Update app.py

app.py

@@ -1,4 +1,5 @@
 import math
+
 import gradio as gr
 import tensorflow as tf
 
@@ -12,36 +13,32 @@ configs = [
 ]
 
 config = configs[0]
-new_model = tf.keras.models.load_model(config["model"])
 
-def classificar_imagem(entrada):
-    entrada = entrada.reshape((-1, config["size"], config["size"], 3))
-    prediction = new_model.predict(entrada).flatten()
+new_model = tf.keras.models.load_model(config["model"])
 
+def classify_image(inp):
+    inp = inp.reshape((-1, config["size"], config["size"], 3))
+    prediction = new_model.predict(inp).flatten()
+    print(prediction)
     if len(prediction) > 1:
-        probabilidade = 100 * math.exp(prediction[0]) / (math.exp(prediction[0]) + math.exp(prediction[1]))
+        probability = 100 * math.exp(prediction[0]) / (math.exp(prediction[0]) + math.exp(prediction[1]))
     else:
-        probabilidade = round(100. / (1 + math.exp(-prediction[0])), 2)
+        probability = round(100. / (1 + math.exp(-prediction[0])), 2)
+    if probability > 45:
+        return "Glaucoma", probability
+    if probability > 25:
+        return "Unclear", probability
+    return "Not glaucoma", probability
 
-    if probabilidade > 45:
-        label = "Glaucoma"
-    elif probabilidade > 25:
-        label = "Incerto"
-    else:
-        label = "Não glaucoma"
-
-    return {"Rótulo": label, "Probabilidade de glaucoma (0 - 100)": probabilidade}
 
-interface = gr.Interface(
-    fn=classificar_imagem, 
+gr.Interface(
+    fn=classify_image, 
     inputs=gr.inputs.Image(shape=(config["size"], config["size"])),
     outputs=[
-        gr.outputs.Textbox(label="Rótulo"),
-        gr.outputs.Textbox(label="Probabilidade de glaucoma (0 - 100)")
+    gr.outputs.Textbox(label="Label"),
+    gr.outputs.Textbox(label="Glaucoma probability (0 - 100)"),
     ],
     examples=["001.jpg", "002.jpg", "225.jpg"],
-    flagging_options=["Rótulo correto", "Rótulo incorreto"],
-    allow_flagging="manual"
-)
-
-interface.launch()
+    flagging_options=["Correct label", "Incorrect label"],
+    allow_flagging="manual",
+).launch()