Update app.py

app.py

@@ -1,27 +1,28 @@
 import os
 import gradio as gr
 from transformers import BartTokenizer, BartForConditionalGeneration
-from huggingface_hub import hf_hub_download
 
-hf_token = os.getenv("HF_TOKEN")
+hf_token = os.getenv("HF_TOKEN")  # optional unless model is private/gated
 
 # Load model and tokenizer from Hugging Face hub using the provided model name
 model_name = "iimran/SAM-TheSummariserV2"
-tokenizer = BartTokenizer.from_pretrained(model_name, use_auth_token=hf_token)
-model = BartForConditionalGeneration.from_pretrained(model_name, use_auth_token=hf_token)
+
+# FIX: use `token=` instead of `use_auth_token=`
+tokenizer = BartTokenizer.from_pretrained(model_name, token=hf_token)
+model = BartForConditionalGeneration.from_pretrained(model_name, token=hf_token)
 
 def summarize(input_text):
     # Tokenize the input text with truncation
     inputs = tokenizer(input_text, max_length=1024, truncation=True, return_tensors="pt")
-    
+
     # Generate the summary using beam search
     summary_ids = model.generate(
         inputs["input_ids"],
-        num_beams=4,          # Use beam search with 4 beams for quality summaries
-        max_length=128,       # Set maximum length for the generated summary
-        early_stopping=True   # Enable early stopping if all beams finish
+        num_beams=4,        # Use beam search with 4 beams for quality summaries
+        max_length=128,     # Set maximum length for the generated summary
+        early_stopping=True # Enable early stopping if all beams finish
     )
-    
+
     # Decode the generated summary tokens to a string
     summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
     return summary
@@ -44,4 +45,4 @@ iface = gr.Interface(
 )
 
 # Launch the Gradio interface
-iface.launch()
+iface.launch()