Update ui/gradio_ui.py

ui/gradio_ui.py

@@ -1,88 +1,88 @@
-import gradio as gr
-from ChatErector import conversation, initializer
-
-def ui():
-    # 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 ui:
-        #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>
-        """)
-        with gr.Row():
-            with gr.Column(scale = 86):
-                gr.Markdown("<b>Step 1 - Upload PDF documents and Initialize RAG pipeline</b>")
-                with gr.Row():
-                    document = gr.Files(height=300, file_count="multiple", file_types=["pdf"], interactive=True, label="Upload PDF documents")
-                #with gr.Row():
-                    #db_btn = gr.Button("Create vector database")
-                #with gr.Row():
-                        #db_progress = gr.Textbox(value="Not initialized", show_label=False) # label="Vector database status", 
-                #gr.Markdown("<style>body { font-size: 16px; }</style><b>Advanced settings</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("Advanced settings", 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():
-                            thold = gr.Slider(minimum = 0.01, maximum = 1.0, value=0.8, step=0.1, label="Treshold", info="Retrieved information relevance level", interactive=True)
-                with gr.Row():
-                    qachain_btn = gr.Button("Initialize Question Answering Chatbot")
-                with gr.Row():
-                        llm_progress = gr.Textbox(value="Not initialized", show_label=False) # label="Chatbot status", 
-
-            with gr.Column(scale = 200):
-                gr.Markdown("<b>Step 2 - Chat with your Document</b>")
-                chatbot = gr.Chatbot(height=505)
-                #with gr.Accordion("Relevent context from the source document", 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="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(initializer, \
-            inputs=[document, slider_temperature, slider_maxtokens, slider_topk, thold], \
-            outputs=[qa_chain, llm_progress]).then(lambda:[None,"",0,"",0,"",0], \
-            inputs=None, \
-            outputs=[chatbot] \ 
-            queue=False)
-
-        # Chatbot events
-        msg.submit(conversation, \
-            inputs=[qa_chain, msg, chatbot], \
-            outputs=[qa_chain, msg, chatbot], \
-            queue=False)
-        submit_btn.click(conversation, \
-            inputs=[qa_chain, msg, chatbot], \
-            outputs=[qa_chain, msg, chatbot], \
-            queue=False)
-        clear_btn.click(lambda:[None,"",0,"",0,"",0], \
-            inputs=None, \
-            outputs=[chatbot], \
-            queue=False)
-    ui.queue().launch(debug=True)
-
-
-
-
-
+import gradio as gr
+from ChatErector import conversation, initializer
+
+def ui():
+    # 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 ui:
+        #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>
+        """)
+        with gr.Row():
+            with gr.Column(scale = 86):
+                gr.Markdown("<b>Step 1 - Upload PDF documents and Initialize RAG pipeline</b>")
+                with gr.Row():
+                    document = gr.Files(height=300, file_count="multiple", file_types=["pdf"], interactive=True, label="Upload PDF documents")
+                #with gr.Row():
+                    #db_btn = gr.Button("Create vector database")
+                #with gr.Row():
+                        #db_progress = gr.Textbox(value="Not initialized", show_label=False) # label="Vector database status", 
+                #gr.Markdown("<style>body { font-size: 16px; }</style><b>Advanced settings</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("Advanced settings", 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():
+                            thold = gr.Slider(minimum = 0.01, maximum = 1.0, value=0.8, step=0.1, label="Treshold", info="Retrieved information relevance level", interactive=True)
+                with gr.Row():
+                    qachain_btn = gr.Button("Initialize Question Answering Chatbot")
+                with gr.Row():
+                        llm_progress = gr.Textbox(value="Not initialized", show_label=False) # label="Chatbot status", 
+
+            with gr.Column(scale = 200):
+                gr.Markdown("<b>Step 2 - Chat with your Document</b>")
+                chatbot = gr.Chatbot(height=505)
+                #with gr.Accordion("Relevent context from the source document", 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="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(initializer, \
+            inputs=[document, slider_temperature, slider_maxtokens, slider_topk, thold], \
+            outputs=[qa_chain, llm_progress]).then(lambda:[None,"",0,"",0,"",0], \
+            inputs=None, \
+            outputs=[chatbot], \ 
+            queue=False)
+
+        # Chatbot events
+        msg.submit(conversation, \
+            inputs=[qa_chain, msg, chatbot], \
+            outputs=[qa_chain, msg, chatbot], \
+            queue=False)
+        submit_btn.click(conversation, \
+            inputs=[qa_chain, msg, chatbot], \
+            outputs=[qa_chain, msg, chatbot], \
+            queue=False)
+        clear_btn.click(lambda:[None,"",0,"",0,"",0], \
+            inputs=None, \
+            outputs=[chatbot], \
+            queue=False)
+    ui.queue().launch(debug=True)
+
+
+
+
+