs, outputs, examples=examples, title="Glaucoma Classification").launch()