app_mcp.py

import os
import time
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
from utils.bitplane import bit_plane_extractor
# from utils.exif import exif_full_dump / currently not working

from forensics.registry import register_model, MODEL_REGISTRY, ModelEntry

# Configure logging
logging.basicConfig(level=logging.DEBUG)
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'],

}

QUICK_INTRO = """
### AI-Generated Content Detection: The Tipping Point

Remember that high-stakes game of whack-a-mole between deepfakes and detection algorithms that the world leaders promised to fund and fight? Well, to no surprise, that battle ended with what seems like a quiet acceptance of defeat. Despite massive increases in 2024 for research and funding for detection systems, it came to no surprise to anyone when the largest public detection project to date was effectively rendered useless just weeks after release. 

Then came the sucker-punches. Month after month, SOTA models started dropping like they were on a release calendar:  
• Hyper-realistic voice clones reading your emotional tells  
• Zero-shot everything making reality checks irrelevant  
• Image models that upgraded "plausible" to "indistinguishable" overnight  

It was terrifying. Exhilarating. Hands-down the most fascinating existential rollercoaster since crypto crashed. And we all know why detection lost: **Defense always lags offense.** Pouring billions into bigger, slower models was like building thicker castle walls while the enemy developed drone strikes.  

The research exodus wasn't betrayal – it was sanity. Why battle an unwinnable arms race when there's actual progress to be made elsewhere? And let's be honest: we saw this coming. When has humanity ever resisted accelerating technology that promises... *interesting* applications? As the ancients wisely tweeted: 🔞 drives innovation.  

So what now? We pivot.  
✅ Stop pretending we'll ever "solve" deepfakes. Accept they'll keep evolving.  
✅ Learn from cybersecurity: Shift from impossible prevention to damage control  
✅ Embrace and strive for radical efficiency – 10X the output at 0.1X the resource burn

But here's the silver lining, the hard-won wisdom, and the next chapter: efficiency. It's time to shift our focus from perpetual catch-up to smarter integration and acceptance. 

Because our current approach? Training mammoth models on volcanic-scale energy consumption to chase diminishing returns? That's the real deepfake we should be fighting. 

Next section: Practical, absurdly efficient alternatives already showing promise. It's not SOTA, but it just makes sense.   ⚡

"""

IMPLEMENTATION = """
### 1. **Shift away from the belief that more data leads to better results. Rather, focus on insight-driven and "quality over quantity" datasets in training.**
* **Move Away from Terabyte-Scale Datasets**: Focus on **quality over quantity** by curating a smaller, highly diverse, and **labeled dataset** emphasizing edge cases and the latest AI generations.
* **Active Learning**: Implement active learning techniques to iteratively select the most informative samples for human labeling, reducing dataset size while maintaining effectiveness.

### 2. **Efficient Model Architectures**
* **Adopt Lightweight, State-of-the-Art Models**: Explore models designed for efficiency like MobileNet, EfficientNet, or recent advancements in vision transformers (ViTs) tailored for forensic analysis.
* **Transfer Learning with Fine-Tuning**: Leverage pre-trained models fine-tuned on your curated dataset to leverage general knowledge while adapting to specific AI image detection tasks.

### 3. **Multi-Modal and Hybrid Approaches**
* **Combine Image Forensics with Metadata Analysis**: Integrate insights from image processing with metadata (e.g., EXIF, XMP) for a more robust detection framework.
* **Incorporate Knowledge Graphs for AI Model Identification**: If feasible, build or utilize knowledge graphs mapping known AI models to their generation signatures for targeted detection.

### 4. **Continuous Learning and Update Mechanism**
* **Online Learning or Incremental Training**: Implement a system that can incrementally update the model with new, strategically selected samples, adapting to new AI generation techniques.
* **Community-Driven Updates**: Establish a feedback loop with users/community to report undetected AI images, fueling model updates.

### 5. **Evaluation and Validation**
* **Robust Validation Protocols**: Regularly test against unseen, diverse datasets including novel AI generations not present during training.
* **Benchmark Against State-of-the-Art**: Periodically compare performance with newly published detection models or techniques.


