Update app.py

app.py

@@ -1,6 +1,5 @@
 import gradio as gr
 import time
-import os
 from langchain.chat_models import ChatOpenAI
 from langchain.prompts.chat import (
     ChatPromptTemplate,
@@ -8,6 +7,7 @@ from langchain.prompts.chat import (
     HumanMessagePromptTemplate,
 )
 from langchain.schema import HumanMessage, SystemMessage
+from gradio import HuggingFaceLogger
 
 # Initialize the chat model
 chat = ChatOpenAI(model="gpt-3.5-turbo-16k")
@@ -26,17 +26,19 @@ chat_prompt = ChatPromptTemplate.from_messages(
     [system_message_prompt, human_message_prompt]
 )
 
-with gr.Blocks() as demo:
-    chatbot = gr.Chatbot()
-    msg = gr.Textbox()
-    flag = gr.Button("Flag")
+# Setup HuggingFace flagging callback
+hf_flagger = HuggingFaceLogger("CenterFor/HeyJesus_Feedback", api_key=HF_TOKEN)
 
-    # Initialize HuggingFaceDatasetSaver
-    HF_TOKEN = os.getenv('HF_TOKEN')  # replace 'HF_TOKEN' with the name of your environment variable
-    hf_writer = gr.HuggingFaceDatasetSaver(HF_TOKEN, "CenterFor/HeyJesus_Feedback")
+# Define examples
+examples = [
+    ["Who is Michael of Nebadon?"],
+    ["What is Nebadon?"],
+]
 
-    # This needs to be called at some point prior to the first call to hf_writer.flag()
-    hf_writer.setup([chatbot, msg], "flagged_conversations")
+with gr.Blocks(examples=examples) as demo:
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox()
+    clear = gr.ClearButton([msg, chatbot])
 
     def user(user_message, history):
         return gr.update(value="", interactive=False), history + [[user_message, None]]
@@ -58,8 +60,10 @@ with gr.Blocks() as demo:
     )
     response.then(lambda: gr.update(interactive=True), None, [msg], queue=False)
 
-    # We can choose which components to flag -- in this case, we'll flag all of them
-    flag.click(lambda *args: hf_writer.flag(args), [chatbot, msg], None, preprocess=False)
+    # Flagging button
+    flag = gr.Button('Flag')
+    hf_flagger.setup([chatbot, msg], "flagged_conversations")
+    flag.click(lambda: hf_flagger.flag([chatbot, msg]), [chatbot, msg], None, preprocess=False)
 
 demo.queue()
 demo.launch()