Update app.py

app.py

@@ -20,9 +20,9 @@ HF_TOKEN = os.environ.get("HUGGINGFACE_API_KEY", None)
 if not HF_TOKEN:
     raise ValueError("Hugging Face API key not found in environment variables. Please set the HUGGINGFACE_API_KEY environment variable.")
 
-# Load the tokenizer and model
-tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")
-model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct", device_map="auto")
+# Load the tokenizer and model with authentication
+tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct", use_auth_token=HF_TOKEN)
+model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct", use_auth_token=HF_TOKEN, device_map="auto")
 
 # Function to refine and translate text using Meta-Llama 3B
 def refine_text(prompt):
@@ -40,7 +40,7 @@ def get_answer(user_question, threshold=0.30):
 
     # Find the most similar question
     max_similarity = np.max(similarities)
-    if max_similarity > threshold:
+    if (max_similarity > threshold):
         # Get the index of the most similar question
         similar_question_idx = np.argmax(similarities)
         # Retrieve the corresponding answer
@@ -65,4 +65,5 @@ iface = gr.Interface(
     description="Ask questions related to blood donation and get answers.",
 )
 
-iface.launch()
+if __name__ == "__main__":
+    iface.launch()