app.py

import gradio as gr
import numpy as np
from PIL import Image
import onnxruntime as ort  # For ONNX inference

# Load the ONNX model
model_path = "cifar10_model.onnx"
ort_session = ort.InferenceSession(model_path)

# CIFAR-10 class labels
labels = [
    "airplane", "automobile", "bird", "cat", "deer",
    "dog", "frog", "horse", "ship", "truck"
]

def preprocess_image(image):
    # Resize to 32x32 and normalize
    image = image.resize((32, 32))
    image = np.array(image).astype(np.float32) / 255.0
    # Reshape to (1, 3, 32, 32) [batch, channels, height, width]
    return np.expand_dims(image.transpose(2, 0, 1), axis=0)

def predict(image):
    # Preprocess the image
    input_data = preprocess_image(image)
    # Run inference (use the correct input name from Netron)
    outputs = ort_session.run(None, {"serving_default_keras_tensor:0": input_data})[0]
    predicted_class_idx = np.argmax(outputs)
    return labels[predicted_class_idx]

# Create the Gradio interface
gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil"),
    outputs=gr.Label(num_top_classes=3),
    title="CIFAR-10 Classifier",
    description="Upload an image to classify it into one of the CIFAR-10 classes.",
).launch()  # Add share=True for a public link