Update app.py

app.py

@@ -12,37 +12,36 @@ configs = [
 ]
 
 config = configs[0]
-
 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()
+def classificar_imagem(entrada):
+    entrada = entrada.reshape((-1, config["size"], config["size"], 3))
+    prediction = new_model.predict(entrada).flatten()
 
     if len(prediction) > 1:
-        probability = 100 * math.exp(prediction[0]) / (math.exp(prediction[0]) + math.exp(prediction[1]))
+        probabilidade = 100 * math.exp(prediction[0]) / (math.exp(prediction[0]) + math.exp(prediction[1]))
     else:
-        probability = round(100. / (1 + math.exp(-prediction[0])), 2)
+        probabilidade = round(100. / (1 + math.exp(-prediction[0])), 2)
 
-    if probability > 45:
+    if probabilidade > 45:
         label = "Glaucoma"
-    elif probability > 25:
-        label = "Unclear"
+    elif probabilidade > 25:
+        label = "Incerto"
     else:
-        label = "Not glaucoma"
+        label = "Não glaucoma"
 
-    return {"Label": label, "Glaucoma probability (0 - 100)": probability}
+    return {"Rótulo": label, "Probabilidade de glaucoma (0 - 100)": probabilidade}
 
-iface = gr.Interface(
-    fn=classify_image, 
+interface = gr.Interface(
+    fn=classificar_imagem, 
     inputs=gr.inputs.Image(shape=(config["size"], config["size"])),
     outputs=[
-        gr.outputs.Textbox(label="Label"),
-        gr.outputs.Textbox(label="Glaucoma probability (0 - 100)")
+        gr.outputs.Textbox(label="Rótulo"),
+        gr.outputs.Textbox(label="Probabilidade de glaucoma (0 - 100)")
     ],
     examples=["001.jpg", "002.jpg", "225.jpg"],
-    flagging_options=["Correct label", "Incorrect label"],
+    flagging_options=["Rótulo correto", "Rótulo incorreto"],
     allow_flagging="manual"
 )
 
-iface.launch()
+interface.launch()