Update app.py

app.py

@@ -52,12 +52,23 @@ def predict_image(img):
     return {classes[i]: float(probs[i]) for i in range(2)}
 
 # 5. Build and launch the Web App!
+description_text = """
+### How it works:
+This model analyzes microscopic pixel noise to determine if an image is real or AI-generated. 
+
+###  Limitations for best results:
+* **Resolution Sweet Spot:** Works best on smaller images (around **256x256 to 512x512 pixels**). Ultra-high resolution (1080p or 4K) images will fail because the model's 'magnifying glass' is only 224x224 pixels, meaning it gets lost looking at a tiny fraction of the photo!
+* **Centered Subjects:** The model strictly scans the dead-center of the image. If the AI mistakes are on the edges, it won't see them!
+* **No Screenshots:** Heavy compression (like taking a screenshot or downloading from social media) destroys the forensic evidence. Please upload raw files.
+"""
+
+# Inside your interface:
 interface = gr.Interface(
     fn=predict_image,
     inputs=gr.Image(type="pil", label="Upload an Image"),
     outputs=gr.Label(num_top_classes=2, label="Prediction"),
-    title="PixelSleuth: AI Image Detector 🕵️‍♂️",
-    description="Upload an image to see if it is a Real Photograph or AI-Generated (Fake). The model analyzes microscopic pixel artifacts to make its decision.",
+    title="PixelSleuth: AI Image Detector",
+    description=description_text, 
     flagging_mode="never"
 )