from transformers import pipeline
from PIL import Image

print("⏳ Loading AI Model...")
pipe = pipeline("image-classification", model="dima806/deepfake_vs_real_image_detection")
print("✅ Model Loaded Successfully!")

def predict_deepfake(image_path):
    try:
        img = Image.open(image_path)
        results = pipe(img)
        
        # DEBUG: Print the raw output
        print(f"🔍 Raw AI Output: {results}")

        fake_score = 0.0
        real_score = 0.0

        for res in results:
            label_fixed = res['label'].lower() # FORCE LOWERCASE
            
            if "fake" in label_fixed or "ai" in label_fixed:
                fake_score = res['score']
            elif "real" in label_fixed:
                real_score = res['score']
        
        print(f"🧐 Analysis -> Real: {real_score:.5f} | Fake: {fake_score:.5f}")

        # --- CORRECTION: STANDARD THRESHOLD ---
        # Changed from 0.001 to 0.50. 
        # This means the model must be at least 50% sure it's fake to flag it.
        THRESHOLD = 0.50 

        if fake_score > THRESHOLD:
            return {
                "is_fake": True,
                "confidence": f"{fake_score * 100:.1f}%",
                "message": "⚠️ DEEPFAKE DETECTED"
            }
        else:
            return {
                "is_fake": False,
                # Return the REAL score if it is real
                "confidence": f"{real_score * 100:.1f}%",
                "message": "✅ VERIFIED REAL"
            }

    except Exception as e:
        print(f"Error: {e}")
        return {"is_fake": False, "confidence": "0%", "message": "Error"}