lilyhof/CLAS-basic-interaction-exercise4

1import os2import gradio as gr3import pandas as pd4from functools import partial5from ai_classroom_suite.UIBaseComponents import *6 7### User Interface Chatbot Functions ###8def get_tutor_reply(chat_tutor):9  chat_tutor.get_tutor_reply()10  return gr.update(value="", interactive=True), chat_tutor.conversation_memory, chat_tutor11 12def get_conversation_history(chat_tutor):13    return chat_tutor.conversation_memory, chat_tutor14    15### Instructor Interface Helper Functions ###16def get_instructor_prompt(fileobj):17    # get file path18    file_path = fileobj.name19    with open(file_path, "r") as f: 20        instructor_prompt = f.read()21    return instructor_prompt22 23def embed_prompt(prompt, chat_tutor):24    # update secret25    os.environ["SECRET_PROMPT"] = prompt26    # update tutor27    chat_tutor.learning_objectives = prompt28    return os.environ.get("SECRET_PROMPT"), chat_tutor29    30### User Interfaces ###31with gr.Blocks() as demo:32    #initialize tutor (with state)33    study_tutor = gr.State(SlightlyDelusionalTutor())34 35    # Student interface36    with gr.Tab("For Students"): 37        38        # Chatbot interface39        gr.Markdown("""40        ## Chat with the Model41        Description here42        """)43    44        with gr.Row(equal_height=True):45            with gr.Column(scale=2):46              chatbot = gr.Chatbot()47              with gr.Row():48                user_chat_input = gr.Textbox(label="User input", scale=9)49                user_chat_submit = gr.Button("Ask/answer model", scale=1)50 51        # First add user's message to the conversation history52        # Then get reply from the tutor and add that to the conversation history53        user_chat_submit.click(54            fn = add_user_message, inputs = [user_chat_input, study_tutor], outputs = [user_chat_input, chatbot, study_tutor], queue=False55        ).then(56            fn = get_tutor_reply, inputs = [study_tutor], outputs = [user_chat_input, chatbot, study_tutor], queue=True57        )58 59        # Testing the chat history storage, can be deleted at deployment60        with gr.Blocks():61            test_btn = gr.Button("View your chat history")62            chat_history = gr.JSON(label = "conversation history")63            test_btn.click(get_conversation_history, inputs=[study_tutor], outputs=[chat_history, study_tutor])64 65        # Download conversation history file66        with gr.Blocks():67            gr.Markdown("""68            ## Export Your Chat History69            Export your chat history as a .json, .txt, or .csv file70            """)71            with gr.Row():72                export_dialogue_button_json = gr.Button("JSON")73                export_dialogue_button_txt = gr.Button("TXT")74                export_dialogue_button_csv = gr.Button("CSV")75        76            file_download = gr.Files(label="Download here", file_types=['.json', '.txt', '.csv'], type="file", visible=False)77        78        export_dialogue_button_json.click(save_json, study_tutor, file_download, show_progress=True)79        export_dialogue_button_txt.click(save_txt, study_tutor, file_download, show_progress=True)80        export_dialogue_button_csv.click(save_csv, study_tutor, file_download, show_progress=True)81 82    # Instructor interface83    with gr.Tab("Instructor Only"):84        """85        API Authentication functionality86        Instead of ask students to provide key, the key is now provided by the instructor. 87        To permanently set the key, go to Settings -> Variables and secrets -> Secrets, 88        then replace OPENAI_API_KEY value with whatever openai key of the instructor.89        """90        api_input = gr.Textbox(show_label=False, type="password", visible=False, value=os.environ.get("OPENAI_API_KEY"))91 92        # Upload secret prompt functionality93        # The instructor will provide a secret prompt/persona to the tutor94        with gr.Blocks():95            # testing purpose, change visible to False at deployment96            view_secret = gr.Textbox(label="Current secret prompt", value=os.environ.get("SECRET_PROMPT"), visible=False)97 98            # Prompt instructor to upload the secret file99            file_input = gr.File(label="Load a .txt or .py file", file_types=['.py', '.txt'], type="file", elem_classes="short-height")100            101            # Verify prompt content102            instructor_prompt = gr.Textbox(label="Verify your prompt content", visible=True)103            file_input.upload(fn=get_instructor_prompt, inputs=file_input, outputs=instructor_prompt)104 105            # Placeholders components106            text_input_none = gr.Textbox(visible=False)107            file_input_none = gr.File(visible=False)108            instructor_input_none = gr.TextArea(visible=False)109            learning_objectives_none = gr.Textbox(visible=False)110 111            # Set the secret prompt in this session and embed it to the study tutor112            prompt_submit_btn = gr.Button("Submit")113            prompt_submit_btn.click(114                fn=embed_prompt, inputs=[instructor_prompt, study_tutor], outputs=[view_secret, study_tutor]115            ).then(116                fn=create_reference_store, 117                inputs=[study_tutor, prompt_submit_btn, instructor_prompt, file_input_none, instructor_input_none, api_input, instructor_prompt],118                outputs=[study_tutor, prompt_submit_btn]119            )120            121            # TODO: The instructor prompt is now only set in session if not go to Settings/secret, 122            # to "permanently" set the secret prompt not seen by the students who use this space, 123            # one possible way is to recreate the instructor interface in another space, 124            # and load it here to chain with the student interface125            126            # TODO: Currently, the instructor prompt is handled as text input and stored in the vector store (and in the learning objective),127            # which means the tutor now is still a question-answering tutor who viewed the prompt as context (but not really acting based on it). 128            # We need to find a way to provide the prompt directly to the model and set its status. 129    130demo.queue().launch(server_name='0.0.0.0', server_port=7860)