Update app.py

app.py

@@ -2,7 +2,6 @@ import streamlit as st
 import torch
 import torch.nn as nn
 from transformers import PreTrainedModel, PretrainedConfig, AutoTokenizer, AutoModelForSequenceClassification, AutoModelForSeq2SeqLM
-from huggingface_hub import login
 import os
 import time
 
@@ -65,17 +64,16 @@ class TinyTransformerForSequenceClassification(PreTrainedModel):
 
 # Load models and tokenizers
 @st.cache_resource
-def load_models_and_tokenizers(hf_token):
-    login(token=hf_token)
+def load_models_and_tokenizers():
     device = torch.device("cpu")  # forcing CPU as overhead of inference on GPU slows down the inference
     
     models = {}
     tokenizers = {}
     
     # Load Tiny-toxic-detector
-    config = TinyTransformerConfig.from_pretrained("AssistantsLab/Tiny-Toxic-Detector", use_auth_token=hf_token)
-    models["Tiny-toxic-detector"] = TinyTransformerForSequenceClassification.from_pretrained("AssistantsLab/Tiny-Toxic-Detector", config=config, use_auth_token=hf_token).to(device)
-    tokenizers["Tiny-toxic-detector"] = AutoTokenizer.from_pretrained("AssistantsLab/Tiny-Toxic-Detector", use_auth_token=hf_token)
+    config = TinyTransformerConfig.from_pretrained("AssistantsLab/Tiny-Toxic-Detector")
+    models["Tiny-toxic-detector"] = TinyTransformerForSequenceClassification.from_pretrained("AssistantsLab/Tiny-Toxic-Detector", config=config).to(device)
+    tokenizers["Tiny-toxic-detector"] = AutoTokenizer.from_pretrained("AssistantsLab/Tiny-Toxic-Detector")
     
     # Load other models
     model_configs = [
@@ -85,8 +83,8 @@ def load_models_and_tokenizers(hf_token):
     ]
     
     for model_name, model_class, tokenizer_name in model_configs:
-        models[model_name] = model_class.from_pretrained(model_name, use_auth_token=hf_token).to(device)
-        tokenizers[model_name] = AutoTokenizer.from_pretrained(tokenizer_name, use_auth_token=hf_token)
+        models[model_name] = model_class.from_pretrained(model_name).to(device)
+        tokenizers[model_name] = AutoTokenizer.from_pretrained(tokenizer_name)
     
     return models, tokenizers, device
 
@@ -142,8 +140,7 @@ def main():
     """)
     
     # Load models
-    hf_token = os.getenv('AT')
-    models, tokenizers, device = load_models_and_tokenizers(hf_token)
+    models, tokenizers, device = load_models_and_tokenizers()
 
     # Reorder the models dictionary so that "Tiny-toxic-detector" is last
     model_names = sorted(models.keys(), key=lambda x: x == "Tiny-toxic-detector")
@@ -177,4 +174,4 @@ def main():
             st.warning("Please enter some text to classify.")
 
 if __name__ == "__main__":
-    main()
+    main()