app.py

import gradio as gr
from transformers import ViTForImageClassification, ViTImageProcessor
from PIL import Image
import torch
import logging

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

# Load the model and processor from Hugging Face
model = ViTForImageClassification.from_pretrained("prithivMLmods/Deep-Fake-Detector-Model")
processor = ViTImageProcessor.from_pretrained("prithivMLmods/Deep-Fake-Detector-Model")

def detect(image, confidence_threshold=0.5):
    """Detect deepfake content using prithivMLmods/Deep-Fake-Detector-Model"""
    if image is None:
        raise gr.Error("Please upload an image to analyze")
    
    try:
        # Convert Gradio image (filepath) to PIL Image
        pil_image = Image.open(image).convert("RGB")
        
        # Resize to match ViT input requirements (224x224)
        pil_image = pil_image.resize((224, 224), Image.Resampling.LANCZOS)
        
        # Preprocess the image
        inputs = processor(images=pil_image, return_tensors="pt")
        
        # Perform inference
        with torch.no_grad():
            outputs = model(**inputs)
            logits = outputs.logits
            probabilities = torch.softmax(logits, dim=1)[0]
        
        # Get confidence scores
        confidence_real = probabilities[0].item() * 100
        confidence_fake = probabilities[1].item() * 100
        
        # Determine prediction based on threshold
        predicted_label = "Fake" if confidence_fake / 100 >= confidence_threshold else "Real"
        confidence_score = max(confidence_real, confidence_fake)
        
        # Log the prediction
        logger.info(f"Real: {confidence_real:.1f}%, Fake: {confidence_fake:.1f}%, Predicted: {predicted_label}")
        
        # Prepare output
        overall = f"{confidence_score:.1f}% Confidence ({predicted_label})"
        aigen = f"{confidence_fake:.1f}% (AI-Generated Content Likelihood)"
        deepfake = f"{confidence_fake:.1f}% (Face Manipulation Likelihood)"
        
        return overall, aigen, deepfake
    
    except Exception as e:
        logger.error(f"Error during analysis: {str(e)}")
        raise gr.Error(f"Analysis error: {str(e)}")

# Custom CSS (unchanged)
custom_css = """
.container {
    max-width: 1200px;
    margin: 0 auto;
    padding: 20px;
    font-family: 'Arial', sans-serif;
}
.header {
    color: #2c3e50;
    border-bottom: 2px solid #3498db;
    padding-bottom: 10px;
}
.button-gradient {
    background: linear-gradient(45deg, #3498db, #2ecc71, #9b59b6);
    background-size: 400% 400%;
    border: none;
    padding: 12px 24px;
    font-size: 16px;
    font-weight: 600;
    color: white;
    border-radius: 8px;
    cursor: pointer;
    transition: all 0.3s ease;
    animation: gradientAnimation 3s ease infinite;
    box-shadow: 0 2px 8px rgba(52, 152, 219, 0.3);
}
.button-gradient:hover {
    transform: translateY(-2px);
    box-shadow: 0 4px 12px rgba(52, 152, 219, 0.5);
}
@keyframes gradientAnimation {
    0% { background-position: 0% 50%; }
    50% { background-position: 100% 50%; }
    100% { background-position: 0% 50%; }
}
"""

MARKDOWN0 = """
<div class="header">
    <h1>DeepFake Detection System</h1>
    <p>Advanced AI-powered analysis for identifying manipulated media<br>
    Powered by prithivMLmods/Deep-Fake-Detector-Model (Updated Jan 2025)<br>
    Adjustable threshold for tuning detection sensitivity</p>
</div>
"""

# Create Gradio interface with threshold slider
with gr.Blocks(css=custom_css, theme=gr.themes.Default()) as demo:
    gr.Markdown(MARKDOWN0)
    with gr.Row(elem_classes="container"):
        with gr.Column(scale=1):
            image = gr.Image(type='filepath', height=400, label="Upload Image")
            threshold = gr.Slider(0, 1, value=0.5, step=0.01, label="Confidence Threshold (Fake)")
            detect_button = gr.Button("Analyze Image", elem_classes="button-gradient")
        with gr.Column(scale=2):
            overall = gr.Label(label="Confidence Score")
            aigen = gr.Label(label="AI-Generated Content")
            deepfake = gr.Label(label="Face Manipulation")
    
    detect_button.click(
        fn=detect,
        inputs=[image, threshold],
        outputs=[overall, aigen, deepfake]
    )

# Launch the application
demo.launch(
    debug=True
)