Update app.py

app.py

@@ -1,34 +1,40 @@
 import gradio as gr
 import tensorflow as tf
-from PIL import Image
+import requests
+import cv2
 import numpy as np
 
-# Carregar o modelo TensorFlow
-model = tf.keras.models.load_model('modelo_treinado.h5')
+# Define a custom layer 'FixedDropout'
+def fixed_dropout(x):
+    return tf.keras.layers.Dropout(0.5)(x)
+
+# Function to register custom layers within a custom_object_scope
+def register_custom_layers():
+    return tf.keras.utils.custom_object_scope({'FixedDropout': fixed_dropout})
+
+# Load the TensorFlow model within the custom_object_scope
+with register_custom_layers():
+    tf_model = tf.keras.models.load_model('modelo_treinado.h5')
 
-# Definir as classes
 class_labels = ["Normal", "Cataract"]
 
-# Função de previsão
 def predict(inp):
-    # Pré-processamento da imagem para adequá-la ao modelo TensorFlow
-    img = np.array(inp)
-    img = tf.image.resize(img, (224, 224))
-    img = img / 255.0  # Normalização, se necessário
-    img = tf.expand_dims(img, axis=0)
-
-    # Fazer previsão com o modelo TensorFlow
-    predictions = model.predict(img)
-    
-    # Obter a classe com a maior probabilidade
-    predicted_class = class_labels[np.argmax(predictions)]
-    
-    return {predicted_class: float(predictions[0, np.argmax(predictions)])}
-
-# Criar a interface Gradio
-demo = gr.Interface(fn=predict, 
-             inputs=gr.inputs.Image(type="pil"),
-             outputs=gr.outputs.Label(),
-             )
-             
+    # Use the TensorFlow model to predict Normal or Cataract
+    img_array = cv2.cvtColor(np.array(inp), cv2.COLOR_RGB2BGR)
+    img_array = cv2.resize(img_array, (224, 224))
+    img_array = img_array / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+
+    prediction_tf = tf_model.predict(img_array)
+    label_index = np.argmax(prediction_tf)
+    confidence_tf = float(prediction_tf[0, label_index])
+
+    return class_labels[label_index], confidence_tf
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.inputs.Image(type="pil"),
+    outputs=["label", "number"],
+)
+
 demo.launch()