update(chat interface): some refactoring and resize of chatbot after gradio update

app.py

@@ -47,9 +47,6 @@ set_global_service_context(service_context)
 class AwesumIndexBuilder(IndexBuilder):
     def _load_doucments(self):
         directory = "./awesumcare_data/awesumcare_manual_data"
-        # all_files = glob.glob(os.path.join(directory, '*.md'))
-        # faq_files = [f for f in all_files if 'FAQ' in os.path.basename(f)]
-        # print(faq_files)
         dir_reader = SimpleDirectoryReader(directory, file_extractor={
             ".pdf": UnstructuredReader(),
             ".docx": UnstructuredReader(),
@@ -57,7 +54,6 @@ class AwesumIndexBuilder(IndexBuilder):
             ".md": MarkdownReader()
         },
             recursive=True,
-            # input_files=faq_files,
             exclude=["*.png", "*.pptx", "*.docx", "*.pdf"],
             file_metadata=default_file_metadata_func)
 
@@ -80,7 +76,6 @@ class AwesumIndexBuilder(IndexBuilder):
             return
         pipeline = IngestionPipeline(
             transformations=[
-                # SentenceSplitter(),
                 self.embed_model,
             ],
             vector_store=self.vector_store,
@@ -107,13 +102,6 @@ class AwesumCareToolChatbot(Chatbot):
     def _setup_index(self):
         super()._setup_index()
 
-    # def _setup_index(self):
-    #     self.index = VectorStoreIndex.from_documents(
-    #         self.documents,
-    #         service_context=self.service_context
-    #     )
-    #     super()._setup_index()
-
     def _setup_query_engine(self):
         super()._setup_query_engine()
         self.query_engine = self.index.as_query_engine(
@@ -126,7 +114,6 @@ class AwesumCareToolChatbot(Chatbot):
         return super()._setup_tools()
 
     def _setup_chat_engine(self):
-        # testing #
         from llama_index.agent import OpenAIAgent
         self.chat_engine = OpenAIAgent.from_tools(
             tools=[self.tools],
@@ -135,11 +122,6 @@ class AwesumCareToolChatbot(Chatbot):
             verbose=True
         )
         print("set up agent as chat engine")
-        # testing #
-        # self.chat_engine = self.index.as_chat_engine(
-        #     chat_mode=ChatMode.BEST,
-        #     similarity_top_k=5,
-        #     text_qa_template=CHAT_TEXT_QA_PROMPT)
         super()._setup_chat_engine()
 
 class AweSumCareContextChatbot(AwesumCareToolChatbot):
@@ -186,84 +168,30 @@ def service_setup(model_name):
     set_global_service_context(service_context)
     return LLM, EMBED_MODEL
 
+chatbot = gr.Chatbot(height=360)
 
 def vote(data: gr.LikeData):
     if data.liked:
-        gr.Info("You up-voted this response: " + data.value)
+        print("You upvoted this response: " + data.value)
     else:
-        gr.Info("You down-voted this response: " + data.value)
-
+        print("You downvoted this response: " + data.value)
 
-chatbot = gr.Chatbot()
 
-with gr.Blocks() as demo:
+with gr.Blocks(fill_height=True) as demo:
 
     gr.Markdown("# Awesum Care demo")
 
-
-
-    # with gr.Row():
-    #     model_selector = gr.Radio(
-    #         value=ChatbotVersion.CHATGPT_35.value,
-    #         choices=[ChatbotVersion.CHATGPT_35.value, ChatbotVersion.CHATGPT_4.value],
-    #         label="Select Chatbot Model (To be implemented)"
-    #     )
-
     with gr.Tab("With relevant context sent to system prompt"):
         context_interface = gr.ChatInterface(
             awesum_chatbot_context.stream_chat,
             examples=awesum_chatbot.CHAT_EXAMPLES,
+            chatbot=chatbot
         )
-        chatbot.like(vote, None, None)
-
-    # with gr.Tab("With function calling as tool to retrieve"):
-    #     function_call_interface = gr.ChatInterface(
-    #         awesum_chatbot.stream_chat,
-    #         examples=awesum_chatbot.CHAT_EXAMPLES,
-    #     )
-    #     chatbot.like(vote, None, None)
-
-
-    # with gr.Tab("Vanilla ChatGPT without modification"):
-    #     vanilla_interface = gr.ChatInterface(
-    #         awesum_chatbot_simple.stream_chat,
-    #         examples=awesum_chatbot.CHAT_EXAMPLES)
-
-    gr.Markdown("instructions:\n"
-                "\nUsing model gpt-4-preview-1106, the most advanced model now in the market.\n"
-                "\n(Note that it can be much slower than gpt-3.5, openai's api can be unstable sometimes.)\n"
-                # "\nThree Tabs:\n"
-                # "1. Relevant context: retreiving relevant documents and send to ChatGPT.\n"
-                # "2. Give tools to chatgpt to retrieve context: the most advanced, slowest (>30s to use the tools, before answering).\n"
-                # "3. Vanilla ChatGPT: self-explanatory.\n"
-                )
-    # @model_selector.change(inputs=[model_selector, chatbot], outputs=[context_interface, function_call_interface, vanilla_interface])
-    # def switch_model(model_name, my_chatbot):
-    #     print(model_name)
-    #     print(my_chatbot.config())
-    #     LLM, EMBED_MODEL = service_setup(model_name)
-    #     # global awesum_chatbot, awesum_chatbot_context, awesum_chatbot_simple
-    #     # Logic to switch models - create new instances of the chatbots with the selected model
-    #     index_builder = AwesumIndexBuilder(vdb_collection_name=VDB_COLLECTION_NAME,
-    #                                     embed_model=EMBED_MODEL,
-    #                                     is_load_from_vector_store=IS_LOAD_FROM_VECTOR_STORE)
-    #     awesum_chatbot = AwesumCareToolChatbot(model_name=model_name, index_builder=index_builder, llm=LLM)
-    #     awesum_chatbot_context = AweSumCareContextChatbot(model_name=model_name, index_builder=index_builder)
-    #     awesum_chatbot_simple = AweSumCareSimpleChatbot(model_name=model_name, index_builder=index_builder)
-    #     # return awesum_chatbot.stream_chat, awesum_chatbot_context.stream_chat, awesum_chatbot_simple.stream_chat
-
-    #     new_context_interface = gr.ChatInterface(
-    #         awesum_chatbot_context.stream_chat,
-    #     )
-    #     new_function_call_interface = gr.ChatInterface(
-    #         awesum_chatbot.stream_chat,
-    #     )
-    #     new_vanilla_interface = gr.ChatInterface(
-    #         awesum_chatbot_simple.stream_chat,
-    #     )
-    #     return new_context_interface, new_function_call_interface, new_vanilla_interface
 
+    # gr.Markdown("instructions:\n"
+    #             "\nUsing model gpt-4-preview-1106, the most advanced model now in the market.\n"
+    #             "\n(Note that it can be much slower than gpt-3.5, openai's api can be unstable sometimes.)\n"
+    #             )
 
 demo.queue()
 demo.launch(share=False, auth=("demo", os.getenv("PASSWORD")))
-