simplify tabs

app.py

@@ -207,18 +207,18 @@ def demo():
         collection_name = gr.State()
 
         gr.Markdown(
-            """<center><h2>PDF-based chatbot</center></h2>
-        <h3>Ask any questions about your PDF documents</h3>"""
+            """
+            <center><h2>PDF-based chatbot</center></h2>
+            <h3>Ask any questions about your PDF documents</h3>
+            """
         )
         gr.Markdown(
             """<b>Note:</b> This AI assistant, using Langchain and open-source LLMs, performs retrieval-augmented generation (RAG) from your PDF documents. \
-        The user interface explicitely shows multiple steps to help understand the RAG workflow. 
-        This chatbot takes past questions into account when generating answers (via conversational memory), and includes document references for clarity purposes.<br>
-        <br><b>Warning:</b> This space uses the free CPU Basic hardware from Hugging Face. Some steps and LLM models used below (free inference endpoints) can take some time to generate a reply.
-        """
+            This chatbot takes past questions into account when generating answers (via conversational memory), and includes document references for clarity purposes.<br>
+            """
         )
 
-        with gr.Tab("Step 1 - Upload PDF"):
+        with gr.Tab("Configure the chatbot"):
             with gr.Row():
                 document = gr.Files(
                     height=100,
@@ -229,7 +229,6 @@ def demo():
                 )
                 # upload_btn = gr.UploadButton("Loading document...", height=100, file_count="multiple", file_types=["pdf"], scale=1)
 
-        with gr.Tab("Step 2 - Process document"):
             with gr.Row():
                 db_btn = gr.Radio(
                     ["ChromaDB"],
@@ -266,7 +265,6 @@ def demo():
             with gr.Row():
                 db_btn = gr.Button("Generate vector database")
 
-        with gr.Tab("Step 3 - Initialize QA chain"):
             with gr.Row():
                 llm_btn = gr.Radio(
                     list_llm_simple,
@@ -311,7 +309,7 @@ def demo():
             with gr.Row():
                 qachain_btn = gr.Button("Initialize Question Answering chain")
 
-        with gr.Tab("Step 4 - Chatbot"):
+        with gr.Tab("Chatbot"):
             chatbot = gr.Chatbot(height=300)
             with gr.Accordion("Advanced - Document references", open=False):
                 with gr.Row():