"""

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

def get_consensus_label(results):
    labels = [r[4] for r in results if len(r) > 4]
    if not labels:
        return "No results"
    consensus = max(set(labels), key=labels.count)
    color = {"AI": "red", "REAL": "green", "UNCERTAIN": "orange"}.get(consensus, "gray")
    return f"<b><span style='color:{color}'>{consensus}</span></b>"

# Update predict_image_with_json to return consensus label

class ModelWeightManager:
    def __init__(self):
        self.base_weights = {
            "model_1": 0.15,  # SwinV2 Based
            "model_2": 0.15,  # ViT Based
            "model_3": 0.15,  # SDXL Dataset
            "model_4": 0.15,  # SDXL + FLUX
            "model_5": 0.15,  # ViT Based
            "model_5b": 0.10, # ViT Based, Newer Dataset
            "model_6": 0.10,  # Swin, Midj + SDXL
            "model_7": 0.05   # ViT
        }
        self.situation_weights = {
            "high_confidence": 1.2,    # Boost weights for high confidence predictions
            "low_confidence": 0.8,     # Reduce weights for low confidence
            "conflict": 0.5,          # Reduce weights when models disagree
            "consensus": 1.5          # Boost weights when models agree
        }
    
    def adjust_weights(self, predictions, confidence_scores):
        """Dynamically adjust weights based on prediction patterns"""
        adjusted_weights = self.base_weights.copy()
        
        # Check for consensus
        if self._has_consensus(predictions):
            for model in adjusted_weights:
                adjusted_weights[model] *= self.situation_weights["consensus"]
        
        # Check for conflicts
        if self._has_conflicts(predictions):
            for model in adjusted_weights:
                adjusted_weights[model] *= self.situation_weights["conflict"]
        
        # Adjust based on confidence
        for model, confidence in confidence_scores.items():
            if confidence > 0.8:
                adjusted_weights[model] *= self.situation_weights["high_confidence"]
            elif confidence < 0.5:
                adjusted_weights[model] *= self.situation_weights["low_confidence"]
        
        return self._normalize_weights(adjusted_weights)
    
    def _has_consensus(self, predictions):
        """Check if models agree on prediction"""
        return len(set(predictions.values())) == 1
    
    def _has_conflicts(self, predictions):
        """Check if models have conflicting predictions"""
        return len(set(predictions.values())) > 2
    
    def _normalize_weights(self, weights):
        """Normalize weights to sum to 1"""
        total = sum(weights.values())
        return {k: v/total for k, v in weights.items()}

class EnsembleMonitorAgent:
    def __init__(self):
        self.performance_metrics = {
            "model_accuracy": {},
            "response_times": {},
            "confidence_distribution": {},
            "consensus_rate": 0.0
        }
        self.alerts = []
    
    def monitor_prediction(self, model_id, prediction, confidence, response_time):
        """Monitor individual model performance"""
        if model_id not in self.performance_metrics["model_accuracy"]:
            self.performance_metrics["model_accuracy"][model_id] = []
            self.performance_metrics["response_times"][model_id] = []
            self.performance_metrics["confidence_distribution"][model_id] = []
        
        self.performance_metrics["response_times"][model_id].append(response_time)
        self.performance_metrics["confidence_distribution"][model_id].append(confidence)
        
        # Check for performance issues
        self._check_performance_issues(model_id)
    
    def _check_performance_issues(self, model_id):
        """Check for any performance anomalies"""
        response_times = self.performance_metrics["response_times"][model_id]
        if len(response_times) > 10:
            avg_time = sum(response_times[-10:]) / 10
            if avg_time > 2.0:  # More than 2 seconds
                self.alerts.append(f"High latency detected for {model_id}: {avg_time:.2f}s")

class WeightOptimizationAgent:
    def __init__(self, weight_manager):
        self.weight_manager = weight_manager
        self.performance_history = []
        self.optimization_threshold = 0.1  # 10% performance change triggers optimization
    
    def analyze_performance(self, predictions, actual_results):
        """Analyze model performance and suggest weight adjustments"""
        # Placeholder for actual_results. In a real scenario, this would come from a validation set.
        # For now, we'll just track predictions.
        self.performance_history.append(predictions)
        
        if self._should_optimize():
            self._optimize_weights()
    
    def _should_optimize(self):
        """Determine if weights should be optimized"""
        if len(self.performance_history) < 10:
            return False
        
