app_mcp.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 forensics.registry import register_model, MODEL_REGISTRY, ModelEntry


# 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'],

}

def preprocess_resize_256(image):
    if image.mode != 'RGB':
        image = image.convert('RGB')
    return transforms.Resize((256, 256))(image)

def preprocess_resize_224(image):
    if image.mode != 'RGB':
        image = image.convert('RGB')
    return transforms.Resize((224, 224))(image)

def postprocess_pipeline(prediction, class_names):
    # Assumes HuggingFace pipeline output
    return {pred['label']: pred['score'] for pred in prediction}

def postprocess_logits(outputs, class_names):
    # Assumes model output with logits
    logits = outputs.logits.cpu().numpy()[0]
    probabilities = softmax(logits)
    return {class_names[i]: probabilities[i] for i in range(len(class_names))}

# Expand ModelEntry to include metadata
# (Assume ModelEntry is updated in registry.py to accept display_name, contributor, model_path)
# If not, we will update registry.py accordingly after this.

def register_model_with_metadata(model_id, model, preprocess, postprocess, class_names, display_name, contributor, model_path):
    entry = ModelEntry(model, preprocess, postprocess, class_names)
    entry.display_name = display_name
    entry.contributor = contributor
    entry.model_path = model_path
    MODEL_REGISTRY[model_id] = entry

# Load and register models (example for two models)
image_processor_1 = AutoImageProcessor.from_pretrained(MODEL_PATHS["model_1"], use_fast=True)
model_1 = Swinv2ForImageClassification.from_pretrained(MODEL_PATHS["model_1"]).to(device)
clf_1 = pipeline(model=model_1, task="image-classification", image_processor=image_processor_1, device=device)
register_model_with_metadata(
    "model_1", clf_1, preprocess_resize_256, postprocess_pipeline, CLASS_NAMES["model_1"],
    display_name="SwinV2 Based", contributor="haywoodsloan", model_path=MODEL_PATHS["model_1"]
)

clf_2 = pipeline("image-classification", model=MODEL_PATHS["model_2"], device=device)
register_model_with_metadata(
    "model_2", clf_2, preprocess_resize_224, postprocess_pipeline, CLASS_NAMES["model_2"],
    display_name="ViT Based", contributor="Heem2", model_path=MODEL_PATHS["model_2"]
)

# Register remaining models
feature_extractor_3 = AutoFeatureExtractor.from_pretrained(MODEL_PATHS["model_3"], device=device)
model_3 = AutoModelForImageClassification.from_pretrained(MODEL_PATHS["model_3"]).to(device)
def preprocess_256(image):
    if image.mode != 'RGB':
        image = image.convert('RGB')
    return transforms.Resize((256, 256))(image)
def postprocess_logits_model3(outputs, class_names):
    logits = outputs.logits.cpu().numpy()[0]
    probabilities = softmax(logits)
    return {class_names[i]: probabilities[i] for i in range(len(class_names))}
def model3_infer(image):
    inputs = feature_extractor_3(image, return_tensors="pt").to(device)
    with torch.no_grad():
        outputs = model_3(**inputs)
    return outputs
register_model_with_metadata(
    "model_3", model3_infer, preprocess_256, postprocess_logits_model3, CLASS_NAMES["model_3"],
    display_name="SDXL Dataset", contributor="Organika", model_path=MODEL_PATHS["model_3"]
)

feature_extractor_4 = AutoFeatureExtractor.from_pretrained(MODEL_PATHS["model_4"], device=device)
model_4 = AutoModelForImageClassification.from_pretrained(MODEL_PATHS["model_4"]).to(device)
def model4_infer(image):
    inputs = feature_extractor_4(image, return_tensors="pt").to(device)
    with torch.no_grad():
        outputs = model_4(**inputs)
    return outputs
def postprocess_logits_model4(outputs, class_names):
    logits = outputs.logits.cpu().numpy()[0]
    probabilities = softmax(logits)
    return {class_names[i]: probabilities[i] for i in range(len(class_names))}
register_model_with_metadata(
    "model_4", model4_infer, preprocess_256, postprocess_logits_model4, CLASS_NAMES["model_4"],
    display_name="SDXL + FLUX", contributor="cmckinle", model_path=MODEL_PATHS["model_4"]
)

clf_5 = pipeline("image-classification", model=MODEL_PATHS["model_5"], device=device)
register_model_with_metadata(
    "model_5", clf_5, preprocess_resize_224, postprocess_pipeline, CLASS_NAMES["model_5"],
    display_name="Vit Based", contributor="prithivMLmods", model_path=MODEL_PATHS["model_5"]
)

clf_5b = pipeline("image-classification", model=MODEL_PATHS["model_5b"], device=device)
register_model_with_metadata(
    "model_5b", clf_5b, preprocess_resize_224, postprocess_pipeline, CLASS_NAMES["model_5b"],
    display_name="Vit Based, Newer Dataset", contributor="prithivMLmods", model_path=MODEL_PATHS["model_5b"]
)

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)
register_model_with_metadata(
    "model_6", clf_6, preprocess_resize_224, postprocess_pipeline, CLASS_NAMES["model_6"],
    display_name="Swin, Midj + SDXL", contributor="ideepankarsharma2003", model_path=MODEL_PATHS["model_6"]
)

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)
register_model_with_metadata(
    "model_7", clf_7, preprocess_resize_224, postprocess_pipeline, CLASS_NAMES["model_7"],
    display_name="ViT", contributor="temp", model_path=MODEL_PATHS["model_7"]
)

# Generic inference function

def infer(image: Image.Image, model_id: str, confidence_threshold: float = 0.75) -> dict:
    entry = MODEL_REGISTRY[model_id]
    img = entry.preprocess(image)
    try:
        result = entry.model(img)
        scores = entry.postprocess(result, entry.class_names)
        # Flatten output for Dataframe: include metadata and both class scores
        ai_score = scores.get(entry.class_names[0], 0.0)
        real_score = scores.get(entry.class_names[1], 0.0)
        label = "AI" if ai_score >= confidence_threshold else ("REAL" if real_score >= confidence_threshold else "UNCERTAIN")
        return {
            "Model": entry.display_name,
            "Contributor": entry.contributor,
            "HF Model Path": entry.model_path,
            "AI Score": ai_score,
            "Real Score": real_score,
            "Label": label
        }
    except Exception as e:
        return {
            "Model": entry.display_name,
            "Contributor": entry.contributor,
            "HF Model Path": entry.model_path,
            "AI Score": None,
            "Real Score": None,
            "Label": f"Error: {str(e)}"
        }

# Update predict_image to use all registered models in order

def predict_image(img, confidence_threshold):
    model_ids = [
        "model_1", "model_2", "model_3", "model_4", "model_5", "model_5b", "model_6", "model_7"
    ]
    results = [infer(img, model_id, confidence_threshold) for model_id in model_ids]
    return img, results

# Update predict_image_with_json to return results as a list of dicts

def predict_image_with_json(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]
    
    return img_pil, forensics_images, results

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):
                        # Use Gradio-native Dataframe to display results with headers
                        results_table = gr.Dataframe(
                            label="Model Predictions",
                            headers=["Model", "Contributor", "HF Model Path", "AI Score", "Real Score", "Label"],
                            datatype=["str", "str", "str", "number", "number", "str"]
                        )
                        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_table]
                
                # 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_json, 
                    inputs=inputs, 
                    outputs=outputs
                )
                augment_button.click(  # Connect Augment button to the function
                    fn=predict_image_with_json, 
                    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
                )
            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()