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import gradio as gr
import tensorflow as tf
from tensorflow.keras.preprocessing import image
import numpy as np
from PIL import Image
from keras import layers
# Load your trained Xception model
model = tf.keras.models.load_model("inception_acc_0.989001-_val_acc_0.98252.h5")
# Define the labels for your classification
class_labels = ['arm', 'hand', 'foot', 'legs','fullbody','head','backside', 'torso', 'stake', 'plastic'] # Replace with your actual class names
def classify_image(img):
# Preprocess the image to fit the model input shape
img = img.resize((299, 299)) # Xception takes 299x299 input size
img = np.array(img) / 255.0 # Normalize the image
img = np.expand_dims(img, axis=0)
# Make prediction
predictions = model.predict(img)
predicted_class = np.argmax(predictions, axis=1)[0]
confidence = np.max(predictions)
return {class_labels[i]: float(predictions[0][i]) for i in range(len(class_labels))}, confidence
# Example images (local paths or URLs)
example_images = [
'head.jpg', # Replace with actual local file paths or URLs
'torso.jpg'
]
# Gradio interface
demo = gr.Interface(
fn=classify_image,
title="Human Bodypart Image Classification",
description = "Predict the bodypart of human bodypart images. This is a demo of our human bodypart image <a href=\"https://huggingface.co/icputrd/Inception-V3-Human-Bodypart-Classifier\">classifier</a>.",
inputs=gr.Image(type="pil"),
outputs=[gr.Label(num_top_classes=len(class_labels)), gr.Number()],
examples=example_images,
cache_examples=False,
live=True,
)
if __name__ == "__main__":
demo.launch()
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