app.py

import spaces
import gradio as gr
import modelscope_studio.components.antd as antd
import modelscope_studio.components.base as ms
from transformers import pipeline, AutoImageProcessor, SwinForImageClassification, Swinv2ForImageClassification, AutoFeatureExtractor, AutoModelForImageClassification
from torchvision import transforms
import torch
from PIL import Image
import numpy as np
import io
import logging
from utils.utils import softmax, augment_image, convert_pil_to_bytes, ELA
from utils.gradient import gradient_processing
from utils.minmax import preprocess as minmax_preprocess


# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)


# Ensure using GPU if available
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')


# Model paths and class names
MODEL_PATHS = {
    "model_1": "haywoodsloan/ai-image-detector-deploy",
    "model_2": "Heem2/AI-vs-Real-Image-Detection",
    "model_3": "Organika/sdxl-detector",
    "model_4": "cmckinle/sdxl-flux-detector",
    "model_5": "prithivMLmods/Deep-Fake-Detector-v2-Model",
    "model_5b": "prithivMLmods/Deepfake-Detection-Exp-02-22",
    "model_6": "ideepankarsharma2003/AI_ImageClassification_MidjourneyV6_SDXL",
    "model_7": "date3k2/vit-real-fake-classification-v4"
}

CLASS_NAMES = {
    "model_1": ['artificial', 'real'],
    "model_2": ['AI Image', 'Real Image'],
    "model_3": ['AI', 'Real'],
    "model_4": ['AI', 'Real'],
    "model_5": ['Realism', 'Deepfake'],
    "model_5b": ['Real', 'Deepfake'],
    "model_6": ['ai_gen', 'human'],
    "model_7": ['Fake', 'Real'],

}

# Load models and processors
def load_models():
    image_processor_1 = AutoImageProcessor.from_pretrained(MODEL_PATHS["model_1"], use_fast=True)
    model_1 = Swinv2ForImageClassification.from_pretrained(MODEL_PATHS["model_1"])
    model_1 = model_1.to(device)
    clf_1 = pipeline(model=model_1, task="image-classification", image_processor=image_processor_1, device=device)

    clf_2 = pipeline("image-classification", model=MODEL_PATHS["model_2"], device=device)

    feature_extractor_3 = AutoFeatureExtractor.from_pretrained(MODEL_PATHS["model_3"], device=device)
    model_3 = AutoModelForImageClassification.from_pretrained(MODEL_PATHS["model_3"]).to(device)

    feature_extractor_4 = AutoFeatureExtractor.from_pretrained(MODEL_PATHS["model_4"], device=device)
    model_4 = AutoModelForImageClassification.from_pretrained(MODEL_PATHS["model_4"]).to(device)

    clf_5 = pipeline("image-classification", model=MODEL_PATHS["model_5"], device=device)
    clf_5b = pipeline("image-classification", model=MODEL_PATHS["model_5b"], device=device)

    image_processor_6 = AutoImageProcessor.from_pretrained(MODEL_PATHS["model_6"], use_fast=True)
    model_6 = SwinForImageClassification.from_pretrained(MODEL_PATHS["model_6"]).to(device)
    clf_6 = pipeline(model=model_6, task="image-classification", image_processor=image_processor_6, device=device)

    image_processor_7 = AutoImageProcessor.from_pretrained(MODEL_PATHS["model_7"], use_fast=True)
    model_7 = AutoModelForImageClassification.from_pretrained(MODEL_PATHS["model_7"]).to(device)
    clf_7 = pipeline(model=model_7, task="image-classification", image_processor=image_processor_7, device=device)

    return clf_1, clf_2, feature_extractor_3, model_3, feature_extractor_4, model_4, clf_5, clf_5b, clf_6, model_7, clf_7

clf_1, clf_2, feature_extractor_3, model_3, feature_extractor_4, model_4, clf_5, clf_5b, clf_6, model_7, clf_7 = load_models()

