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app.py

@@ -8,12 +8,12 @@ import torch
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
 # Load the tokenizer
-tokenizer = AutoTokenizer.from_pretrained("{full_repository_id}")
+tokenizer = AutoTokenizer.from_pretrained("rxpbtn21/t5-small-lora-summarizer")
 
 # Load the base model and then the LoRA adapter
 # Ensure the base model is also moved to the correct device
 base_model = AutoModelForSeq2SeqLM.from_pretrained("t5-small").to(device)
-model = PeftModel.from_pretrained(base_model, "{full_repository_id}")
+model = PeftModel.from_pretrained(base_model, "rxpbtn21/t5-small-lora-summarizer")
 model.eval()
 
 def summarize(text):
@@ -26,11 +26,11 @@ def summarize(text):
         return summary
     except Exception as e:
         # Log the error and return an informative message
-        print(f"Error during summarization: {{e}}")
-        return f"An error occurred during summarization. Please check the Space logs for details. Error: {{e}}"
+        print(f"Error during summarization: {e}")
+        return f"An error occurred during summarization. Please check the Space logs for details. Error: {e}"
 
 # Create Gradio interface
 iface = gr.Interface(fn=summarize, inputs="text", outputs="text", title="LoRA Fine-tuned T5-small Summarizer")
 
 # Launch the interface
-iface.launch(share=False)
+iface.launch(share=False)