Create app.py

app.py

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+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
+    
+### Instructor Interface Helper Functions ###
+def get_instructor_prompt(fileobj):
+    # get file path
+    file_path = fileobj.name
+    with open(file_path, "r") as f: 
+        instructor_prompt = f.read()
+    return instructor_prompt
+
+def embed_prompt(prompt, chat_tutor):
+    # update secret
+    os.environ["SECRET_PROMPT"] = prompt
+    # update tutor
+    chat_tutor.learning_objectives = prompt
+    return os.environ.get("SECRET_PROMPT"), chat_tutor
+    
+### User Interfaces ###
+with gr.Blocks() as demo:
+    #initialize tutor (with state)
+    study_tutor = gr.State(SlightlyDelusionalTutor())
+
+    # Student interface
+    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)
+
+        # 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])
+
+        # 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)
+
+    # Instructor interface
+    with gr.Tab("Instructor Only"):
+        """
+        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"))
+
+        # Upload secret prompt functionality
+        # The instructor will provide a secret prompt/persona to the tutor
+        with gr.Blocks():
+            # testing purpose, change visible to False at deployment
+            view_secret = gr.Textbox(label="Current secret prompt", value=os.environ.get("SECRET_PROMPT"), visible=False)
+
+            # Prompt instructor to upload the secret file
+            file_input = gr.File(label="Load a .txt or .py file", file_types=['.py', '.txt'], type="file", elem_classes="short-height")
+            
+            # Verify prompt content
+            instructor_prompt = gr.Textbox(label="Verify your prompt content", visible=True)
+            file_input.upload(fn=get_instructor_prompt, inputs=file_input, outputs=instructor_prompt)
+
+            # 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("Submit")
+            prompt_submit_btn.click(
+                fn=embed_prompt, inputs=[instructor_prompt, study_tutor], outputs=[view_secret, study_tutor]
+            ).then(
+                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]
+            )
+            
+            # TODO: The instructor prompt is now only set in session if not go to Settings/secret, 
+            # to "permanently" set the secret prompt not seen by the students who use this space, 
+            # one possible way is to recreate the instructor interface in another space, 
+            # and load it here to chain with the student interface
+            
+            # TODO: Currently, the instructor prompt is handled as text input and stored in the vector store (and in the learning objective),
+            # which means the tutor now is still a question-answering tutor who viewed the prompt as context (but not really acting based on it). 
+            # We need to find a way to provide the prompt directly to the model and set its status. 
+    
+demo.queue().launch(server_name='0.0.0.0', server_port=7860)