app.py

import gradio as gr
from gradio_client import Client, handle_file
from PIL import Image, ImageFilter
import numpy as np
import os
import time
import logging
import io
import collections
import onnxruntime

from utils.utils import softmax, augment_image
from forensics.gradient import gradient_processing
from forensics.minmax import minmax_process
from forensics.ela import ELA
from forensics.wavelet import noise_estimation
from forensics.bitplane import bit_plane_extractor
from utils.hf_logger import log_inference_data
from utils.load import load_image
from agents.ensemble_team import EnsembleMonitorAgent, WeightOptimizationAgent, SystemHealthAgent
from agents.smart_agents import ContextualIntelligenceAgent, ForensicAnomalyDetectionAgent
from utils.registry import register_model, MODEL_REGISTRY, ModelEntry
from agents.ensemble_weights import ModelWeightManager
from transformers import pipeline, AutoImageProcessor, SwinForImageClassification, Swinv2ForImageClassification, AutoFeatureExtractor, AutoModelForImageClassification
from torchvision import transforms
import torch
import json
from huggingface_hub import CommitScheduler
from dotenv import load_dotenv

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
os.environ['HF_HUB_CACHE'] = './models'

# --- Gradio Log Handler ---
class GradioLogHandler(logging.Handler):
    def __init__(self, log_queue):
        super().__init__()
        self.log_queue = log_queue
        self.setFormatter(logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s'))

    def emit(self, record):
        self.log_queue.append(self.format(record))

log_queue = collections.deque(maxlen=1000) # Store last 1000 log messages
gradio_handler = GradioLogHandler(log_queue)

# Set root logger level to DEBUG to capture all messages from agents
logging.getLogger().setLevel(logging.INFO)
logging.getLogger().addHandler(gradio_handler)
# --- End Gradio Log Handler ---

LOCAL_LOG_DIR = "./hf_inference_logs"
HF_DATASET_NAME="aiwithoutborders-xyz/degentic_rd0"
load_dotenv()

# Custom JSON Encoder to handle numpy types
class NumpyEncoder(json.JSONEncoder):
    def default(self, obj):
        if isinstance(obj, np.float32):
            return float(obj)
        return json.JSONEncoder.default(self, obj)

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

# Model paths and class names (copied from app_mcp.py)
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_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_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))}

def register_model_with_metadata(model_id, model, preprocess, postprocess, class_names, display_name, contributor, model_path, architecture=None, dataset=None):
    entry = ModelEntry(model, preprocess, postprocess, class_names, display_name=display_name, contributor=contributor, model_path=model_path, architecture=architecture, dataset=dataset)
    MODEL_REGISTRY[model_id] = entry

# Load and register models (copied from app_mcp.py)
# 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="SWIN1", contributor="haywoodsloan", model_path=MODEL_PATHS["model_1"],
#     architecture="SwinV2", dataset="TBA"
# )

# --- ONNX Quantized Model Example ---
ONNX_QUANTIZED_MODEL_PATH = "./models/model_1_quantized.onnx"

def preprocess_onnx_input(image: Image.Image):
    # Preprocess image for ONNX model (e.g., for SwinV2, usually 256x256, normalized)
    if image.mode != 'RGB':
        image = image.convert('RGB')
    
    transform = transforms.Compose([
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485,0.456,0.406], std=[0.229,0.224,0.225]), # ImageNet normalization
    ])
    input_tensor = transform(image)
    # ONNX expects numpy array with batch dimension (1, C, H, W)
    return input_tensor.unsqueeze(0).cpu().numpy()

def infer_onnx_model(preprocessed_image_np):
    try:
        # Ensure the ONNX model exists before trying to load it
        if not os.path.exists(ONNX_QUANTIZED_MODEL_PATH):
            logger.error(f"ONNX quantized model not found at: {ONNX_QUANTIZED_MODEL_PATH}")
            raise FileNotFoundError(f"ONNX quantized model not found at: {ONNX_QUANTIZED_MODEL_PATH}")

        ort_session = onnxruntime.InferenceSession(ONNX_QUANTIZED_MODEL_PATH)
        ort_inputs = {ort_session.get_inputs()[0].name: preprocessed_image_np}
        ort_outputs = ort_session.run(None, ort_inputs)
        
