# Use a pipeline as a high-level helper
from PIL import Image
from transformers import pipeline

img = Image.open("")
classifier = pipeline("image-classification", model="Falconsai/nsfw_image_detection")
classifier(img)

# Load model directly
import torch
from PIL import Image
from transformers import AutoModelForImageClassification, ViTImageProcessor

img = Image.open("")
model = AutoModelForImageClassification.from_pretrained("Falconsai/nsfw_image_detection")
processor = ViTImageProcessor.from_pretrained('Falconsai/nsfw_image_detection')
with torch.no_grad():
    inputs = processor(images=img, return_tensors="pt")
    outputs = model(**inputs)
    logits = outputs.logits

predicted_label = logits.argmax(-1).item()
model.config.id2label[predicted_label]

import os
import matplotlib.pyplot as plt
from PIL import Image
import numpy as np
import onnxruntime as ort
import json # Added import for json

# Predict using YOLOv9 model
def predict_with_yolov9(image_path, model_path, labels_path, input_size):
    """
    Run inference using the converted YOLOv9 model on a single image.

    Args:
        image_path (str): Path to the input image file.
        model_path (str): Path to the ONNX model file.
        labels_path (str): Path to the JSON file containing class labels.
        input_size (tuple): The expected input size (height, width) for the model.

    Returns:
        str: The predicted class label.
        PIL.Image.Image: The original loaded image.
    """
    def load_json(file_path):
        with open(file_path, "r") as f:
            return json.load(f)

    # Load labels
    labels = load_json(labels_path)

    # Preprocess image
    original_image = Image.open(image_path).convert("RGB")
    image_resized = original_image.resize(input_size, Image.Resampling.BILINEAR)
    image_np = np.array(image_resized, dtype=np.float32) / 255.0
    image_np = np.transpose(image_np, (2, 0, 1))  # [C, H, W]
    input_tensor = np.expand_dims(image_np, axis=0).astype(np.float32)

    # Load YOLOv9 model
    session = ort.InferenceSession(model_path)
    input_name = session.get_inputs()[0].name
    output_name = session.get_outputs()[0].name # Assuming classification output

    # Run inference
    outputs = session.run([output_name], {input_name: input_tensor})
    predictions = outputs[0]

    # Postprocess predictions (assuming classification output)
    # Adapt this section if your model output is different (e.g., detection boxes)
    predicted_index = np.argmax(predictions)
    predicted_label = labels[str(predicted_index)] # Assumes labels are indexed by string numbers

    return predicted_label, original_image

# Display prediction for a single image
def display_single_prediction(image_path, model_path, labels_path, input_size):
    """
    Predicts the class for a single image and displays the image with its prediction.

    Args:
        image_path (str): Path to the input image file.
        model_path (str): Path to the ONNX model file.
        labels_path (str): Path to the JSON file containing class labels.
        input_size (tuple): The expected input size (height, width) for the model.
    """
    try:
        # Run prediction
        prediction, img = predict_with_yolov9(image_path, model_path, labels_path, input_size)

        # Display image and prediction
        fig, ax = plt.subplots(1, 1, figsize=(8, 8)) # Create a single plot
        ax.imshow(img)
        ax.set_title(f"Prediction: {prediction}", fontsize=14)
        ax.axis("off") # Hide axes ticks and labels

        plt.tight_layout()
        plt.show()

    except FileNotFoundError:
        print(f"Error: Image file not found at {image_path}")
    except Exception as e:
        print(f"An error occurred: {e}")

# --- Main Execution ---

# Paths and parameters - **MODIFY THESE**
single_image_path = "path/to/your/single_image.jpg"  # 

### 局限性
- **特定任务微调**：虽然该模型擅长 NSFW 图像分类，但在应用于其他任务时，其性能可能会有所变化。
- 有兴趣将该模型用于不同任务的用户，应探索模型中心中可用的微调版本，以获得最佳效果。

## 训练数据

该模型的训练数据包含一个约 80,000 张图像的专有数据集。该数据集具有显著的可变性，包含两个不同的类别："normal" 和 "nsfw"。在此数据上的训练过程旨在使模型具备有效区分安全内容与敏感内容的能力。

### 训练统计信息
``` markdown

- 'eval_loss': 0.07463177293539047,
- 'eval_accuracy': 0.980375, 
- 'eval_runtime': 304.9846, 
- 'eval_samples_per_second': 52.462, 
- 'eval_steps_per_second': 3.279