Update app.py

app.py

@@ -8,7 +8,6 @@ import pandas as pd
 import warnings
 import math
 import numpy as np
-from tensorflow.keras.models import load_model
 
 # Suppress warnings
 warnings.filterwarnings("ignore", category=UserWarning, message="Using a slow image processor as `use_fast` is unset")
@@ -36,7 +35,6 @@ model_3 = AutoModelForImageClassification.from_pretrained(models[0]).to(device)
 feature_extractor_4 = AutoFeatureExtractor.from_pretrained(models[1], device=device)
 model_4 = AutoModelForImageClassification.from_pretrained(models[1]).to(device)
 
-model_5 = load_model("large_model_3lakh_v1.h5")
 # Define class names for all models
 class_names_1 = ['artificial', 'real']
 class_names_2 = ['AI Image', 'Real Image']
@@ -158,14 +156,14 @@ def predict_image(img, confidence_threshold):
     except Exception as e:
         label_4 = f"Error: {str(e)}"
 
-    try:
-        pred = model.predict(np.expand_dims(img_pil / 255, 0))
-        result_5 = {
-            'AI': pred[0],
-            'Real': pred[1]
-        }
-    except Exception as e:
-        label_3 = f"Error: {str(e)}"
+    # try:
+    #     pred = model.predict(np.expand_dims(img_pil / 255, 0))
+    #     result_5 = {
+    #         'AI': pred[0],
+    #         'Real': pred[1]
+    #     }
+    # except Exception as e:
+    #     label_3 = f"Error: {str(e)}"
     
     
     # Combine results
@@ -174,7 +172,7 @@ def predict_image(img, confidence_threshold):
         "AI-vs-Real-Image-Detection": label_2,
         "Organika/sdxl-detector": label_3,
         "cmckinle/sdxl-flux-detector": label_4,
-        "ALSv": label_5
+        # "ALSv": label_5
     }
     
     return combined_results