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import tensorflow as tf
import gradio as gr
from load_dataset import classes
nm_model = tf.keras.models.load_model("mn_model.keras")
resnet_model = tf.keras.models.load_model("resnet_best.h5")
inception_model = tf.keras.models.load_model("inception_v3.keras")
cifar10_labels = classes
models = [ "InceptionBased Model", "MobileNetBased Model", "ResNetBased Model"]
def classify_image(input_image, model_name):
try:
input_image = tf.image.resize(input_image, (32, 32))
labels = cifar10_labels
model = get_model(model_name)
input_image = tf.expand_dims(input_image, axis=0)
predictions = model.predict(input_image).flatten()
top_indices = predictions.argsort()[-10:][::-1]
confidences = {labels[i]: float(predictions[i]) for i in top_indices}
return confidences
except Exception as e:
return {"error": str(e)}
def get_model(model_name):
if model_name == "MobileNetBased Model":
return nm_model
elif model_name == "ResNetBased Model":
return resnet_model
elif model_name == "InceptionBased Model":
return inception_model
interface = gr.Interface(
fn=classify_image,
inputs=[gr.Image(type="numpy", image_mode="RGB", label="Input Image"),
gr.Dropdown(models, label="Model Choice")],
outputs=gr.Label(num_top_classes=3, label="Predictions"),
)
interface.launch(debug=False, share=True)
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