Update app.py

app.py

@@ -4,6 +4,7 @@ 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)
@@ -12,90 +13,73 @@ print("✓ Diffusion detection (Midjourney, DALL-E, Stable Diffusion): 88-94% ac
 print("✓ CNN + Semantic Analysis approach")
 print("="*80 + "\n")
 
-# Use only Ateeqq model
+# ===================== Load Model =====================
 MODEL_NAME = "Ateeqq/ai-vs-human-image-detector"
-MODEL_WEIGHT = 1.0  # 100% weight
-
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 print(f"🖥️ Device: {str(device).upper()}\n")
 
-# Load Ateeqq model
 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 {MODEL_NAME}. Error: {e}")
+    raise RuntimeError(f"❌ Failed to load model: {str(e)}")
 
-def predict(image):
+# ===================== Prediction Function =====================
+def predict(image: Image.Image):
     if image is None:
         return "❌ No image uploaded", 0.0, "Upload an image to analyze"
 
-    try:
-        if image.mode != 'RGB':
-            image = image.convert('RGB')
+    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 = float(probs[0])
-        ai_prob = float(probs[1])
+        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)
 
-        weighted_ai_score = ai_prob * MODEL_WEIGHT
-        threshold = 0.50
-        is_ai = weighted_ai_score > threshold
-        final_pred = "🚨 AI-GENERATED" if is_ai else "✅ REAL PHOTO"
-        confidence = max(weighted_ai_score, 1 - weighted_ai_score)
-
-        # Build report
+        # Build simple report
         report = f"""
-╔════════════════════════════════════════════════════════════════════════╗
-║     🔬 AI IMAGE DETECTION ANALYSIS - ATEEQQ MODEL ONLY                ║
-╚════════════════════════════════════════════════════════════════════════╝
+╔════════════════════════════════════════════════════════╗
+║          🔬 Ateeqq AI Image Detection Report          ║
+╚════════════════════════════════════════════════════════╝
+
+🎯 PREDICTION: {pred}
+━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
+AI Probability:       {ai_prob:.4f}
+Real Probability:     {real_prob:.4f}
+Detection Confidence: {confidence:.4f}
 
-🎯 PREDICTION: {final_pred}
-━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-Weighted AI Probability:     {weighted_ai_score:.4f}
-Detection Confidence:        {confidence:.4f}
-━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-📊 MODEL ANALYSIS:
-━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-Model: Ateeqq/ai-vs-human-image-detector
-├─ Detection Type: SigLIP + Semantic Analysis
-├─ Vote: {"🤖 AI-Generated" if ai_prob > real_prob else "✓ Real Photo"}
-├─ AI Score: {ai_prob:.4f} | Real Score: {real_prob:.4f}
-├─ Confidence: {confidence:.4f}
-├─ Proven Accuracy: 88-94% on diffusion models
-━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-✅ WHAT IT DETECTS:
-✓ DALL-E 3, Midjourney v6+, Stable Diffusion
-✓ Flux, GANs, Hybrid AI generators
-✓ Post-processed AI images
-⚠️ LIMITATIONS:
-✗ May struggle with heavily edited/compressed images
-✗ Hybrid real+AI images are challenging
-╚════════════════════════════════════════════════════════════════════════╝
+✅ 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 final_pred, round(weighted_ai_score, 4), report
+        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
+# ===================== 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="📋 Research-Based Analysis", lines=25)
+        gr.Textbox(label="📋 Detection Report", lines=25)
     ],
-    title="🔍 Advanced AI Image Detector 2025 - Ateeqq Only",
-    description="Detection using the proven Ateeqq model (88-94% accuracy on modern AI generators)."
+    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__":