Update interface

app.py

@@ -9,7 +9,7 @@ from llama_index.query_engine import RetrieverQueryEngine
 
 title = "Gaia Mistral Chat RAG URL Demo"
 description = "Example of an assistant with Gradio, RAG from url and Mistral AI via its API"
-placeholder = "Posez moi une question sur l'agriculture"
+placeholder = "Vous pouvez me posez une question sur ce contexte, appuyer sur Entrée pour valider"
 placeholder_url = "Extract text from this url"
 llm_model = 'mistral-small'
 # choose api_key from .env or from input field
@@ -25,9 +25,6 @@ with gr.Blocks() as demo:
     This demo allows you to interact with a webpage and then ask questions to Mistral APIs.
     Mistral will answer with the context extracted from the webpage.
     """)
-    chatbot = gr.Chatbot()
-    msg = gr.Textbox()
-    clear = gr.ClearButton([msg, chatbot])
 
     # with gr.Row():
         # api_key_text_box = gr.Textbox(placeholder=placeholder_api_key, container=False, scale=7)
@@ -46,10 +43,18 @@ with gr.Blocks() as demo:
         query_engine = index.as_query_engine(similarity_top_k=15)
         return placeholder
 
+    gr.Markdown(""" ### 1 / Extract data from URL """)
+
     with gr.Row():
         url_msg = gr.Textbox(placeholder=placeholder_url, container=False, scale=7)
-        url_btn = gr.Button(value="Set-up API and process url ✅", interactive=True)
-        url_btn.click(setup_with_url, [url_msg], msg, show_progress= "full")
+        url_btn = gr.Button(value="Process url ✅", interactive=True)
+        url_btn.click(setup_with_url, [url_msg], url_msg, show_progress= "full")
+    
+    gr.Markdown(""" ### 2 / Ask a question about this context """)
+
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox(placeholder=placeholder)
+    clear = gr.ClearButton([msg, chatbot])
 
     def respond(message, chat_history):
         response = query_engine.query(message)