Update app.py

app.py

@@ -1,52 +1,43 @@
-import gradio as gr
+import requests
 import tensorflow as tf
-from tensorflow.keras.models import load_model
-from tensorflow.keras.layers import Layer
-import numpy as np
+import gradio as gr
 from PIL import Image
+import numpy as np
 
-# Define the custom 'FixedDropout' layer
-class FixedDropout(Layer):
-    def __init__(self, rate, **kwargs):
-        super(FixedDropout, self).__init__(**kwargs)
-        self.rate = rate
-
-    def call(self, inputs, training=None):
-        if training is None:
-            training = K.learning_phase()
-
-        if training == 1:
-            return K.dropout(inputs, self.rate)
-        return inputs
-
-# Register the custom layer in a custom object scope
-custom_objects = {"FixedDropout": FixedDropout}
-
-# Load the TensorFlow model with the custom object scope
-tf_model_path = 'modelo_treinado.h5'  # Update with the path to your model
-tf_model = load_model(tf_model_path, custom_objects=custom_objects)
+# Load your custom TensorFlow model. Update 'modelo_treinado.h5' with the path to your model.
+tf_model_path = 'modelo_treinado.h5'
+tf_model = tf.keras.models.load_model(tf_model_path)
 
-# Class labels for the model
+# Define your class labels.
 class_labels = ["Normal", "Cataract"]
 
-# Define a function for prediction
-def predict(image):
-    # Preprocess the input image
-    image = image.resize((224, 224))  # Adjust the size as needed
-    image = np.array(image) / 255.0  # Normalize pixel values
-    image = np.expand_dims(image, axis=0)  # Add batch dimension
-
-    # Make a prediction using the loaded TensorFlow model
-    predictions = tf_model.predict(image)
-
-    # Get the predicted class label
-    predicted_label = class_labels[np.argmax(predictions)]
-
-    return predicted_label
-
-# Create the Gradio interface
+def preprocess_image(image):
+    # Resize the image to the input size required by the model (e.g., 224x224).
+    image = image.resize((224, 224))
+    # Convert the PIL image to a NumPy array and normalize pixel values.
+    image = np.array(image) / 255.0
+    # Add a batch dimension to the image.
+    image = np.expand_dims(image, axis=0)
+    return image
+
+def predict(inp):
+    # Preprocess the input image.
+    inp = preprocess_image(inp)
+    # Make predictions using your custom TensorFlow model.
+    predictions = tf_model.predict(inp)
+    # Get the class label with the highest confidence.
+    predicted_class = class_labels[np.argmax(predictions)]
+    # Get the confidence score of the predicted class.
+    confidence = float(predictions[0][np.argmax(predictions)])
+    
+    # Create a dictionary with the predicted class and its confidence.
+    result = {predicted_class: confidence}
+    
+    return result
+
+# Create a Gradio interface.
 gr.Interface(
     fn=predict,
-    inputs=gr.Image(type="pil"),
-    outputs=gr.Label(num_top_classes=2)
+    inputs=gr.inputs.Image(type="pil"),
+    outputs=gr.outputs.Label(num_top_classes=1)
 ).launch()