app.py

from transformers import pipeline
import onnxruntime
import numpy as np
from PIL import Image
import gradio as gr

# Load the ONNX model
onnx_model_path = "https://huggingface.co/spaces/Anas090/sites_classification/resolve/main/InceptionV3-20epochs.onnx?dl=1"
session = onnxruntime.InferenceSession(onnx_model_path)

class_labels = ['Ajloun Castle', 'Hadrians Arch', 'Petra-siq', 'Roman Ruins-Jerash', 'Roman amphitheater', 'The Cardo Maximus of Jerash', 'Wadi Rum', 'petra-Treasury', 'umm qais']

dic ={'Ajloun Castle': 0,
 'Hadrians Arch': 1,
 'Petra-siq': 2,
 'Roman Ruins-Jerash': 3,
 'Roman amphitheater': 4,
 'The Cardo Maximus of Jerash': 5,
 'Wadi Rum': 6,
 'petra-Treasury': 7,
 'umm qais': 8}

def classify_image(image, labels_text, model_name, hypothesis_template):
    img = Image.open(image).resize((475, 550))
    img_array = np.array(img).astype(np.float32) / 255.0
    img_array = np.expand_dims(img_array, axis=0)

    # Run inference with the ONNX model
    output = session.run(None, {"input": img_array})  # Replace "input" with the actual input name of your ONNX model

    # Get the predicted class index
    predicted_class_index = np.argmax(output)

    # Map the class index to the corresponding label
    predicted_class = class_labels[predicted_class_index]

    return {predicted_class: 1.0}  # You may need to adjust the confidence score based on your model's output

inputs = [
    gr.inputs.Image(type='pil', label="Site_image"),
    gr.inputs.Radio(choices=[
        "ViT/B-16",
        "ViT/L-14",
        "ViT/L-14@336px",
        "ViT/H-14",
    ], type="value", default="ViT/B-16", label="Model 模型规模"),
]

iface = gr.Interface(classify_image,
                     inputs,
                     "label",
                     title="Your Title Here")
iface.launch()