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import gradio as gr | |
import tensorflow as tf | |
from tensorflow.keras.models import load_model | |
import numpy as np | |
class_names = ["bird", "cat", "deer", "dog"] | |
#CloudDeploymentTest/ | |
model = load_model("model.keras") | |
def classify(input_img): | |
# We need to "normalize" the input. | |
# Input pixels are between 0 and 255, | |
# but neural net expects values 0 to 1. | |
input_img = np.array(input_img) / 255 | |
# Add a batch dimension of size 1. | |
input_img = np.array([input_img]) | |
# Run our image through the model. | |
prediction = model.predict(input_img) | |
# Remove batch dimension from output. | |
prediction = prediction[0] | |
# Turn softmax output into index. | |
prediction = np.argmax(prediction) | |
# Turn index into class name | |
return class_names[prediction] | |
demo = gr.Interface(classify, gr.Image(), "text") | |
demo.launch() |