app.py

import os
import gradio as gr
import pandas as pd
from functools import partial
from ai_classroom_suite.UIBaseComponents import *

# history is a list of list  
# [[user_input_str, bot_response_str], ...]

class EchoingTutor(SlightlyDelusionalTutor):
    def add_user_message(self, user_message):
        self.conversation_memory.append([user_message, None])
        self.flattened_conversation = self.flattened_conversation + '\n\n' + 'User: ' + user_message

    def get_tutor_reply(self, user_message):
        # get tutor message
        tutor_message = "You said: " + user_message
        # add tutor message to conversation memory
        self.conversation_memory[-1][1] = tutor_message
        self.flattened_conversation = self.flattened_conversation + '\nAI: ' + tutor_message

    def forget_conversation(self):
        self.conversation_memory = []
        self.flattened_conversation = ''


### Chatbot Functions ###
def add_user_message(user_message, chat_tutor):
  """Display user message and update chat history to include it."""
  chat_tutor.add_user_message(user_message)
  return chat_tutor.conversation_memory, chat_tutor

def get_tutor_reply(user_message, chat_tutor):
  chat_tutor.get_tutor_reply(user_message)
  return gr.update(value="", interactive=True), chat_tutor.conversation_memory, chat_tutor

def get_conversation_history(chat_tutor):
    return chat_tutor.conversation_memory, chat_tutor

def get_instructor_prompt(fileobj):
    file_path = fileobj.name
    f = open(file_path, "r")
    instructor_prompt = f.read()
    return instructor_prompt


with gr.Blocks() as demo:
    #initialize tutor (with state)
    study_tutor = gr.State(EchoingTutor())

    # Instead of ask students to provide key, the key is now directly provided by the instructor
    api_input = os.environ.get("OPENAI_API_KEY")
    embed_key(api_input, study_tutor)

    with gr.Tab("For Students"): 
        # Chatbot interface
        gr.Markdown("""
        ## Chat with the Model
        Description here
        """)
    
        with gr.Row(equal_height=True):
            with gr.Column(scale=2):
              chatbot = gr.Chatbot()
              with gr.Row():
                user_chat_input = gr.Textbox(label="User input", scale=9)
                user_chat_submit = gr.Button("Ask/answer model", scale=1)
        
        user_chat_submit.click(add_user_message, 
                               [user_chat_input, study_tutor], 
                               [chatbot, study_tutor], queue=False).then(
            get_tutor_reply, [user_chat_input, study_tutor], [user_chat_input, chatbot, study_tutor], queue=True)

        # Testing purpose
        test_btn = gr.Button("View your chat history")
        chat_history = gr.JSON(label = "conversation history")
        test_btn.click(get_conversation_history, inputs=[study_tutor], outputs=[chat_history, study_tutor])

        with gr.Blocks():
            gr.Markdown("""
            ## Export Your Chat History
            Export your chat history as a .json, .txt, or .csv file
            """)
            with gr.Row():
                export_dialogue_button_json = gr.Button("JSON")
                export_dialogue_button_txt = gr.Button("TXT")
                export_dialogue_button_csv = gr.Button("CSV")
        
            file_download = gr.Files(label="Download here", file_types=['.json', '.txt', '.csv'], type="file", visible=False)
        
        export_dialogue_button_json.click(save_json, study_tutor, file_download, show_progress=True)
        export_dialogue_button_txt.click(save_txt, study_tutor, file_download, show_progress=True)
        export_dialogue_button_csv.click(save_csv, study_tutor, file_download, show_progress=True)
        
    with gr.Tab("Instructor Only"):
        with gr.Blocks():
            file_input = gr.File(label="Load a .txt or .py file",
                            file_types=['.py', '.txt'], type="file",
                            elem_classes="short-height")
            instructor_prompt = file_input.name
            gr.Textbox(value=instructor_prompt)
            
    
demo.queue().launch(server_name='0.0.0.0', server_port=7860)