refactor: remove unnecessary preprocessing function and streamline image handling in simple_prediction

app.py

@@ -172,13 +172,6 @@ register_model_with_metadata(
     architecture="VIT", dataset="TBA"
 )
 
-def preprocess_simple_prediction(image):
-    print(type(image))
-    im = load_image(image)
-    print(type(im))
-    # 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")
@@ -190,26 +183,21 @@ def postprocess_simple_prediction(result, class_names):
 
 def simple_prediction(img):
     client = Client("aiwithoutborders-xyz/OpenSight-Community-Forensics-Preview")
-    
-    # Convert PIL Image to a file-like object in memory
-    img_byte_arr = io.BytesIO()
-    img.save(img_byte_arr, format='PNG') # Using PNG for lossless conversion, can be JPEG if preferred
-    img_byte_arr.seek(0) # Rewind to the beginning of the stream
-    im = load_image(img)
-
+    client.view_api()
+    print(type(img))
     result = client.predict(
-            input_image=handle_file(img),
-            api_name="/simple_predict"
+           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"], 
+    model_id="simple_prediction",
+    model=simple_prediction,
+    preprocess=None,
+    postprocess=postprocess_simple_prediction,
+    class_names=["AI", "REAL"], 
     display_name="Community Forensics",
     contributor="Jeongsoo Park",
     model_path="aiwithoutborders-xyz/CommunityForensics-DeepfakeDet-ViT",
@@ -227,7 +215,7 @@ def infer(image: Image.Image, model_id: str, confidence_threshold: float = 0.75)
         dict: A dictionary containing the model details, classification scores, and label.
     """
     entry = MODEL_REGISTRY[model_id]
-    img = entry.preprocess(image)
+    img = entry.preprocess(image) if entry.preprocess else image
     try:
         result = entry.model(img)
         scores = entry.postprocess(result, entry.class_names)