🛠️ chore(model): update Swin model and refactor model loading

-improved Swin model import
-replaced redundant variable: model_6 with clf_6

🐛 fix(model): fix prediction for Swin model in `predict_image` method

-removed redundant feature_extractor usage in `predict_with_model`

app.py

@@ -1,6 +1,6 @@
 import spaces
 import gradio as gr
-from transformers import pipeline, AutoImageProcessor, Swinv2ForImageClassification, AutoFeatureExtractor, AutoModelForImageClassification
+from transformers import pipeline, AutoImageProcessor, SwinForImageClassification, Swinv2ForImageClassification, AutoFeatureExtractor, AutoModelForImageClassification
 from torchvision import transforms
 import torch
 from PIL import Image
@@ -59,12 +59,13 @@ def load_models():
     clf_5 = pipeline("image-classification", model=MODEL_PATHS["model_5"], device=device)
     clf_5b = pipeline("image-classification", model=MODEL_PATHS["model_5b"], device=device)
 
-    feature_extractor_6 = AutoFeatureExtractor.from_pretrained(MODEL_PATHS["model_6"], device=device)
-    model_6 = AutoModelForImageClassification.from_pretrained(MODEL_PATHS["model_6"]).to(device)
+    image_processor_6 = AutoImageProcessor.from_pretrained(MODEL_PATHS["model_6"], use_fast=True)
+    model_6 = SwinForImageClassification.from_pretrained(MODEL_PATHS["model_6"]).to(device)
+    clf_6 = pipeline(model=model_6, task="image-classification", image_processor=image_processor_6, device=device)
 
-    return clf_1, clf_2, feature_extractor_3, model_3, feature_extractor_4, model_4, clf_5, clf_5b, model_6, feature_extractor_6
+    return clf_1, clf_2, feature_extractor_3, model_3, feature_extractor_4, model_4, clf_5, clf_5b, clf_6
 
-clf_1, clf_2, feature_extractor_3, model_3, feature_extractor_4, model_4, clf_5, clf_5b, feature_extractor_6, model_6 = load_models()
+clf_1, clf_2, feature_extractor_3, model_3, feature_extractor_4, model_4, clf_5, clf_5b, clf_6 = load_models()
 
 @spaces.GPU(duration=10)
 def predict_with_model(img_pil, clf, class_names, confidence_threshold, model_name, model_id, feature_extractor=None):
@@ -116,7 +117,7 @@ def predict_image(img, confidence_threshold):
     label_4, result_4output = predict_with_model(img_pil, model_4, CLASS_NAMES["model_4"], confidence_threshold, "SDXL + FLUX", 4, feature_extractor_4)
     label_5, result_5output = predict_with_model(img_pilvits, clf_5, CLASS_NAMES["model_5"], confidence_threshold, "ViT-base Newcomer", 5)
     label_5b, result_5boutput = predict_with_model(img_pilvits, clf_5b, CLASS_NAMES["model_5b"], confidence_threshold, "ViT-base Newcomer", 6)
-    label_6, result_6output = predict_with_model(img_pilvits, model_6, CLASS_NAMES["model_6"], confidence_threshold, "Swin Midjourney/SDXL", 7, feature_extractor_6)
+    label_6, result_6output = predict_with_model(img_pilvits, clf_6, CLASS_NAMES["model_6"], confidence_threshold, "Swin Midjourney/SDXL", 7)
 
     combined_results = {
         "SwinV2/detect": label_1,