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.MediaVectorStores import *
+from ai_classroom_suite.UIBaseComponents import *
+
+#  default folder path
+folder_path = "context_files"
+#  default output file name
+out_file_name = "vector_store.txt"
+
+# Check if vector store file already exist on disk
+def vector_store_file_exist():
+    # Get all files in the folder
+    files = os.listdir(folder_path)
+    # Check if output file already exist in this folder
+    return (out_file_name in files)
+
+# Helper function to get all files' paths from a folder
+# Return a list of file paths except for README.txt and vector_store.txt (if exist)
+def get_filepaths_from_folder(folder_path):
+    # Store the paths of files
+    filepath_list = []
+    
+    # Check if the specified folder exists
+    if not os.path.exists(folder_path):
+        print(f"Folder '{folder_path}' does not exist.")
+        return filepath_list
+    
+    # Get all the files in the folder
+    files = os.listdir(folder_path)
+    
+    for file_name in files:
+        # Excluding README.txt and vector_store.txt
+        if file_name != "README.txt" and file_name != "vector_store.txt":
+            # Get the file path for each item
+            file_path = os.path.join(folder_path, file_name)
+            # Check if the item is a file and not a subdirectory
+            if os.path.isfile(file_path):
+                filepath_list.append(file_path)
+    
+    return filepath_list
+
+# Helper function to write content of files in a folder to output file
+def write_vector_store_to_file(out_file_name):
+    # If vector_store.txt already exist, return nothing
+    if vector_store_file_exist():
+        return gr.File(value=out_file_name, visible=False)
+ 
+    # Only try to create the vector store if vector_store.txt doesn't exist
+    else:
+        # Call the function to read files (excluding README.txt and vector_store.txt) pathes
+        filepath_list = get_filepaths_from_folder(folder_path)
+        # Extract the text out from files
+        files_content = files_to_text(filepath_list, chunk_size=100, chunk_overlap=20)
+        
+        # Write the vector_store onto the output file
+        with open(out_file_name, "w") as f:
+            for i in range(len(files_content)):
+                item = str(files_content[i]) + "\n"
+                f.write(item)
+        
+        # Show the downlodable vector store file and give instruction on upload the vector store file to disk (on HuggingFace)
+        return gr.File(title="Download your vector store file and upload it into the context_files folder under Files", 
+                       value=out_file_name, visible=True)
+
+# overwrites the original method since we don't deal with any vector stores display here
+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
+
+# To show the loading process on the button when creating vector store file
+def creating_vs_button(obj_in):
+    return gr.update(interactive=False, value='Creating Vector Store file...')
+# To show the loading process on the button when initializing tutor
+def initializing_tutor_button(obj_in):
+    return gr.update(interactive=False, value='Initializing Tutor...')
+
+
+with gr.Blocks() as ReadingQuiz:
+    #initialize tutor (with state)
+    study_tutor = gr.State(SlightlyDelusionalTutor())
+
+    # Student chatbot interface
+    gr.Markdown("""
+    ## Chat with the Model
+    Description here
+    """)
+    
+    # Instead of ask students to provide key, the key is now provided by the instructor. 
+    api_input = gr.Textbox(show_label=False, type="password", visible=False, value=os.environ.get("OPENAI_API_KEY"))
+
+    # 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)
+    
+    # Show input files
+    file_input = gr.File(label="Reading materials", value=get_filepaths_from_folder(folder_path), visible=True)
+    
+    # Show output file for vector store when needed
+    vs_file_name = gr.Text(visible=False, value=out_file_name)
+    file_output = gr.File(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
+    vs_build_button = gr.Button("Initialize Tutor")
+    vs_build_button.click(
+        fn=creating_vs_button, inputs=vs_build_button, outputs=vs_build_button
+    ).then(
+        fn=write_vector_store_to_file, inputs=[vs_file_name], outputs=[file_output]
+    ).then(
+        fn=initializing_tutor_button, inputs=[vs_build_button], outputs=[vs_build_button]
+    ).then(
+        fn=create_reference_store, 
+        inputs=[study_tutor, vs_build_button, instructor_prompt, file_output, instructor_input_none, api_input, learning_objectives_none],
+        outputs=[study_tutor, vs_build_button]
+    )
+
+    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
+    )
+    # User can also press "Enter" on keyboard to submit a message
+    user_chat_input.submit(
+        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
+    )
+
+    # 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)
+    
+ReadingQuiz.queue().launch(server_name='0.0.0.0', server_port=7860)