app.backup.py

import os
from typing import Literal
import spaces
import gradio as gr
import modelscope_studio.components.antd as antd
import modelscope_studio.components.antdx as antdx
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
from utils.gradient import gradient_processing
from utils.minmax import preprocess as minmax_preprocess
from utils.ela import genELA as ELA
from utils.wavelet import wavelet_blocking_noise_estimation

# 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')

header_style = {
    "textAlign": 'center',
    "color": '#fff',
    "height": 64,
    "paddingInline": 48,
    "lineHeight": '64px',
    "backgroundColor": '#4096ff',
}

content_style = {
    "textAlign": 'center',
    "minHeight": 120,
    "lineHeight": '120px',
    "color": '#fff',
    "backgroundColor": '#0958d9',
}

sider_style = {
    "textAlign": 'center',
    "lineHeight": '120px',
    "color": '#fff',
    "backgroundColor": '#1677ff',
}

footer_style = {
    "textAlign": 'center',
    "color": '#fff',
    "backgroundColor": '#4096ff',
}

layout_style = {
    "borderRadius": 8,
    "overflow": 'hidden',
    "width": 'calc(100% - 8px)',
    "maxWidth": 'calc(100% - 8px)',
}
# 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_v1.1",
    "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="text-base whitespace-nowrap  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 text-nowrap 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
    img_np_og = np.array(img)  # Convert PIL Image to NumPy array

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

    # First pass - standard analysis
    ela1 = ELA(img_np_og, quality=75, scale=50, contrast=20, linear=False, grayscale=True)

    # Second pass - enhanced visibility
    ela2 = ELA(img_np_og, quality=75, scale=75, contrast=25, linear=False, grayscale=True)
    ela3 = ELA(img_np_og, quality=75, scale=75, contrast=25, linear=False, grayscale=False)
    
    forensics_images = [img_pil, ela1, ela2, ela3, 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;} .ms-gr-ant-welcome-icon{ height:unset !important;} .tabs{margin-top:10px;}") as iface:
    with ms.Application() as app:
        with antd.ConfigProvider():
            antdx.Welcome(
                icon=
                "https://cdn-avatars.huggingface.co/v1/production/uploads/639daf827270667011153fbc/WpeSFhuB81DY-1TjNUmV_.png",
                title="Welcome to Project OpenSight",
                description=
                "The OpenSight aims to be an open-source SOTA generated image detection model. This HF Space is not only an introduction but a educational playground for the public to evaluate and challenge current open source models.  **Space will be upgraded shortly; inference on all 6 models should take about 1.2~ seconds.** "
            )
            with gr.Tab("👀 Detection Models Eval / Playground"):
                gr.Markdown("# Open Source Detection Models Found on the Hub\n\n - **Space will be upgraded shortly;** inference on all 6 models should take about 1.2~ seconds once we're back on CUDA.\n - The **Community Forensics** mother of all detection models is now available for inference, head to the middle tab above this.\n - Lots of exciting things coming up, stay tuned!")
                
                with gr.Row():
                    with gr.Column(scale=1):
                        image_input = gr.Image(label="Upload Image to Analyze", sources=['upload', 'webcam'], 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=[4], rows=[2], 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("👑 Community Forensics Preview"):
                temp_space = gr.load("aiwithoutborders-xyz/OpenSight-Community-Forensics-Preview", src="spaces")
                # preview # no idea if this will work
            with gr.Tab("🥇 Leaderboard"):
                gr.Markdown("# AI Generated / Deepfake Detection Models Leaderboard: Soon™")
                

# Launch the interface
iface.launch()