        # Placeholder for actual performance calculation
        # For demonstration, let's say we optimize every 10 runs
        return len(self.performance_history) % 10 == 0
    
    def _optimize_weights(self):
        """Optimize model weights based on performance"""
        logger.info("Optimizing model weights based on recent performance.")
        # This is where more sophisticated optimization logic would go.
        # For example, you could slightly adjust weights of models that consistently predict correctly.
        pass

class SystemHealthAgent:
    def __init__(self):
        self.health_metrics = {
            "memory_usage": [],
            "gpu_utilization": [],
            "model_load_times": {},
            "error_rates": {}
        }
    
    def monitor_system_health(self):
        """Monitor overall system health"""
        self._check_memory_usage()
        self._check_gpu_utilization()
        # You might add _check_model_health() here later
    
    def _check_memory_usage(self):
        """Monitor memory usage"""
        try:
            import psutil
            memory = psutil.virtual_memory()
            self.health_metrics["memory_usage"].append(memory.percent)
            
            if memory.percent > 90:
                logger.warning(f"High memory usage detected: {memory.percent}%")
        except ImportError:
            logger.warning("psutil not installed. Cannot monitor memory usage.")
    
    def _check_gpu_utilization(self):
        """Monitor GPU utilization if available"""
        if torch.cuda.is_available():
            try:
                gpu_util = torch.cuda.memory_allocated() / torch.cuda.max_memory_allocated()
                self.health_metrics["gpu_utilization"].append(gpu_util)
                
                if gpu_util > 0.9:
                    logger.warning(f"High GPU utilization detected: {gpu_util*100:.2f}%")
            except Exception as e:
                logger.warning(f"Error monitoring GPU utilization: {e}")
        else:
            logger.info("CUDA not available. Skipping GPU utilization monitoring.")

def predict_image_with_json(img, confidence_threshold, augment_methods, rotate_degrees, noise_level, sharpen_strength):
    # Initialize agents
    monitor_agent = EnsembleMonitorAgent()
    weight_manager = ModelWeightManager()
    optimization_agent = WeightOptimizationAgent(weight_manager)
    health_agent = SystemHealthAgent()
    
    # Monitor system health
    health_agent.monitor_system_health()

    if augment_methods:
        img_pil, _ = augment_image(img, augment_methods, rotate_degrees, noise_level, sharpen_strength)
    else:
        img_pil = img
    img_np_og = np.array(img)  # Convert PIL Image to NumPy array

    # Get predictions with timing
    model_predictions = {}
    confidence_scores = {}
    results = [] # To store the results for the DataFrame

    for model_id in MODEL_REGISTRY:
        model_start = time.time()
        result = infer(img_pil, model_id, confidence_threshold)
        model_end = time.time()
        
        # Monitor individual model performance
        monitor_agent.monitor_prediction(
            model_id,
            result["Label"],
            max(result.get("AI Score", 0.0), result.get("Real Score", 0.0)),
            model_end - model_start
        )
        
        model_predictions[model_id] = result["Label"]
        confidence_scores[model_id] = max(result.get("AI Score", 0.0), result.get("Real Score", 0.0))
        results.append(result) # Add individual model result to the list
    
    # Get adjusted weights
    adjusted_weights = weight_manager.adjust_weights(model_predictions, confidence_scores)
    
    # Optimize weights if needed
    optimization_agent.analyze_performance(model_predictions, None) # Placeholder for actual results
    