        # Assuming the output is logits, apply softmax to get probabilities
        logits = ort_outputs[0]
        probabilities = softmax(logits[0]) # Remove batch dim, apply softmax
        return {"logits": logits, "probabilities": probabilities}

    except Exception as e:
        logger.error(f"Error during ONNX inference: {e}")
        # Return a structure consistent with other model errors
        return {"logits": np.array([]), "probabilities": np.array([])}

def postprocess_onnx_output(onnx_output, class_names):
    probabilities = onnx_output.get("probabilities")
    if probabilities is not None and len(probabilities) == len(class_names):
        return {class_names[i]: probabilities[i] for i in range(len(class_names))}
    else:
        logger.warning("ONNX post-processing failed or class names mismatch.")
        return {name: 0.0 for name in class_names}

# Register the ONNX quantized model
register_model_with_metadata(
    "model_1_onnx_quantized", 
    infer_onnx_model, 
    preprocess_onnx_input, 
    postprocess_onnx_output, 
    CLASS_NAMES["model_1"], # Assuming it uses the same class names as model_1
    display_name="SWIN1", 
    contributor="haywoodsloan", 
    model_path=ONNX_QUANTIZED_MODEL_PATH,
    architecture="SwinV2", 
    dataset="TBA"
)
# --- End ONNX Quantized Model Example ---

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="VIT2", contributor="Heem2", model_path=MODEL_PATHS["model_2"],
    architecture="ViT", dataset="TBA"
)

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="SDXL3", contributor="Organika", model_path=MODEL_PATHS["model_3"],
    architecture="VIT", dataset="SDXL"
)

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="XLFLUX4", contributor="cmckinle", model_path=MODEL_PATHS["model_4"],
    architecture="VIT", dataset="SDXL, FLUX"
)

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="VIT5", contributor="prithivMLmods", model_path=MODEL_PATHS["model_5"],
    architecture="VIT", dataset="TBA"
)

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="SWIN6", contributor="ideepankarsharma2003", model_path=MODEL_PATHS["model_6"],
    architecture="SWINv1", dataset="SDXL, Midjourney"
)

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="VIT7", contributor="date3k2", model_path=MODEL_PATHS["model_7"],
    architecture="VIT", dataset="TBA"
)

# def postprocess_simple_prediction(result, class_names):
#     scores = {name: 0.0 for name in class_names}
#     fake_prob = result.get("Fake Probability")
#     if fake_prob is not None:
#         # Assume class_names = ["AI", "REAL"]
#         scores["AI"] = float(fake_prob)
#         scores["REAL"] = 1.0 - float(fake_prob)
#     return scores

# def simple_prediction(img):
#     client = Client("aiwithoutborders-xyz/OpenSight-Community-Forensics-Preview")
#     client.view_api()
#     print(type(img))
#     result = client.predict(
#            handle_file(img),
#            api_name="simple_predict"
#     )
#     return result


# register_model_with_metadata(
#     model_id="simple_prediction",
#     model=simple_prediction,
#     preprocess=None,
#     postprocess=postprocess_simple_prediction,
#     class_names=["AI", "REAL"], 
#     display_name="Community Forensics",
#     contributor="Jeongsoo Park",
#     model_path="aiwithoutborders-xyz/CommunityForensics-DeepfakeDet-ViT",
#     architecture="ViT", dataset="GOAT"
# )
def infer(image: Image.Image, model_id: str, confidence_threshold: float = 0.75) -> dict:
    """Predict using a specific model.

    Args:
        image (Image.Image): The input image to classify.
        model_id (str): The ID of the model to use for classification.
        confidence_threshold (float, optional): The confidence threshold for classification. Defaults to 0.75.

    Returns:
        dict: A dictionary containing the model details, classification scores, and label.
    """
    entry = MODEL_REGISTRY[model_id]
    img = entry.preprocess(image) if entry.preprocess else image
    try:
        result = entry.model(img)
        scores = entry.postprocess(result, entry.class_names)
        ai_score = float(scores.get(entry.class_names[0], 0.0))
        real_score = float(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": 0.0,
            "Real Score": 0.0,
            "Label": f"Error: {str(e)}"
        }

def full_prediction(img, confidence_threshold, rotate_degrees, noise_level, sharpen_strength):
    """Full prediction run, with a team of ensembles and agents.

