Update app.py

app.py

@@ -133,40 +133,34 @@ def upload_file(file_obj):
 
 
 def demo():
-    # with gr.Blocks(theme=gr.themes.Default(primary_hue="sky")) as demo:
-    with gr.Blocks(theme=gr.themes.Default(primary_hue="red", secondary_hue="pink", neutral_hue = "sky")) as demo:
+    with gr.Blocks(theme=gr.themes.Default(primary_hue="red", secondary_hue="pink", neutral_hue="sky")) as demo:
         vector_db = gr.State()
         qa_chain = gr.State()
+
         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>
-        """)
+        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():
-            with gr.Column(scale = 1):
+            # Left Column (thin)
+            with gr.Column(scale=1):
                 gr.Markdown("<b>Step 1 - Upload PDF documents and Initialize RAG pipeline</b>")
-                with gr.Row():
-                    document = gr.Files(height=140, file_count="multiple", file_types=[".pdf"], interactive=True, label="Upload PDF documents")
-                with gr.Row():
-                    db_btn = gr.Button("Create vector DB")
-                with gr.Row():
-                        db_progress = gr.Textbox(value="Not initialized", show_label=False) # label="Vector database status", 
+                document = gr.Files(height=140, file_count="multiple", file_types=[".pdf"], interactive=True, label="Upload PDF documents")
+                db_btn = gr.Button("Create vector DB")
+                db_progress = gr.Textbox(value="Not initialized", show_label=False)
+
                 gr.Markdown("<style>body { font-size: 16px; }</style><b>Select Large Language Model (LLM) and input parameters</b>")
-                with gr.Row():
-                    llm_btn = gr.Radio(list_llm_simple, label="Available LLMs", value = list_llm_simple[0], type="index") # info="Select LLM", show_label=False
-                with gr.Row():
-                    with gr.Accordion("LLM input parameters", open=False):
-                        with gr.Row():
-                            slider_temperature = gr.Slider(minimum = 0.01, maximum = 1.0, value=0.5, step=0.1, label="Temperature", info="Controls randomness in token generation", interactive=True)
-                        with gr.Row():
-                            slider_maxtokens = gr.Slider(minimum = 128, maximum = 9192, value=4096, step=128, label="Max New Tokens", info="Maximum number of tokens to be generated",interactive=True)
-                        with gr.Row():
-                                slider_topk = gr.Slider(minimum = 1, maximum = 10, value=3, step=1, label="top-k", info="Number of tokens to select the next token from", interactive=True)
-                with gr.Row():
-                    qachain_btn = gr.Button("Initialize QA Chatbot")
-                with gr.Row():
-                        llm_progress = gr.Textbox(value="Not initialized", show_label=False) # label="Chatbot status", 
+                llm_btn = gr.Radio(list_llm_simple, label="Available LLMs", value=list_llm_simple[0], type="index")
+
+                with gr.Accordion("LLM input parameters", open=False):
+                    slider_temperature = gr.Slider(minimum=0.01, maximum=1.0, value=0.5, step=0.1, label="Temperature", info="Controls randomness in token generation", interactive=True)
+                    slider_maxtokens = gr.Slider(minimum=128, maximum=9192, value=4096, step=128, label="Max New Tokens", info="Maximum number of tokens to be generated", interactive=True)
+                    slider_topk = gr.Slider(minimum=1, maximum=10, value=3, step=1, label="top-k", info="Number of tokens to select the next token from", interactive=True)
 
-            with gr.Column(scale = 5):
+                qachain_btn = gr.Button("Initialize QA Chatbot")
+                llm_progress = gr.Textbox(value="Not initialized", show_label=False)
+
+            # Right Column (wide)
+            with gr.Column(scale=5):
                 gr.Markdown("<b>Step 2 - Chat with your Document</b>")
                 chatbot = gr.Chatbot(height=480)
                 with gr.Accordion("Relevent context from the source document", open=False):
@@ -179,38 +173,27 @@ def demo():
                     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="Ask a question", container=True)
+                msg = gr.Textbox(placeholder="Ask a question", container=True)
                 with gr.Row():
                     submit_btn = gr.Button("Submit")
                     clear_btn = gr.ClearButton([msg, chatbot], value="Clear")
-            
+
         # Preprocessing events
-        db_btn.click(initialize_database, \
-            inputs=[document], \
-            outputs=[vector_db, db_progress])
-        qachain_btn.click(initialize_LLM, \
-            inputs=[llm_btn, slider_temperature, slider_maxtokens, slider_topk, vector_db], \
-            outputs=[qa_chain, llm_progress]).then(lambda:[None,"",0,"",0,"",0], \
-            inputs=None, \
-            outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \
-            queue=False)
+        db_btn.click(initialize_database, inputs=[document], outputs=[vector_db, db_progress])
+        qachain_btn.click(initialize_LLM, inputs=[llm_btn, slider_temperature, slider_maxtokens, slider_topk, vector_db], outputs=[qa_chain, llm_progress]).then(
+            lambda: [None, "", 0, "", 0, "", 0],
+            inputs=None,
+            outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page],
+            queue=False
+        )
 
         # Chatbot events
-        msg.submit(conversation, \
-            inputs=[qa_chain, msg, chatbot], \
-            outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \
-            queue=False)
-        submit_btn.click(conversation, \
-            inputs=[qa_chain, msg, chatbot], \
-            outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \
-            queue=False)
-        clear_btn.click(lambda:[None,"",0,"",0,"",0], \
-            inputs=None, \
-            outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \
-            queue=False)
+        msg.submit(conversation, inputs=[qa_chain, msg, chatbot], outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], queue=False)
+        submit_btn.click(conversation, inputs=[qa_chain, msg, chatbot], outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], queue=False)
+        clear_btn.click(lambda: [None, "", 0, "", 0, "", 0], inputs=None, outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], queue=False)
+
     demo.queue().launch(debug=True)
-    # demo.launch(debug=True)
+
 
 
 if __name__ == "__main__":