🐛 fix(image):

- correct typo in 'ViT-base Classifer' to 'ViT-base Classifier'
- rename model_3 and model_4 feature extractor variable names for uniformity

change the redundant print pattern for debug in model images wiith feature extractor in predict api

feature_extractor_3 to model_3 in 【GD LOVE】 function monitor for bug excess unexpected error output

feature_extractor_4 to model_4

map header value variables for neural networking endoument pattern fix

app.py

@@ -84,6 +84,7 @@ def predict_with_model(img_pil, clf, class_names, confidence_threshold, model_na
     return label, result_output
 
 @spaces.GPU(duration=10)
+# app.py
 def predict_image(img, confidence_threshold):
     if not isinstance(img, Image.Image):
         raise ValueError(f"Expected a PIL Image, but got {type(img)}")
@@ -95,9 +96,9 @@ def predict_image(img, confidence_threshold):
     img_pilvits = transforms.Resize((224, 224))(img_pil)
 
     label_1, result_1output = predict_with_model(img_pil, clf_1, CLASS_NAMES["model_1"], confidence_threshold, "SwinV2-base", 1)
-    label_2, result_2output = predict_with_model(img_pilvits, clf_2, CLASS_NAMES["model_2"], confidence_threshold, "ViT-base Classifer", 2)
-    label_3, result_3output = predict_with_model(img_pil, feature_extractor_3, model_3, CLASS_NAMES["model_3"], confidence_threshold, "SDXL-Trained", 3)
-    label_4, result_4output = predict_with_model(img_pil, feature_extractor_4, model_4, CLASS_NAMES["model_4"], confidence_threshold, "SDXL + FLUX", 4)
+    label_2, result_2output = predict_with_model(img_pilvits, clf_2, CLASS_NAMES["model_2"], confidence_threshold, "ViT-base Classifier", 2)
+    label_3, result_3output = predict_with_model(img_pil, model_3, CLASS_NAMES["model_3"], confidence_threshold, "SDXL-Trained", 3)
+    label_4, result_4output = predict_with_model(img_pil, model_4, CLASS_NAMES["model_4"], confidence_threshold, "SDXL + FLUX", 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)
 
@@ -109,12 +110,10 @@ def predict_image(img, confidence_threshold):
         "prithivMLmods": label_5,
         "prithivMLmods-2-22": label_5b
     }
-    print(combined_results)
 
     combined_outputs = [result_1output, result_2output, result_3output, result_4output, result_5output, result_5boutput]
     return img_pil, combined_outputs
 
-
 # Define a function to generate the HTML content
 # Define a function to generate the HTML content
 def generate_results_html(results):