    Args:
        img (url: str, Image.Image, np.ndarray): The input image to classify.
        confidence_threshold (float, optional): The confidence threshold for classification. Defaults to 0.75.
        rotate_degrees (int, optional): The degrees to rotate the image.
        noise_level (int, optional): The noise level to use.
        sharpen_strength (int, optional): The sharpen strength to use.

    Returns:
        dict: A dictionary containing the model details, classification scores, and label.
    """
    # Ensure img is a PIL Image object
    if img is None:
        raise gr.Error("No image provided. Please upload an image to analyze.")
    # Handle filepath conversion if needed
    if isinstance(img, str):
        try:
            img = load_image(img)
        except Exception as e:
            logger.error(f"Error loading image from path: {e}")
            raise gr.Error(f"Could not load image from the provided path. Error: {str(e)}")
        
    if not isinstance(img, Image.Image):
        try:
            img = Image.fromarray(img)
        except Exception as e:
            logger.error(f"Error converting input image to PIL: {e}")
            raise gr.Error("Input image could not be converted to a valid image format. Please try another image.")

    # Ensure image is in RGB format for consistent processing
    if img.mode != 'RGB':
        img = img.convert('RGB')

    monitor_agent = EnsembleMonitorAgent()
    weight_manager = ModelWeightManager(strongest_model_id="simple_prediction")
    optimization_agent = WeightOptimizationAgent(weight_manager)
    health_agent = SystemHealthAgent()
    context_agent = ContextualIntelligenceAgent()
    anomaly_agent = ForensicAnomalyDetectionAgent()
    health_agent.monitor_system_health()
    if rotate_degrees or noise_level or sharpen_strength:
        img_pil, _ = augment_image(img, ["rotate", "add_noise", "sharpen"], rotate_degrees, noise_level, sharpen_strength)
    else:
        img_pil = img
    img_np_og = np.array(img)

    model_predictions_raw = {}
    confidence_scores = {}
    results = []
    table_rows = []

    # Stream results as each model finishes
    for model_id in MODEL_REGISTRY:
        model_start = time.time()
        result = infer(img_pil, model_id, confidence_threshold)
        model_end = time.time()
        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_raw[model_id] = result
        confidence_scores[model_id] = max(result.get("AI Score", 0.0), result.get("Real Score", 0.0))
        results.append(result)
        table_rows.append([
            result.get("Model", ""),
            result.get("Contributor", ""),
            round(result.get("AI Score", 0.0), 3) if result.get("AI Score") is not None else 0.0,
            round(result.get("Real Score", 0.0), 3) if result.get("Real Score") is not None else 0.0,
            result.get("Label", "Error")
        ])
        # Yield partial results: only update the table, others are None
        yield None, None, table_rows, None, None

