Update app.py

app.py

@@ -246,55 +246,55 @@ def demo():
         When generating answers, it takes past questions into account (via conversational memory), and includes document references for clarity purposes.</i>
         <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 an output.<br>
         """)
-        with gr.Tab("Step 1 - Document pre-processing"):
-            with gr.Row():
-                document = gr.Files(height=100, file_count="multiple", file_types=["pdf"], interactive=True, label="Upload your PDF documents (single or multiple)")
-                # upload_btn = gr.UploadButton("Loading document...", height=100, file_count="multiple", file_types=["pdf"], scale=1)
-            with gr.Row():
-                db_btn = gr.Radio(["ChromaDB"], label="Vector database type", value = "ChromaDB", type="index", info="Choose your vector database")
-            with gr.Accordion("Advanced options - Document text splitter", open=False):
-                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():
+            document = gr.Files(height=100, file_count="multiple", file_types=["pdf"], interactive=True, label="Upload your PDF documents (single or multiple)")
+            # upload_btn = gr.UploadButton("Loading document...", height=100, file_count="multiple", file_types=["pdf"], scale=1)
+        with gr.Row():
+            db_btn = gr.Radio(["ChromaDB"], label="Vector database type", value = "ChromaDB", type="index", info="Choose your vector database")
+        with gr.Accordion("Advanced options - Document text splitter", open=False):
             with gr.Row():
-                db_progress = gr.Textbox(label="Vector database initialization", value="None")
+                slider_chunk_size = gr.Slider(minimum = 100, maximum = 1000, value=600, step=20, label="Chunk size", info="Chunk size", interactive=True)
             with gr.Row():
-                db_btn = gr.Button("Generate vector database...")
+                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...")
             
-        with gr.Tab("Step 2 - QA chain initialization"):
+
+        with gr.Row():
+            llm_btn = gr.Radio(list_llm_simple, \
+                label="LLM models", value = list_llm_simple[0], type="index", info="Choose your LLM model")
+        with gr.Accordion("Advanced options - LLM model", open=False):
             with gr.Row():
-                llm_btn = gr.Radio(list_llm_simple, \
-                    label="LLM models", value = list_llm_simple[0], type="index", info="Choose your LLM model")
-            with gr.Accordion("Advanced options - LLM model", open=False):
-                with gr.Row():
-                    slider_temperature = gr.Slider(minimum = 0.0, 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)
+                slider_temperature = gr.Slider(minimum = 0.0, maximum = 1.0, value=0.7, step=0.1, label="Temperature", info="Model temperature", interactive=True)
             with gr.Row():
-                llm_progress = gr.Textbox(value="None",label="QA chain initialization")
+                slider_maxtokens = gr.Slider(minimum = 224, maximum = 4096, value=1024, step=32, label="Max Tokens", info="Model max tokens", interactive=True)
             with gr.Row():
-                qachain_btn = gr.Button("Initialize question-answering chain...")
+                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 - Conversation with 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)
+        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():
-                msg = gr.Textbox(placeholder="Type message", container=True)
+                doc_source2 = gr.Textbox(label="Reference 2", lines=2, container=True, scale=20)
+                source2_page = gr.Number(label="Page", scale=1)
             with gr.Row():
-                submit_btn = gr.Button("Submit")
-                clear_btn = gr.ClearButton([msg, chatbot])
+                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", container=True)
+        with gr.Row():
+            submit_btn = gr.Button("Submit")
+            clear_btn = gr.ClearButton([msg, chatbot])
             
         # Preprocessing events
         #upload_btn.upload(upload_file, inputs=[upload_btn], outputs=[document])