@spaces.GPU(duration=10)
def predict_with_model(img_pil, clf, class_names, confidence_threshold, model_name, model_id, feature_extractor=None):
    try:
        if feature_extractor:
            inputs = feature_extractor(img_pil, return_tensors="pt").to(device)
            with torch.no_grad():
                outputs = clf(**inputs)
            logits = outputs.logits
            probabilities = softmax(logits.cpu().numpy()[0])
            result = {class_names[i]: probabilities[i] for i in range(len(class_names))}
        else:
            prediction = clf(img_pil)
            result = {pred['label']: pred['score'] for pred in prediction}

        result_output = [model_id, model_name, result.get(class_names[1], 0.0), result.get(class_names[0], 0.0)]
        logger.info(result_output)
        for class_name in class_names:
            if class_name not in result:
                result[class_name] = 0.0
        if result[class_names[0]] >= confidence_threshold:
            label = f"AI, Confidence: {result[class_names[0]]:.4f}"
            result_output.append('AI')
        elif result[class_names[1]] >= confidence_threshold:
            label = f"Real, Confidence: {result[class_names[1]]:.4f}"
            result_output.append('REAL')
        else:
            label = "Uncertain Classification"
            result_output.append('UNCERTAIN')
    except Exception as e:
        label = f"Error: {str(e)}"
        result_output = [model_id, model_name, 0.0, 0.0, 'ERROR']  # Ensure result_output is assigned in case of error
    return label, result_output

@spaces.GPU(duration=10)
def predict_image(img, confidence_threshold):
    if not isinstance(img, Image.Image):
        raise ValueError(f"Expected a PIL Image, but got {type(img)}")
    if img.mode != 'RGB':
        img_pil = img.convert('RGB')
    else:
        img_pil = img
    img_pil = transforms.Resize((256, 256))(img_pil)
    img_pilvits = transforms.Resize((224, 224))(img_pil)

    label_1, result_1output = predict_with_model(img_pil, clf_1, CLASS_NAMES["model_1"], confidence_threshold, "SwinV2-base", 1)
    label_2, result_2output = predict_with_model(img_pilvits, clf_2, CLASS_NAMES["model_2"], confidence_threshold, "ViT-base Classifier", 2)
    label_3, result_3output = predict_with_model(img_pil, model_3, CLASS_NAMES["model_3"], confidence_threshold, "SDXL-Trained", 3, feature_extractor_3)
    label_4, result_4output = predict_with_model(img_pil, model_4, CLASS_NAMES["model_4"], confidence_threshold, "SDXL + FLUX", 4, feature_extractor_4)
    label_5, result_5output = predict_with_model(img_pilvits, clf_5, CLASS_NAMES["model_5"], confidence_threshold, "ViT-base Newcomer", 5)
    label_5b, result_5boutput = predict_with_model(img_pilvits, clf_5b, CLASS_NAMES["model_5b"], confidence_threshold, "ViT-base Newcomer", 6)
    label_6, result_6output = predict_with_model(img_pilvits, clf_6, CLASS_NAMES["model_6"], confidence_threshold, "Swin Midjourney/SDXL", 7)
    label_7, result_7output = predict_with_model(img_pilvits, clf_7, CLASS_NAMES["model_7"], confidence_threshold, "Vit", 7)

    combined_results = {
        "SwinV2/detect": label_1,
        "ViT/AI-vs-Real": label_2,
        "Swin/SDXL": label_3,
        "Swin/SDXL-FLUX": label_4,
        "prithivMLmods": label_5,
        "prithivMLmods-2-22": label_5b,
        "SwinMidSDXL": label_6,
        "Vit": label_7
    }
    print(combined_results)

    combined_outputs = [result_1output, result_2output, result_3output, result_4output, result_5output, result_5boutput, result_6output, result_7output]
    return img_pil, combined_outputs
# Define a function to generate the HTML content

def generate_results_html(results):
    def get_header_color(label):
        if label == 'AI':
            return 'bg-red-500 text-red-700', 'bg-red-400', 'bg-red-100', 'bg-red-700 text-red-700', 'bg-red-200'
        elif label == 'REAL':
            return 'bg-green-500 text-green-700', 'bg-green-400', 'bg-green-100', 'bg-green-700 text-green-700', 'bg-green-200'
        elif label == 'UNCERTAIN':
            return 'bg-yellow-500 text-yellow-700 bg-yellow-100', 'bg-yellow-400', 'bg-yellow-100', 'bg-yellow-700 text-yellow-700', 'bg-yellow-200'
        elif label == 'MAINTENANCE':
            return 'bg-blue-500 text-blue-700', 'bg-blue-400', 'bg-blue-100', 'bg-blue-700 text-blue-700', 'bg-blue-200'
        else:
            return 'bg-gray-300 text-gray-700', 'bg-gray-400', 'bg-gray-100', 'bg-gray-700 text-gray-700', 'bg-gray-200'