    # After all models, compute the rest as before
    image_data_for_context = {
        "width": img.width,
        "height": img.height,
        "mode": img.mode,
    }
    detected_context_tags = context_agent.infer_context_tags(image_data_for_context, model_predictions_raw)
    logger.info(f"Detected context tags: {detected_context_tags}")
    adjusted_weights = weight_manager.adjust_weights(model_predictions_raw, confidence_scores, context_tags=detected_context_tags)
    weighted_predictions = {"AI": 0.0, "REAL": 0.0, "UNCERTAIN": 0.0}
    for model_id, prediction in model_predictions_raw.items():
        prediction_label = prediction.get("Label")
        if prediction_label in weighted_predictions:
            weighted_predictions[prediction_label] += adjusted_weights[model_id]
        else:
            logger.warning(f"Unexpected prediction label '{prediction_label}' 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"
    optimization_agent.analyze_performance(final_prediction_label, None)
    gradient_image = gradient_processing(img_np_og)
    gradient_image2 = gradient_processing(img_np_og, intensity=45, equalize=True)
    minmax_image = minmax_process(img_np_og)
    minmax_image2 = minmax_process(img_np_og, radius=6)
    # bitplane_image = bit_plane_extractor(img_pil)
    ela1 = ELA(img_np_og, quality=75, scale=50, contrast=20, linear=False, grayscale=True)
    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, gradient_image2, minmax_image, minmax_image2]
    forensic_output_descriptions = [
        f"Original augmented image (PIL): {img_pil.width}x{img_pil.height}",
        "ELA analysis (Pass 1): Grayscale error map, quality 75.",
        "ELA analysis (Pass 2): Grayscale error map, quality 75, enhanced contrast.",
        "ELA analysis (Pass 3): Color error map, quality 75, enhanced contrast.",
        "Gradient processing: Highlights edges and transitions.",
        "Gradient processing: Int=45, Equalize=True",
        "MinMax processing: Deviations in local pixel values.",
        "MinMax processing (Radius=6): Deviations in local pixel values.",
        # "Bit Plane extractor: Visualization of individual bit planes from different color channels."
    ]
    anomaly_detection_results = anomaly_agent.analyze_forensic_outputs(forensic_output_descriptions)
    logger.info(f"Forensic anomaly detection: {anomaly_detection_results['summary']}")
    consensus_html = f"<div style='font-size: 2.2em; font-weight: bold;padding: 10px;'>Consensus: <span style='color:{'red' if final_prediction_label == 'AI' else ('green' if final_prediction_label == 'REAL' else 'orange')}'>{final_prediction_label}</span></div>"
    inference_params = {
        "confidence_threshold": confidence_threshold,
        "rotate_degrees": rotate_degrees,
        "noise_level": noise_level,
        "sharpen_strength": sharpen_strength,
        "detected_context_tags": detected_context_tags
    }
    ensemble_output_data = {
        "final_prediction_label": final_prediction_label,
        "weighted_predictions": weighted_predictions,
        "adjusted_weights": adjusted_weights
    }
    agent_monitoring_data_log = {
        "ensemble_monitor": {
            "alerts": monitor_agent.alerts,
            "performance_metrics": monitor_agent.performance_metrics
        },
        "weight_optimization": {
            "prediction_history_length": len(optimization_agent.prediction_history),
        },
        "system_health": {
            "memory_usage": health_agent.health_metrics["memory_usage"],
            "gpu_utilization": health_agent.health_metrics["gpu_utilization"]
        },
        "context_intelligence": {
            "detected_context_tags": detected_context_tags
        },
        "forensic_anomaly_detection": anomaly_detection_results
    }
    log_inference_data(
        original_image=img,
        inference_params=inference_params,
        model_predictions=results,
        ensemble_output=ensemble_output_data,
        forensic_images=forensics_images,
        agent_monitoring_data=agent_monitoring_data_log,
        human_feedback=None
    )
    cleaned_forensics_images = []
    for f_img in forensics_images:
        if isinstance(f_img, Image.Image):
            cleaned_forensics_images.append(f_img)
        elif isinstance(f_img, np.ndarray):
            try:
                cleaned_forensics_images.append(Image.fromarray(f_img))
            except Exception as e:
                logger.warning(f"Could not convert numpy array to PIL Image for gallery: {e}")
        else:
            logger.warning(f"Unexpected type in forensic_images: {type(f_img)}. Skipping.")
    logger.info(f"Cleaned forensic images types: {[type(img) for img in cleaned_forensics_images]}")
    for i, res_dict in enumerate(results):
        for key in ["AI Score", "Real Score"]:
            value = res_dict.get(key)
            if isinstance(value, np.float32):
                res_dict[key] = float(value)
                logger.info(f"Converted {key} for result {i} from numpy.float32 to float.")
    json_results = json.dumps(results, cls=NumpyEncoder)
    yield img_pil, cleaned_forensics_images, table_rows, json_results, consensus_html

