feat: add simple prediction model with preprocessing and postprocessing functions

app_test.py

@@ -166,7 +166,38 @@ register_model_with_metadata(
     display_name="ViT", contributor="temp", model_path=MODEL_PATHS["model_7"]
 )
 
+def preprocess_simple_prediction(image):
+    # The simple_prediction function expects a PIL image (filepath is handled internally)
+    return image
+
+def postprocess_simple_prediction(result, class_names):
+    scores = {name: 0.0 for name in class_names}
+    fake_prob = result.get("Fake Probability")
+    if fake_prob is not None:
+        # Assume class_names = ["AI", "REAL"]
+        scores["AI"] = float(fake_prob)
+        scores["REAL"] = 1.0 - float(fake_prob)
+    return scores
 
+def simple_prediction(img):
+    client = Client("aiwithoutborders-xyz/OpenSight-Community-Forensics-Preview")
+    result = client.predict(
+            input_image=handle_file(img),
+            api_name="/simple_predict"
+    )
+    return result
+
+
+register_model_with_metadata(
+    "simple_prediction",
+    simple_prediction,
+    preprocess_simple_prediction,
+    postprocess_simple_prediction,
+    ["AI", "REAL"], 
+    display_name="Community Forensics",
+    contributor="Jeongsoo Park",
+    model_path="aiwithoutborders-xyz/CommunityForensics-DeepfakeDet-ViT"
+)
 def infer(image: Image.Image, model_id: str, confidence_threshold: float = 0.75) -> dict:
     """Predict using a specific model.
 
@@ -403,15 +434,6 @@ def ensemble_prediction(img, confidence_threshold, augment_methods, rotate_degre
 
     return img_pil, cleaned_forensics_images, table_rows, json_results, consensus_html
 
-def simple_prediction(img):
-    client = Client("aiwithoutborders-xyz/OpenSight-Community-Forensics-Preview")
-    result = client.predict(
-            input_image=handle_file(img),
-            api_name="/simple_predict"
-    )
-    return result
-
-
 detection_model_eval_playground = gr.Interface(
     fn=ensemble_prediction,
     inputs=[