Update app.py

app.py

@@ -3,17 +3,10 @@ import torch
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import re
 from tokenizers.normalizers import Replace, Regex, Sequence, Strip
-import os # For checking model file path
+import os
 
-# --- Model & Tokenizer Configuration ---
-# Check if the local model file exists
-model1_filename = "modernbert.bin"
-if not os.path.exists(model1_filename):
-    print(f"Warning: Model file '{model1_filename}' not found. Please ensure it is in the correct directory.")
-    # You might want to handle this more gracefully, e.g., by disabling the app or using a fallback.
-    # For now, the script will likely fail at model_1.load_state_dict if the file is missing.
 
-model1_path = model1_filename
+model1_path = "https://huggingface.co/spaces/SzegedAI/AI_Detector/resolve/main/modernbert.bin"
 model2_path = "https://huggingface.co/mihalykiss/modernbert_2/resolve/main/Model_groups_3class_seed12"
 model3_path = "https://huggingface.co/mihalykiss/modernbert_2/resolve/main/Model_groups_3class_seed22"
 
@@ -24,14 +17,7 @@ try:
     tokenizer = AutoTokenizer.from_pretrained("answerdotai/ModernBERT-base")
 
     model_1 = AutoModelForSequenceClassification.from_pretrained("answerdotai/ModernBERT-base", num_labels=41)
-    if os.path.exists(model1_path):
-        model_1.load_state_dict(torch.load(model1_path, map_location=device))
-    else:
-        # Fallback or error if local model is not found.
-        # This part depends on how you want to handle the missing file.
-        # For this example, we'll assume it might raise an error later if not handled.
-        print(f"ERROR: Local model file '{model1_path}' not found. Model 1 cannot be loaded.")
-        # exit() # Or raise an exception
+    model_1.load_state_dict(torch.hub.load_state_dict_from_url(model1_path, map_location=device, progress=True))
     model_1.to(device).eval()
 
     model_2 = AutoModelForSequenceClassification.from_pretrained("answerdotai/ModernBERT-base", num_labels=41)