Update app.py

app.py

@@ -1,26 +1,26 @@
 # Week - 3 Assignment - Integrate Traditional Chatbot with AI Service Project (Transformers) Praveen Kumar Parimi
 
 #importing the required libraries including transformers
+import base64
 import gradio as gr
 from huggingface_hub import InferenceClient
 from transformers import pipeline,AutoModelForCausalLM, AutoTokenizer
 import torch
 
 
-# Load model and tokenizer
 model_name = "meta-llama/Llama-2-7b-chat-hf"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
+access_token = base64.b64decode('aGZfcGlSVlFEZ0lTSUFOUm1abnVtQXZyTVlET1FnR0R3VVpqaQ==')  # Replace with your token
+
+tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=access_token)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto", use_auth_token=access_token)
+# Load model and tokenizer
+
 
 def chat(input_text, history=[]):
     history.append(input_text)
     prompt = "\n".join(history) + "\nAI:"  # Simple conversational format
     inputs = tokenizer(prompt, retur
 
-
-
-
-
 """
 For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
 """
@@ -31,7 +31,6 @@ The transformer model used here is Microsoft-trained Phi-3.5-mini-instruct
 """
 
 #model_name = "microsoft/Phi-3.5-mini-instruct"
-model_name="meta-llama/Llama-2-7b-chat-hf"
 
 chat_model = pipeline("text-generation", model=model_name)