Update app.py

app.py

@@ -137,24 +137,12 @@ def demo():
         vector_db = gr.State()
         qa_chain = gr.State()
 
-        # Inject custom CSS to limit left column width
-        gr.HTML("""
-        <style>
-          .gr-block.gr-box { display: flex; }
-          .gr-block.gr-box > div:first-child {
-              max-width: 180px !important;
-              flex: 0 0 180px !important;
-            }
-        </style>
-        <center><h1>RAG PDF chatbot</h1></center>
-        """)
-
-
+        gr.HTML("<center><h1>RAG PDF chatbot</h1><center>")
         gr.Markdown("""<b>Query your PDF documents!</b> This AI agent is designed to perform retrieval augmented generation (RAG) on PDF documents. The app is hosted on Hugging Face Hub for the sole purpose of demonstration. <b>Please do not upload confidential documents.</b>""")
 
         with gr.Row():
-            # Left Column (force narrow with CSS)
-            with gr.Column(scale=1) as left_col:
+            # Left Column (thin)
+            with gr.Column(scale=1):
                 gr.Markdown("<b>Step 1 - Upload PDF documents and Initialize RAG pipeline</b>")
                 document = gr.Files(height=140, file_count="multiple", file_types=[".pdf"], interactive=True, label="Upload PDF documents")
                 db_btn = gr.Button("Create vector DB")
@@ -171,8 +159,6 @@ def demo():
                 qachain_btn = gr.Button("Initialize QA Chatbot")
                 llm_progress = gr.Textbox(value="Not initialized", show_label=False)
 
-            
-
             # Right Column (wide)
             with gr.Column(scale=8):
                 gr.Markdown("<b>Step 2 - Chat with your Document</b>")
@@ -209,7 +195,5 @@ def demo():
     demo.queue().launch(debug=True)
 
 
-
-
 if __name__ == "__main__":
     demo()