refactor: rename image preprocessing function for clarity and update HTML output for consensus display

app_mcp.py

@@ -107,16 +107,14 @@ CLASS_NAMES = {
 
 }
 
-def preprocess_resize_256(image):
-    if image.mode != 'RGB':
-        image = image.convert('RGB')
-    return transforms.Resize((256, 256))(image)
-
 def preprocess_resize_224(image):
     if image.mode != 'RGB':
         image = image.convert('RGB')
     return transforms.Resize((224, 224))(image)
-
+def preprocess_256(image):
+    if image.mode != 'RGB':
+        image = image.convert('RGB')
+    return transforms.Resize((256, 256))(image)
 def postprocess_pipeline(prediction, class_names):
     # Assumes HuggingFace pipeline output
     return {pred['label']: pred['score'] for pred in prediction}
@@ -143,7 +141,7 @@ image_processor_1 = AutoImageProcessor.from_pretrained(MODEL_PATHS["model_1"], u
 model_1 = Swinv2ForImageClassification.from_pretrained(MODEL_PATHS["model_1"]).to(device)
 clf_1 = pipeline(model=model_1, task="image-classification", image_processor=image_processor_1, device=device)
 register_model_with_metadata(
-    "model_1", clf_1, preprocess_resize_256, postprocess_pipeline, CLASS_NAMES["model_1"],
+    "model_1", clf_1, preprocess_256, postprocess_pipeline, CLASS_NAMES["model_1"],
     display_name="SwinV2 Based", contributor="haywoodsloan", model_path=MODEL_PATHS["model_1"]
 )
 
@@ -156,10 +154,7 @@ register_model_with_metadata(
 # Register remaining models
 feature_extractor_3 = AutoFeatureExtractor.from_pretrained(MODEL_PATHS["model_3"], device=device)
 model_3 = AutoModelForImageClassification.from_pretrained(MODEL_PATHS["model_3"]).to(device)
-def preprocess_256(image):
-    if image.mode != 'RGB':
-        image = image.convert('RGB')
-    return transforms.Resize((256, 256))(image)
+
 def postprocess_logits_model3(outputs, class_names):
     logits = outputs.logits.cpu().numpy()[0]
     probabilities = softmax(logits)
@@ -405,7 +400,7 @@ def predict_with_ensemble(img, confidence_threshold, augment_methods, rotate_deg
         logger.info(f"Row {i} types: {[type(item) for item in row]}")
 
     # The get_consensus_label function is now replaced by final_prediction_label from weighted consensus
-    consensus_html = f"<b><span style='color:{'red' if final_prediction_label == 'AI' else ('green' if final_prediction_label == 'REAL' else 'orange')}'>{final_prediction_label}</span></b>"
+    consensus_html = f"<div style='display: flex; justify-content: space-between;'><div style='flex: 1;'><b>THIS IMAGE IS LIKELY <span style='color:{'red' if final_prediction_label == 'AI' else ('green' if final_prediction_label == 'REAL' else 'orange')}'>{final_prediction_label}</span></b></div><div style='flex: 1;'><b>CONSENSUS REACHED BY {len(results)} MODELS</b></div></div>"
 
     # Prepare data for logging to Hugging Face dataset
     inference_params = {