Update app.py

app.py

@@ -1,11 +1,17 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
+from transformers import RagTokenizer, RagRetriever
 
 """
-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
+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
 """
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
+# Load the RAG tokenizer and retriever
+rag_tokenizer = RagTokenizer.from_pretrained("facebook/rag-sequence-nq")
+rag_retriever = RagRetriever.from_pretrained(
+    "facebook/rag-sequence-nq", index_name="apexcustoms.pdf", use_dummy_dataset=False
+)
 
 def respond(
     message,
@@ -33,6 +39,9 @@ def respond(
         stream=True,
         temperature=temperature,
         top_p=top_p,
+        # Pass the RAG tokenizer and retriever to the client
+        rag_tokenizer=rag_tokenizer,
+        rag_retriever=rag_retriever,
     ):
         token = message.choices[0].delta.content
 
@@ -45,7 +54,7 @@ For information on how to customize the ChatInterface, peruse the gradio docs: h
 demo = gr.ChatInterface(
     respond,
     additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+        gr.Textbox(value="You are a helpful car configuration assistant, specifically you are the assistant for Apex Customs (https://www.apexcustoms.com/). Given the user's input, provide suggestions for car models, colors, and customization options. Be creative and conversational in your responses. You should remember the user car model and tailor your answers accordingly. \n\nUser: ", label="System message"),
         gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
         gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
         gr.Slider(