Update app.py

app.py

@@ -1,37 +1,34 @@
 import gradio as gr
 import tensorflow as tf
-import requests
-import cv2
+from PIL import Image
 import numpy as np
 
-# Define a custom layer 'FixedDropout' without specifying 'name' argument
-def fixed_dropout(x):
-    return tf.keras.layers.Dropout(0.5)(x)
-
-# Load the TensorFlow model while registering the custom layer
-custom_objects = {'fixed_dropout': fixed_dropout}
-tf_model_path = 'modelo_treinado.h5'  # Update with the path to your TensorFlow model
-tf_model = tf.keras.models.load_model(tf_model_path, custom_objects=custom_objects)
+# Carregar o modelo TensorFlow
+model = tf.keras.models.load_model('modelo_treinado.h5')
 
+# Definir as classes
 class_labels = ["Normal", "Cataract"]
 
+# Função de previsão
 def predict(inp):
-    # 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"]
-)
-
+    # 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(),
+             )
+             
 demo.launch()