Merge pull request #201 from vanderbilt-data-science/189-prompt-fix

Dockerfile

@@ -30,4 +30,4 @@ WORKDIR $HOME/app
 # Copy the current directory contents into the container at $HOME/app setting the owner to the user
 COPY --chown=user . $HOME/app
 
-CMD ["python", "app.py"]
+CMD ["python", "app.py"]

app.py

@@ -1,89 +1,11 @@
-import os
-import gradio as gr
-import pandas as pd
-from functools import partial
-from ai_classroom_suite.UIBaseComponents import *
-
-### User Interface Chatbot Functions ###
-def get_tutor_reply(chat_tutor):
-  chat_tutor.get_tutor_reply()
-  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
-
-### User Interfaces ###
-with gr.Blocks() as demo:
-    #initialize tutor (with state)
-    study_tutor = gr.State(SlightlyDelusionalTutor())
-
-    # Student chatbot interface
-    gr.Markdown("""
-    ## Chat with the Model
-    Description here
-    """)
-    
-    """
-    API Authentication functionality
-    Instead of ask students to provide key, the key is now provided by the instructor. 
-    To permanently set the key, go to Settings -> Variables and secrets -> Secrets, 
-    then replace OPENAI_API_KEY value with whatever openai key of the instructor.
-    """
-    api_input = gr.Textbox(show_label=False, type="password", visible=False, value=os.environ.get("OPENAI_API_KEY"))
+###Autogenerated code from internal Makefile
+### Sourced from file: Apps/ui_nbs/BasicInteractionApp.ipynb
 
-    # The instructor will provide a secret prompt/persona to the tutor
-    instructor_prompt = gr.Textbox(label="Verify your prompt content", value = os.environ.get("SECRET_PROMPT"), visible=False)
-    
-    # Placeholders components
-    text_input_none = gr.Textbox(visible=False)
-    file_input_none = gr.File(visible=False)
-    instructor_input_none = gr.TextArea(visible=False)
-    learning_objectives_none = gr.Textbox(visible=False)
-
-    # Set the secret prompt in this session and embed it to the study tutor
-    prompt_submit_btn = gr.Button("Initialize Tutor")
-    prompt_submit_btn.click(
-        fn=create_reference_store, 
-        inputs=[study_tutor, prompt_submit_btn, instructor_prompt, file_input_none, instructor_input_none, api_input, instructor_prompt],
-        outputs=[study_tutor, prompt_submit_btn]
-    )
-
-    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)
-
-    # First add user's message to the conversation history
-    # Then get reply from the tutor and add that to the conversation history
-    user_chat_submit.click(
-        fn = add_user_message, inputs = [user_chat_input, study_tutor], outputs = [user_chat_input, chatbot, study_tutor], queue=False
-    ).then(
-        fn = get_tutor_reply, inputs = [study_tutor], outputs = [user_chat_input, chatbot, study_tutor], queue=True
-    )
-
-    # Testing the chat history storage, can be deleted at deployment
-    with gr.Blocks():
-        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])
+#| to_script
+# Import the needed components
+from ai_classroom_suite.UIBaseComponents import *
+from ai_classroom_suite.BasicInteraction import *
+from ai_classroom_suite.BasicInteraction import BasicInteractionDemo
 
-    # Download conversation history file
-    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)
-            
-demo.queue().launch(server_name='0.0.0.0', server_port=7860)
+# Launch the relevant component
+BasicInteractionDemo.queue().launch(server_name='0.0.0.0', server_port=7860, show_error=True)

requirements.txt

@@ -2,7 +2,9 @@ langchain
 pandas
 numpy
 openai
-gradio
+gradio>3.5,<4.0
 chromadb
 tiktoken
-unstructured
+unstructured
+poppler-utils
+unstructured[pdf]