    def generate_tile_html(index, result, model_name, contributor, model_path):
        label = result[-1]
        header_colors = get_header_color(label)
        real_conf = result[2]
        ai_conf = result[3]
        return f"""
        <div
            class="flex flex-col bg-gray-800 rounded-sm p-4 m-1 border border-gray-800 shadow-xs transition hover:shadow-lg dark:shadow-gray-700/25">
            <div
                class="-m-4 h-24 {header_colors[0]} rounded-sm rounded-b-none transition border group-hover:border-gray-100 group-hover:shadow-lg group-hover:{header_colors[4]}">
                <span class="text-gray-300 font-mono tracking-widest p-4 pb-3 block text-xs text-center">MODEL {index + 1}:</span>
                <span
                    class="flex w-30 mx-auto tracking-wide items-center justify-center rounded-full {header_colors[2]} px-1 py-0.5 {header_colors[3]}"
                >
                    <svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="3" stroke="currentColor" class="w-4 h-4 mr-2 -ml-3 group-hover:animate group-hover:animate-pulse">
                        {'<path stroke-linecap="round" stroke-linejoin="round" d="M9 12.75 11.25 15 15 9.75M21 12a9 9 0 1 1-18 0 9 9 0 0 1 18 0Z" />' if label == 'REAL' else '<path stroke-linecap="round" stroke-linejoin="round" d="m9.75 9.75 4.5 4.5m0-4.5-4.5 4.5M21 12a9 9 0 1 1-18 0 9 9 0 0 1 18 0Z" />'}
                    </svg>
                    <p class="whitespace-nowrap text-lg leading-normal font-bold text-center self-center align-middle py-px">{label}</p>
                </span>
            </div>
            <div>
                <div class="mt-4 relative -mx-4 bg-gray-800">
                    <div class="w-full bg-gray-400 rounded-none h-8">
                        <div class="inline-flex whitespace-nowrap bg-green-400 h-full rounded-none" style="width: {real_conf * 100:.2f}%;">
                            <p class="p-2 px-4 text-xs self-center align-middle">Conf:
                                <span class="ml-1 font-medium font-mono">{real_conf:.4f}</span>
                            </p>
                        </div>
                    </div>
                </div>
                <div class="relative -mx-4 bg-gray-800">
                    <div class="w-full bg-gray-400 rounded-none h-8">
                        <div class="inline-flex whitespace-nowrap bg-red-400 h-full rounded-none" style="width: {ai_conf * 100:.2f}%;">
                            <p class="p-2 px-4 text-xs self-center align-middle">Conf:
                                <span class="ml-1 font-medium font-mono">{ai_conf:.4f}</span>
                            </p>
                        </div>
                    </div>
                </div>
            </div>
            <div class="flex flex-col items-start">
                <h4 class="mt-4 text-sm font-semibold tracking-wide">{model_name}</h4>
                <div class="text-xs font-mono">Real: {real_conf:.4f}, AI: {ai_conf:.4f}</div>
                <div class="card-footer">
                <a href="https://huggingface.co/{model_path}" target="_blank" class="mt-2 text-xs tracking-wide nowrap" style="font-size:0.66rem !important;">by @{contributor}</a>
                </div>
            </div>
        </div>
        """

    html_content = f"""
    <link href="https://unpkg.com/[email protected]/dist/tailwind.min.css" rel="stylesheet">
    <div class="container mx-auto">
        <div class="grid xl:grid-cols-4 md:grid-cols-4 grid-cols-1 gap-1">
            {generate_tile_html(0, results[0], "SwinV2 Based", "haywoodsloan", MODEL_PATHS["model_1"])}
            {generate_tile_html(1, results[1], "ViT Based", "Heem2", MODEL_PATHS["model_2"])}
            {generate_tile_html(2, results[2], "SDXL Dataset", "Organika", MODEL_PATHS["model_3"])}
            {generate_tile_html(3, results[3], "SDXL + FLUX", "cmckinle", MODEL_PATHS["model_4"])}
            {generate_tile_html(4, results[4], "Vit Based", "prithivMLmods", MODEL_PATHS["model_5"])}
            {generate_tile_html(5, results[5], "Vit Based, Newer Dataset", "prithivMLmods", MODEL_PATHS["model_5b"])}
            {generate_tile_html(6, results[6], "Swin, Midj + SDXL", "ideepankarsharma2003", MODEL_PATHS["model_6"])}
            {generate_tile_html(7, results[7], "ViT", "temp", MODEL_PATHS["model_7"])}
        </div>
    </div>
    """
    return html_content


def predict_image_with_html(img, confidence_threshold, augment_methods, rotate_degrees, noise_level, sharpen_strength):
    if augment_methods:
        img_pil, _ = augment_image(img, augment_methods, rotate_degrees, noise_level, sharpen_strength)
    else:
        img_pil = img
    img_pil, results = predict_image(img_pil, confidence_threshold)
    img_np = np.array(img_pil)  # Convert PIL Image to NumPy array

    gradient_image = gradient_processing(img_np)  # Added gradient processing
    minmax_image = minmax_preprocess(img_np)  # Added MinMax processing

