Spaces:
Sleeping
Sleeping
File size: 1,596 Bytes
adf2111 32e9f67 78b24ae dfdde84 8d9c7cb 78b24ae da33e50 32e9f67 da33e50 32e9f67 dfdde84 1a65837 32e9f67 78b24ae 32e9f67 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 |
import gradio as gr
import tensorflow as tf
from tensorflow.keras.models import load_model
from tensorflow.keras.layers import Layer
import numpy as np
from PIL import Image
# 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)
# Class labels for the model
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
gr.Interface(
fn=predict,
inputs=gr.Image(type="pil"),
outputs=gr.Label(num_top_classes=2)
).launch()
|