Update app.py

app.py

@@ -148,7 +148,7 @@ def demo():
         collection_name = gr.State()
 
         gr.Markdown(
-            """<center><h2>PDF-based chatbot</center></h2>
+            """<center><h2>Haor Chatbot by Tasrif Nur Himel</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. \
@@ -157,50 +157,50 @@ def demo():
             <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.
             """)
 
-        gr.Markdown("<h4>Step 1 - Process and Load Documents from 'data' Folder</h4>")
-        with gr.Row():
-            slider_chunk_size = gr.Slider(minimum=100, maximum=1000, value=600, step=20, label="Chunk size",
-                                          info="Chunk size", interactive=True)
-        with gr.Row():
-            slider_chunk_overlap = gr.Slider(minimum=10, maximum=200, value=40, step=10, label="Chunk overlap",
-                                             info="Chunk overlap", interactive=True)
-        with gr.Row():
-            db_progress = gr.Textbox(label="Vector database initialization", value="None")
-        with gr.Row():
-            db_btn = gr.Button("Generate vector database")
-
-        gr.Markdown("<h4>Step 2 - Initialize QA chain</h4>")
-        with gr.Row():
-            slider_temperature = gr.Slider(minimum=0.01, maximum=1.0, value=0.7, step=0.1, label="Temperature",
-                                           info="Model temperature", interactive=True)
-        with gr.Row():
-            slider_maxtokens = gr.Slider(minimum=224, maximum=4096, value=1024, step=32, label="Max Tokens",
-                                         info="Model max tokens", interactive=True)
-        with gr.Row():
-            slider_topk = gr.Slider(minimum=1, maximum=10, value=3, step=1, label="top-k samples",
-                                    info="Model top-k samples", interactive=True)
-        with gr.Row():
-            llm_progress = gr.Textbox(value="None", label="QA chain initialization")
-        with gr.Row():
-            qachain_btn = gr.Button("Initialize Question Answering chain")
-
-        gr.Markdown("<h4>Step 3 - Chatbot</h4>")
-        chatbot = gr.Chatbot(height=300)
-        with gr.Accordion("Advanced - Document references", open=False):
+        with gr.Tab("Step 1 - Process and Load Document"):
             with gr.Row():
-                doc_source1 = gr.Textbox(label="Reference 1", lines=2, container=True, scale=20)
-                source1_page = gr.Number(label="Page", scale=1)
+                slider_chunk_size = gr.Slider(minimum=100, maximum=1000, value=600, step=20, label="Chunk size",
+                                              info="Chunk size", interactive=True)
             with gr.Row():
-                doc_source2 = gr.Textbox(label="Reference 2", lines=2, container=True, scale=20)
-                source2_page = gr.Number(label="Page", scale=1)
+                slider_chunk_overlap = gr.Slider(minimum=10, maximum=200, value=40, step=10, label="Chunk overlap",
+                                                 info="Chunk overlap", interactive=True)
             with gr.Row():
-                doc_source3 = gr.Textbox(label="Reference 3", lines=2, container=True, scale=20)
-                source3_page = gr.Number(label="Page", scale=1)
-        with gr.Row():
-            msg = gr.Textbox(placeholder="Type message (e.g. 'What is this document about?')", container=True)
-        with gr.Row():
-            submit_btn = gr.Button("Submit message")
-            clear_btn = gr.ClearButton([msg, chatbot], value="Clear conversation")
+                db_progress = gr.Textbox(label="Vector database initialization", value="None")
+            with gr.Row():
+                db_btn = gr.Button("Generate vector database")
+
+        with gr.Tab("Step 2 - Initialize QA chain"):
+            with gr.Row():
+                slider_temperature = gr.Slider(minimum=0.01, maximum=1.0, value=0.7, step=0.1, label="Temperature",
+                                               info="Model temperature", interactive=True)
+            with gr.Row():
+                slider_maxtokens = gr.Slider(minimum=224, maximum=4096, value=1024, step=32, label="Max Tokens",
+                                             info="Model max tokens", interactive=True)
+            with gr.Row():
+                slider_topk = gr.Slider(minimum=1, maximum=10, value=3, step=1, label="top-k samples",
+                                        info="Model top-k samples", interactive=True)
+            with gr.Row():
+                llm_progress = gr.Textbox(value="None", label="QA chain initialization")
+            with gr.Row():
+                qachain_btn = gr.Button("Initialize Question Answering chain")
+
+        with gr.Tab("Step 3 - Chatbot"):
+            chatbot = gr.Chatbot(height=300)
+            with gr.Accordion("Advanced - Document references", open=False):
+                with gr.Row():
+                    doc_source1 = gr.Textbox(label="Reference 1", lines=2, container=True, scale=20)
+                    source1_page = gr.Number(label="Page", scale=1)
+                with gr.Row():
+                    doc_source2 = gr.Textbox(label="Reference 2", lines=2, container=True, scale=20)
+                    source2_page = gr.Number(label="Page", scale=1)
+                with gr.Row():
+                    doc_source3 = gr.Textbox(label="Reference 3", lines=2, container=True, scale=20)
+                    source3_page = gr.Number(label="Page", scale=1)
+            with gr.Row():
+                msg = gr.Textbox(placeholder="Type message (e.g. 'What is this document about?')", container=True)
+            with gr.Row():
+                submit_btn = gr.Button("Submit message")
+                clear_btn = gr.ClearButton([msg, chatbot], value="Clear conversation")
 
         db_btn.click(initialize_database, \
                      inputs=[slider_chunk_size, slider_chunk_overlap], \