Update app.py

app.py

@@ -1,44 +1,33 @@
-import math
-
+import numpy as np
 import gradio as gr
 import tensorflow as tf
 
-configs = [
-    {
-        "model": "my_model_2.h5", "size": 512
-    },
-    {
-        "model": "my_model.h5", "size": 224
-    },
-]
-
-config = configs[0]
-
-new_model = tf.keras.models.load_model(config["model"])
+models = [    {"name": "my_model_2.h5", "size": 512},    {"name": "my_model.h5", "size": 224},]
 
-def classify_image(inp):
-    inp = inp.reshape((-1, config["size"], config["size"], 3))
-    prediction = new_model.predict(inp).flatten()
-    print(prediction)
+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)
+    input_image = np.expand_dims(image, axis=0)
+    prediction = model.predict(input_image).flatten()
     if len(prediction) > 1:
-        probability = 100 * math.exp(prediction[0]) / (math.exp(prediction[0]) + math.exp(prediction[1]))
+        probability = 100 * np.exp(prediction[0]) / (np.exp(prediction[0]) + np.exp(prediction[1]))
     else:
-        probability = round(100. / (1 + math.exp(-prediction[0])), 2)
+        probability = round(100. / (1 + np.exp(-prediction[0])), 2)
     if probability > 45:
-        return "Glaucoma", probability
-    if probability > 25:
-        return "Unclear", probability
-    return "Not glaucoma", probability
+        label = "Glaucoma"
+    elif probability > 25:
+        label = "Unclear"
+    else:
+        label = "Not glaucoma"
+    return label, probability
 
+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)"),
+]
 
-gr.Interface(
-    fn=classify_image, 
-    inputs=gr.inputs.Image(shape=(config["size"], config["size"])),
-    outputs=[
-    gr.outputs.Textbox(label="Label"),
-    gr.outputs.Textbox(label="Glaucoma probability (0 - 100)"),
-    ],
-    examples=["001.jpg", "002.jpg", "225.jpg"],
-    flagging_options=["Correct label", "Incorrect label"],
-    allow_flagging="manual",
-).launch()
+gr.Interface(classify_image, inputs, outputs, examples=["001.jpg", "002.jpg", "225.jpg"]).launch()