    # Calculate weighted consensus
    weighted_predictions = {
        "AI": 0.0,
        "REAL": 0.0,
        "UNCERTAIN": 0.0
    }
    
    for model_id, prediction in model_predictions.items():
        # Ensure the prediction label is valid for weighted_predictions
        if prediction in weighted_predictions:
            weighted_predictions[prediction] += adjusted_weights[model_id]
        else:
            # Handle cases where prediction might be an error or unexpected label
            logger.warning(f"Unexpected prediction label '{prediction}' from model '{model_id}'. Skipping its weight in consensus.")

    final_prediction_label = "UNCERTAIN"
    if weighted_predictions["AI"] > weighted_predictions["REAL"] and weighted_predictions["AI"] > weighted_predictions["UNCERTAIN"]:
        final_prediction_label = "AI"
    elif weighted_predictions["REAL"] > weighted_predictions["AI"] and weighted_predictions["REAL"] > weighted_predictions["UNCERTAIN"]:
        final_prediction_label = "REAL"
    
    # Rest of your existing code remains the same after this point
    gradient_image = gradient_processing(img_np_og)  # Added gradient processing
    minmax_image = minmax_preprocess(img_np_og)  # 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]
    
    # Prepare table rows for Dataframe (exclude model path)
    table_rows = [[
        r.get("Model", ""),
        r.get("Contributor", ""),
        r.get("AI Score", ""),
        r.get("Real Score", ""),
        r.get("Label", "")
    ] for r in results]
    
    # The get_consensus_label function is now replaced by final_prediction_label from weighted consensus
    consensus_html = f"<b><span style='color:{'red' if final_prediction_label == 'AI' else ('green' if final_prediction_label == 'REAL' else 'orange')}'>{final_prediction_label}</span></b>"
    
    return img_pil, forensics_images, table_rows, results, consensus_html

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 demo:
    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", "AI Score", "Real Score", "Label"],
                            datatype=["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")
                        with gr.Accordion("Debug Output (Raw JSON)", open=False):
                            debug_json = gr.JSON(label="Raw Model Results")
                        consensus_md = gr.Markdown(label="Consensus", value="")

                        outputs = [image_output, forensics_gallery, results_table, debug_json, consensus_md]
                
                # 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("🙈 Project Introduction"):
                gr.Markdown("# AI Generated / Deepfake Detection Models Leaderboard: Soon™")
                
            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™")
                
            with gr.Tab("Wavelet Blocking Noise Estimation"):
                gr.Interface(
                    fn=wavelet_blocking_noise_estimation,
                    inputs=[gr.Image(type="pil"), gr.Slider(1, 32, value=8, step=1, label="Block Size")],
                    outputs=gr.Image(type="pil"),
                    title="Wavelet-Based Noise Analysis",
                    description="Analyzes image noise patterns using wavelet decomposition. This tool helps detect compression artifacts and artificial noise patterns that may indicate image manipulation. Higher noise levels in specific regions can reveal areas of potential tampering."
                )


            with gr.Tab("Bit Plane Values"):
                gr.Interface(
                    fn=bit_plane_extractor,
                    inputs=[
                        gr.Image(type="pil"),
                        gr.Dropdown(["Luminance", "Red", "Green", "Blue", "RGB Norm"], label="Channel", value="Luminance"),
                        gr.Slider(0, 7, value=0, step=1, label="Bit Plane"),
                        gr.Dropdown(["Disabled", "Median", "Gaussian"], label="Filter", value="Disabled")
                    ],
                    outputs=gr.Image(type="pil"),
                    title="Bit Plane Analysis",
                    description="Extracts and visualizes individual bit planes from different color channels. This forensic tool helps identify hidden patterns and artifacts in image data that may indicate manipulation. Different bit planes can reveal inconsistencies in image processing or editing."
                )
            # with gr.Tab("EXIF Full Dump"):
            #     gr.Interface(
            #         fn=exif_full_dump,
            #         inputs=gr.Image(type="pil"),
            #         outputs=gr.JSON(),
            #         description="Extract all EXIF metadata from the uploaded image."
            #     )

# --- MCP-Ready Launch ---
if __name__ == "__main__":
    demo.launch(mcp_server=True)