fix DataParallel loading, add limitation note to UI

app.py

@@ -9,35 +9,60 @@ from src.services.gradcam import generate_gradcam
 import tempfile
 import os
 
-# Load model once
+# Load both models once at startup — singleton pattern
+# Models stay in memory for the lifetime of the app
+# Avoids 2-3 second reload penalty on every user request
 model = load_model()
 generator_model = load_generator_model()
 
+
 def analyze_image(image):
+    """
+    Main function called by Gradio when user submits an image.
+
+    Flow:
+    1. Save PIL image to a temp file (inference functions require file paths)
+    2. Run binary prediction (real vs fake)
+    3. Run generator type prediction (Real/GAN/Diffusion/Other)
+    4. Extract image metadata and EXIF
+    5. Generate Grad-CAM heatmap
+    6. Build formatted markdown result string
+    7. Delete temp file
+    8. Return (result_text, cam_image) — matches Gradio output components
+
+    Why temp file:
+    Gradio passes images as PIL objects but our inference functions
+    expect file paths. We save temporarily and clean up after.
+    """
     if image is None:
         return "Please upload an image.", None
 
-    # Save PIL image to temp file
+    # Save PIL image to temp file with unique name
     with tempfile.NamedTemporaryFile(suffix=".jpg", delete=False) as tmp:
         image.save(tmp.name)
         tmp_path = tmp.name
 
     try:
+        # Run all analysis pipelines
         prediction = predict(tmp_path, model=model)
         metadata = get_metadata(tmp_path)
         cam_array = generate_gradcam(tmp_path, model=model)
-        cam_image = Image.fromarray(cam_array)
+        cam_image = Image.fromarray(cam_array)  # convert numpy array to PIL for Gradio
         generator_result = predict_generator(tmp_path, model=generator_model)
     finally:
+        # Always clean up temp file regardless of success or failure
         os.remove(tmp_path)
 
     label = prediction["label"]
     confidence = prediction["confidence"]
 
+    # Build markdown-formatted result string for Gradio Markdown component
     result_text = f"**{label}** — Confidence: {confidence}%\n\n"
     result_text += f"⚠️ This is a model-based estimate, not definitive proof.\n\n"
+    result_text += f"📌 **Note:** Model performs best on Stable Diffusion, StyleGAN, and DDPM images. "
+    result_text += f"Performance drops on unseen generators (DALL-E, MidJourney).\n\n"
 
-    # Generator type
+    # Generator type section — shows architecture family
     gen_type = generator_result["generator_type"]
     gen_conf = generator_result["confidence"]
     result_text += f"**Generator Type:** {gen_type} ({gen_conf}%)\n\n"
@@ -45,18 +70,27 @@ def analyze_image(image):
     for cls, prob in generator_result["class_probabilities"].items():
         result_text += f"- {cls}: {prob}%\n"
 
+    # Metadata section
     result_text += f"\n**Metadata:**\n"
     result_text += f"- Format: {metadata['format']}\n"
     result_text += f"- Dimensions: {metadata['dimensions']}\n"
     result_text += f"- File Size: {metadata['file_size_kb']} KB\n"
     result_text += f"- EXIF Data: {'Yes' if metadata['has_exif'] else 'No'}\n"
 
+    # Show EXIF note in italics — honest disclaimer about what missing EXIF means
     if not metadata["has_exif"]:
         result_text += f"\n_{metadata['exif_note']}_"
 
+    # Gradio expects return values matching the outputs list order:
+    # outputs[0] = Markdown → result_text
+    # outputs[1] = Image → cam_image
     return result_text, cam_image
 
 
+# Gradio Interface — simplest Gradio pattern
+# fn: function to call on submit
+# inputs: single image upload component
+# outputs: markdown text + image side by side
 demo = gr.Interface(
     fn=analyze_image,
     inputs=gr.Image(type="pil", label="Upload Image"),