detection_model_eval_playground = gr.Interface(
    fn=full_prediction,
    inputs=[
        gr.Image(label="Upload Image to Analyze", sources=['upload', 'webcam'], type='filepath'),
        gr.Slider(0.0, 1.0, value=0.7, step=0.05, label="Confidence Threshold"),
        gr.Slider(0, 45, value=0, step=1, label="Rotate Degrees", visible=False),
        gr.Slider(0, 50, value=0, step=1, label="Noise Level", visible=False),
        gr.Slider(0, 50, value=0, step=1, label="Sharpen Strength", visible=False)
    ],
    outputs=[
        gr.Image(label="Processed Image", visible=False),
        gr.Gallery(label="Post Processed Images", visible=True, columns=[4], rows=[2], container=False, height="auto", object_fit="contain", elem_id="post-gallery"),
        gr.Dataframe(
            label="Model Predictions",
            headers=["Arch / Dataset", "By", "AI", "Real", "Label"],
            datatype=["str", "str", "number", "number", "str"]
        ),
        gr.JSON(label="Raw Model Results", visible=False),
        gr.Markdown(label="Consensus", value="")
    ],
    title="Multi-Model Ensemble + Agentic Coordinated Deepfake Detection (Paper in Progress)",
    description="The detection of AI-generated images has entered a critical inflection point. While existing solutions struggle with outdated datasets and inflated claims, our approach prioritizes agility, community collaboration, and an offensive approach to deepfake detection.",
    api_name="predict",
    live=True  # Enable streaming
)
# def echo_headers(x, request: gr.Request):
#     print(dict(request.headers))
#     return str(dict(request.headers))


def predict(img):
    """
    Predicts whether an image is AI-generated or real using the SOTA Community Forensics model.

    Args:
        img (str): Path to the input image file to analyze.

    Returns:
        dict: A dictionary containing:
            - 'Fake Probability' (float): Probability score between 0 and 1 indicating likelihood of being AI-generated
            - 'Result Description' (str): Human-readable description of the prediction result

    Example:
        >>> result = predict("path/to/image.jpg")
        >>> print(result)
        {'Fake Probability': 0.002, 'Result Description': 'The image is likely real.'}
    """
    client = Client("aiwithoutborders-xyz/OpenSight-Community-Forensics-Preview")
    client.view_api()
    result = client.predict(
        handle_file(img),
        api_name="/simple_predict"
    )
    return str(result)
community_forensics_preview = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="filepath"),
    outputs=gr.HTML(), # or gr.Markdown() if it's just text
    title="Quick and simple prediction by our strongest model.",
    description="No ensemble, no context, no agents, just a quick and simple prediction by our strongest model.",
    api_name="predict"
)

# leaderboard = gr.Interface(
#     fn=lambda: "# AI Generated / Deepfake Detection Models Leaderboard: Soon™",
#     inputs=None,
#     outputs=gr.Markdown(),
#     title="Leaderboard",
#     api_name="leaderboard"
# )
def simple_prediction(img):
    """
    Quick and simple deepfake or real image prediction by the strongest open-source model on the hub.

    Args:
        img (str): The input image to analyze, provided as a file path.

    Returns:
        str: The prediction result stringified from dict. Example: `{'Fake Probability': 0.002, 'Result Description': 'The image is likely real.'}`
    """    
    client = Client("aiwithoutborders-xyz/OpenSight-Community-Forensics-Preview")
    client.view_api()
    client.predict(
        handle_file(img),
        api_name="simple_predict"
    )
simple_predict_interface = gr.Interface(
    fn=simple_prediction,
    inputs=gr.Image(type="filepath"),
    outputs=gr.Text(),
    title="Quick and simple prediction by our strongest model.",
    description="No ensemble, no context, no agents, just a quick and simple prediction by our strongest model.",
    api_name="simple_predict"
)

noise_estimation_interface = gr.Interface(
    fn=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.",
    api_name="tool_waveletnoise"
)

bit_plane_interface = 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.",
    api_name="tool_bitplane"
)

ela_interface = gr.Interface(
    fn=ELA,
    inputs=[
        gr.Image(type="pil", label="Input Image"),
        gr.Slider(1, 100, value=75, step=1, label="JPEG Quality"),
        gr.Slider(1, 100, value=50, step=1, label="Output Scale (Multiplicative Gain)"),
        gr.Slider(0, 100, value=20, step=1, label="Output Contrast (Tonality Compression)"),
        gr.Checkbox(value=False, label="Use Linear Difference"),
        gr.Checkbox(value=False, label="Grayscale Output")
    ],
    outputs=gr.Image(type="pil"),
    title="Error Level Analysis (ELA)",
    description="Performs Error Level Analysis to detect re-saved JPEG images, which can indicate tampering. ELA highlights areas of an image that have different compression levels.",
    api_name="tool_ela"
)

