app.py

import gradio as gr
from transformers import AutoImageProcessor, AutoModelForImageClassification
from PIL import Image
import torch
import torch.nn.functional as F

# ===================== Startup Info =====================
print("\n" + "="*80)
print("🔍 BEST FREE AI IMAGE DETECTOR 2025 - ATEEQQ MODEL ONLY")
print("="*80)
print("\nBased on Ateeqq 2025 benchmarks:")
print("✓ Diffusion detection (Midjourney, DALL-E, Stable Diffusion): 88-94% accuracy")
print("✓ CNN + Semantic Analysis approach")
print("="*80 + "\n")

# ===================== Load Model =====================
MODEL_NAME = "Ateeqq/ai-vs-human-image-detector"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"🖥️ Device: {str(device).upper()}\n")

try:
    processor = AutoImageProcessor.from_pretrained(MODEL_NAME)
    model = AutoModelForImageClassification.from_pretrained(MODEL_NAME).to(device)
    model.eval()
    print(f"✅ Successfully loaded model: {MODEL_NAME}")
except Exception as e:
    raise RuntimeError(f"❌ Failed to load model: {str(e)}")

# ===================== Prediction Function =====================
def predict(image: Image.Image):
    if image is None:
        return "❌ No image uploaded", 0.0, "Upload an image to analyze"

    if image.mode != "RGB":
        image = image.convert("RGB")

    try:
        inputs = processor(images=image, return_tensors="pt").to(device)
        with torch.no_grad():
            outputs = model(**inputs)
            logits = outputs.logits
            probs = F.softmax(logits, dim=1)[0].cpu().numpy()

        real_prob, ai_prob = float(probs[0]), float(probs[1])
        pred = "🚨 AI-GENERATED" if ai_prob > real_prob else "✅ REAL PHOTO"
        confidence = max(ai_prob, real_prob)

        # Build simple report
        report = f"""
╔════════════════════════════════════════════════════════╗
║          🔬 Ateeqq AI Image Detection Report          ║
╚════════════════════════════════════════════════════════╝

🎯 PREDICTION: {pred}
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
AI Probability:       {ai_prob:.4f}
Real Probability:     {real_prob:.4f}
Detection Confidence: {confidence:.4f}

✅ Detected by: Ateeqq/ai-vs-human-image-detector
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
• High accuracy on DALL-E 3, Midjourney v6+, Stable Diffusion
• CNN + Semantic Analysis approach
• Robust for post-processed AI images
• Free to use for research or analysis
"""
        return pred, round(ai_prob, 4), report

    except Exception as e:
        return f"❌ Error: {str(e)}", 0.0, f"Processing failed: {str(e)}"

# ===================== Gradio Interface =====================
demo = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil", label="📸 Upload Image"),
    outputs=[
        gr.Textbox(label="🎯 Detection Result"),
        gr.Number(label="📊 AI Score (0.0-1.0)"),
        gr.Textbox(label="📋 Detection Report", lines=25)
    ],
    title="🔍 Ateeqq AI Image Detector (2025)",
    description="Detect AI-generated images using the official Ateeqq model from Hugging Face. Works best for DALL-E 3, Midjourney v6+, Stable Diffusion."
)

if __name__ == "__main__":
    demo.launch()