Update app.py

app.py

@@ -1,23 +1,25 @@
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import gradio as gr
 
-# Load model and tokenizer directly
-tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B")
-model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B")
+import os
+
+
+huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
+
+if huggingface_token is None:
+    print("Token Hugging Face tidak ditemukan. Pastikan Anda telah menetapkan variabel lingkungan HUGGINGFACE_TOKEN.")
+    exit()
+    
+tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B", token=huggingface_token)
+model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B", token=huggingface_token)
 
-# Define function to translate code
 def translate_code(input_code, prompt=""):
-    # Combine input code and prompt
     input_text = f"{prompt}\n\n{input_code}"
-    # Tokenize input text
     input_ids = tokenizer.encode(input_text, return_tensors="pt", max_length=1024, truncation=True)
-    # Generate output sequence
     output = model.generate(input_ids, max_length=1024, num_return_sequences=1, temperature=0.7)
-    # Decode output sequence
     translated_code = tokenizer.decode(output[0], skip_special_tokens=True)
     return translated_code
 
-# Launch Gradio interface
 gr.Interface(
     fn=translate_code,
     inputs=["text", "text"],