gradient_processing_interface = gr.Interface(
    fn=gradient_processing,
    inputs=[
        gr.Image(type="pil", label="Input Image"),
        gr.Slider(0, 100, value=90, step=1, label="Intensity"),
        gr.Dropdown(["Abs", "None", "Flat", "Norm"], label="Blue Mode", value="Abs"),
        gr.Checkbox(value=False, label="Invert Gradients"),
        gr.Checkbox(value=False, label="Equalize Histogram")
    ],
    outputs=gr.Image(type="pil"),
    title="Gradient Processing",
    description="Applies gradient filters to an image to enhance edges and transitions, which can reveal inconsistencies due to manipulation.",
    api_name="tool_gradient_processing"
)

minmax_processing_interface = gr.Interface(
    fn=minmax_process,
    inputs=[
        gr.Image(type="pil", label="Input Image"),
        gr.Radio([0, 1, 2, 3, 4], label="Channel (0:Grayscale, 1:Blue, 2:Green, 3:Red, 4:RGB Norm)", value=4),
        gr.Slider(0, 10, value=2, step=1, label="Radius")
    ],
    outputs=gr.Image(type="pil"),
    title="MinMax Processing",
    description="Analyzes local pixel value deviations to detect subtle changes in image data, often indicative of digital forgeries.",
    api_name="tool_minmax_processing"
)

# augmentation_tool_interface = gr.Interface(
#     fn=augment_image,
#     inputs=[
#         gr.Image(label="Upload Image to Augment", sources=['upload', 'webcam'], type='pil'),
#         gr.CheckboxGroup(["rotate", "add_noise", "sharpen"], label="Augmentation Methods"),
#         gr.Slider(0, 360, value=0, step=1, label="Rotate Degrees", visible=True),
#         gr.Slider(0, 100, value=0, step=1, label="Noise Level", visible=True),
#         gr.Slider(0, 200, value=1, step=1, label="Sharpen Strength", visible=True)
#     ],
#     outputs=gr.Image(label="Augmented Image", type='pil'),
#     title="Image Augmentation Tool",
#     description="Apply various augmentation techniques to your image.",
#     api_name="augment_image"
# )

# def get_captured_logs():
#     # Retrieve all logs from the queue and clear it
#     logs = list(log_queue)
#     log_queue.clear() # Clear the queue after retrieving
#     return "\n".join(logs)


demo = gr.TabbedInterface(
    [
        detection_model_eval_playground,
        community_forensics_preview,
        noise_estimation_interface,
        bit_plane_interface,
        ela_interface,
        gradient_processing_interface,
        minmax_processing_interface,
        # gr.Textbox(label="Agent Logs", interactive=False, lines=5, max_lines=20, autoscroll=True) # New textbox for logs
    ],
    [
        "Run Ensemble Prediction",
        "Open-Source SOTA Model",
        "Wavelet Blocking Noise Estimation",
        "Bit Plane Values",
        "Error Level Analysis (ELA)",
        "Gradient Processing",
        "MinMax Processing",
        # "Agent Logs" # New tab title
    ],
    title="Deepfake Detection & Forensics Tools",
    theme=None,

)
footer = gr.Markdown("### ⚠️ ENSEMBLE TEAM IN TRAINING ⚠️ \n\n**DISCLAIMER: METADATA AS WELL AS MEDIA SUBMITTED TO THIS SPACE MAY BE VIEWED AND SELECTED FOR FUTURE DATASETS, PLEASE DO NOT SUBMIT PERSONAL CONTENT. FOR UNTRACKED, PRIVATE USE OF THE MODELS YOU MAY STILL USE [THE ORIGINAL SPACE HERE](https://huggingface.co/spaces/aiwithoutborders-xyz/OpenSight-Deepfake-Detection-Models-Playground), SOTA MODEL INCLUDED.**", elem_classes="footer")

with gr.Blocks() as app:
    demo.render()
    footer.render()
    

app.queue(max_size=10, default_concurrency_limit=2).launch(mcp_server=True)