update interface

app.py

@@ -5,11 +5,6 @@ from retriever import *
 from chain import *
 import gradio as gr
 
-def chatbot(query):
-    llm_response = qa_chain.run({"query": query})
-    return llm_response
-
-
 def load_embeddings_database_from_disk(persistence_directory, embeddings_generator):
     """
     Load a Chroma vector database from disk.
@@ -44,8 +39,31 @@ retriever = initialize_document_retriever(topk_documents, vector_database)
 qa_chain = create_question_answering_chain(retriever)
 
 
-inputs = gr.inputs.Textbox(lines=7, label="Coversational Interface with Chat history")
-outputs = gr.outputs.Textbox(label="Reply")
+def add_text(history, text):
+    history = history + [(text, None)]
+    return history, gr.update(value="", interactive=False)
+
+
+def bot(query):
+    llm_response = qa_chain.run({"query": query[-1][0]})
+    query[-1][1] = llm_response
+    return query
+
+
+with gr.Blocks() as demo:
+    chatbot = gr.Chatbot([], elem_id="Retrieval Augmented Question Answering").style(height=750)
+
+    with gr.Row():
+        with gr.Column(scale=0.95):
+            txt = gr.Textbox(
+                show_label=False,
+                placeholder="Enter text and press enter",
+            ).style(container=False)
+
+    txt_msg = txt.submit(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(
+        bot, chatbot, chatbot
+    )
+    txt_msg.then(lambda: gr.update(interactive=True), None, txt, queue=False)
+
 
-gr.Interface(fn=chatbot, inputs=inputs, outputs=outputs, title="Retrieval Augmented Question Answering",
-             show_progress = True, theme="compact").launch(debug=True)
+demo.launch()