    # Generate ELA images with different presets
    ela_img_1 = ELA(img_pil, scale=100, alpha=0.66)
    ela_img_2 = ELA(img_pil, scale=50, alpha=0.5)
    
    forensics_images = [img_pil, ela_img_1, ela_img_2, gradient_image, minmax_image]
    
    html_content = generate_results_html(results)
    return img_pil, forensics_images, html_content

with gr.Blocks(css="#post-gallery { overflow: hidden !important;} .grid-wrap{ overflow-y: hidden !important;}") as iface:
    with gr.Accordion("Project OpenSight - Model Evaluations & Playground", open=False, elem_id="project_accordion"):
        gr.Markdown("## OpenSight is a SOTA gen. image detection model, in pre-release prep.\n\nThis HF Space is a temporary home for us and the public to evaluate the shortcomings of current open source models.\n\n<-- Feel free to play around by starting with an image as we prepare our formal announcement.")
    with gr.Tab("👀 Model Evals / Playground"):
        gr.Markdown("# AI Generated Image / Deepfake Detection Models Evaluation")
        
        with gr.Row():
            with gr.Column(scale=1):
                image_input = gr.Image(label="Upload Image to Analyze", sources=['upload'], type='pil')
                with gr.Accordion("Settings (Optional)", open=False, elem_id="settings_accordion"):
                    augment_checkboxgroup = gr.CheckboxGroup(["rotate", "add_noise", "sharpen"], label="Augmentation Methods")
                    rotate_slider = gr.Slider(0, 45, value=2, step=1, label="Rotate Degrees", visible=False)
                    noise_slider = gr.Slider(0, 50, value=4, step=1, label="Noise Level", visible=False)
                    sharpen_slider = gr.Slider(0, 50, value=11, step=1, label="Sharpen Strength", visible=False)
                    confidence_slider = gr.Slider(0.0, 1.0, value=0.75, step=0.05, label="Confidence Threshold")
                inputs = [image_input, confidence_slider, augment_checkboxgroup, rotate_slider, noise_slider, sharpen_slider]
                predict_button = gr.Button("Predict")
                augment_button = gr.Button("Augment & Predict")
                image_output = gr.Image(label="Processed Image", visible=False)


            with gr.Column(scale=2):
                # Custom HTML component to display results in 5 columns
                results_html = gr.HTML(label="Model Predictions")
                forensics_gallery = gr.Gallery(label="Post Processed Images", visible=True, columns=[5], rows=[1], container=False, height="auto", object_fit="contain", elem_id="post-gallery")

                outputs = [image_output, forensics_gallery, results_html]
        
        # Show/hide rotate slider based on selected augmentation method
        augment_checkboxgroup.change(lambda methods: gr.update(visible="rotate" in methods), inputs=[augment_checkboxgroup], outputs=[rotate_slider])
        augment_checkboxgroup.change(lambda methods: gr.update(visible="add_noise" in methods), inputs=[augment_checkboxgroup], outputs=[noise_slider])
        augment_checkboxgroup.change(lambda methods: gr.update(visible="sharpen" in methods), inputs=[augment_checkboxgroup], outputs=[sharpen_slider])
        
        predict_button.click(
            fn=predict_image_with_html, 
            inputs=inputs, 
            outputs=outputs
        )
        augment_button.click(  # Connect Augment button to the function
            fn=predict_image_with_html, 
            inputs=[
                image_input, 
                confidence_slider, 
                gr.CheckboxGroup(["rotate", "add_noise", "sharpen"], value=["rotate", "add_noise", "sharpen"], visible=False),  # Default values
                rotate_slider, 
                noise_slider, 
                sharpen_slider
            ], 
            outputs=outputs
        )
        predict_button.click(
            fn=None, 
            js="() => {document.getElementById('project_accordion').open = false;}",  # Close the project accordion
            inputs=[], 
            outputs=[]
        )

    with gr.Tab("🥇 Leaderboard"):
        # Add components for the second interface here
        with ms.Application() as app:
            with antd.ConfigProvider():
            # Example: Add a simple text input and output
                text_input = gr.Textbox(label="Enter Text")
                text_output = gr.Textbox(label="Processed Text")
                text_button = gr.Button("Process Text")
                text_button.click(
                    fn=lambda x: x.upper(),  # Example function to convert text to uppercase
                    inputs=text_input, 
                    outputs=text_output
                )

# Launch the interface
iface.launch()