Update app.py

app.py

@@ -2,6 +2,7 @@ import tensorflow as tf
 import efficientnet.tfkeras as efn
 import numpy as np
 import gradio as gr
+import plotly.express as px
 
 # Dimensões da imagem
 IMG_HEIGHT = 224
@@ -55,18 +56,19 @@ def predict_image(input_image):
     predicted_class = class_names[np.argmax(prediction)]
     probability = prediction[0][np.argmax(prediction)]
 
-    # Formate os resultados da previsão para exibição usando o componente BarPlot
-    bar_plot_values = [probability, 1 - probability]
-    bar_plot_labels = ["Normal", "Cataract"]
-
-    formatted_text = gr.Plot(values=bar_plot_values, labels=bar_plot_labels, type="bar").render()
-    return formatted_text
+    # Retorna a previsão em formato de string
+    return f"""
+Predicted Class: {predicted_class}
+Probability: {probability:.2%}
+"""
 
 # Crie uma interface Gradio para fazer previsões
 iface = gr.Interface(
     fn=predict_image,
     inputs=gr.inputs.Image(label="Upload an Image", type="pil"),
-    outputs=gr.outputs.Textbox(label="Prediction", type="text")
+    outputs=gr.outputs.Output(label="Prediction", type="string"),
+    interpretation="text",
+    custom_interpretation_fn=predict_image
 )
 
 # Execute a interface Gradio