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App_Function_Libraries/Gradio_UI/Audio_ingestion_tab.py

@@ -8,7 +8,7 @@ import gradio as gr
 #
 # Local Imports
 from App_Function_Libraries.Audio_Files import process_audio_files
-from App_Function_Libraries.DB_Manager import load_preset_prompts
+from App_Function_Libraries.DB.DB_Manager import load_preset_prompts
 from App_Function_Libraries.Gradio_UI.Chat_ui import update_user_prompt
 from App_Function_Libraries.Gradio_UI.Gradio_Shared import whisper_models
 #

App_Function_Libraries/Gradio_UI/Chat_ui.py

@@ -15,7 +15,7 @@ import gradio as gr
 #
 # Local Imports
 from App_Function_Libraries.Chat import chat, save_chat_history, update_chat_content, save_chat_history_to_db_wrapper
-from App_Function_Libraries.DB_Manager import add_chat_message, search_chat_conversations, create_chat_conversation, \
+from App_Function_Libraries.DB.DB_Manager import add_chat_message, search_chat_conversations, create_chat_conversation, \
     get_chat_messages, update_chat_message, delete_chat_message, load_preset_prompts, db
 from App_Function_Libraries.Gradio_UI.Gradio_Shared import update_dropdown, update_user_prompt
 
@@ -941,12 +941,6 @@ def create_chat_management_tab():
     return search_query, search_button, conversation_list, conversation_mapping, chat_content, save_button, result_message, chat_preview
 
 
-# FIXME - busted and incomplete
-# Mock function to simulate LLM processing
-def process_with_llm(workflow, context, prompt):
-    return f"LLM output for {workflow} with context: {context[:30]}... and prompt: {prompt[:30]}..."
-
-
 # Load workflows from a JSON file
 json_path = Path('./Helper_Scripts/Workflows/Workflows.json')
 with json_path.open('r') as f:
@@ -956,61 +950,139 @@ with json_path.open('r') as f:
 # FIXME - broken Completely. Doesn't work.
 def chat_workflows_tab():
     with gr.TabItem("Chat Workflows"):
-        with gr.Blocks() as chat_workflows_block:
-            gr.Markdown("# Workflows using LLMs")
+        gr.Markdown("# Workflows using LLMs")
 
+        with gr.Row():
             workflow_selector = gr.Dropdown(label="Select Workflow", choices=[wf['name'] for wf in workflows])
-            context_input = gr.Textbox(label="Context", lines=5)
+            api_selector = gr.Dropdown(
+                label="Select API Endpoint",
+                choices=["OpenAI", "Anthropic", "Cohere", "Groq", "DeepSeek", "Mistral", "OpenRouter",
+                         "Llama.cpp", "Kobold", "Ooba", "Tabbyapi", "VLLM", "ollama", "HuggingFace"],
+                value="OpenAI"
+            )
+            api_key_input = gr.Textbox(label="API Key (optional)", type="password")
 
-            # Create lists to hold UI components
-            prompt_inputs = []
-            process_buttons = []
+        context_input = gr.Textbox(label="Initial Context (optional)", lines=5)
+
+        # Create a container for dynamic components
+        with gr.Column() as dynamic_components:
+            prompt_displays = []
+            user_inputs = []
             output_boxes = []
-            max_prompts = max(len(wf['prompts']) for wf in workflows)
-
-            # Pre-create the maximum number of prompt sections
-            for i in range(max_prompts):
-                prompt_input = gr.Textbox(label=f"Prompt {i + 1}", lines=2, visible=False)
-                output_box = gr.Textbox(label=f"Output {i + 1}", lines=5, visible=False)
-                process_button = gr.Button(f"Process Prompt {i + 1}", visible=False)
-
-                prompt_inputs.append(prompt_input)
-                output_boxes.append(output_box)
-                process_buttons.append(process_button)
-
-                process_button.click(
-                    fn=lambda context, prompt, workflow_name, step=i: process(context, prompt, workflow_name, step),
-                    inputs=[context_input, prompt_input, workflow_selector],
-                    outputs=[output_box]
-                )
+            process_buttons = []
+            regenerate_buttons = []
+
+            # Create the maximum number of components needed
+            max_steps = max(len(wf['prompts']) for wf in workflows)
+            for i in range(max_steps):
+                prompt_displays.append(gr.Markdown(visible=False))
+                user_inputs.append(gr.Textbox(label=f"Your Response", lines=2, visible=False))
+                output_boxes.append(gr.Textbox(label=f"AI Output", lines=5, visible=False))
+                with gr.Row():
+                    process_buttons.append(gr.Button(f"Process Step {i + 1}", visible=False))
+                    regenerate_buttons.append(gr.Button(f"🔄 Regenerate", visible=False))
+
+        def update_workflow_ui(workflow_name):
+            selected_workflow = next(wf for wf in workflows if wf['name'] == workflow_name)
+            num_prompts = len(selected_workflow['prompts'])
+
+            prompt_updates = []
+            input_updates = []
+            output_updates = []
+            button_updates = []
+            regenerate_updates = []
+
+            for i in range(max_steps):
+                if i < num_prompts:
+                    prompt_updates.append(
+                        gr.update(value=f"**Step {i + 1}:** {selected_workflow['prompts'][i]}", visible=True))
+                    input_updates.append(gr.update(value="", visible=True, interactive=(i == 0)))
+                    output_updates.append(gr.update(value="", visible=True))
+                    button_updates.append(gr.update(visible=(i == 0)))
+                    regenerate_updates.append(gr.update(visible=False))
+                else:
+                    prompt_updates.append(gr.update(visible=False))
+                    input_updates.append(gr.update(visible=False))
+                    output_updates.append(gr.update(visible=False))
+                    button_updates.append(gr.update(visible=False))
+                    regenerate_updates.append(gr.update(visible=False))
+
+            return prompt_updates + input_updates + output_updates + button_updates + regenerate_updates
+
+        def process(context, user_inputs, workflow_name, api_endpoint, api_key, step):
+            selected_workflow = next(wf for wf in workflows if wf['name'] == workflow_name)
+
+            # Build up the context from previous steps
+            full_context = context + "\n\n"
+            for i in range(step + 1):
+                full_context += f"Question: {selected_workflow['prompts'][i]}\n"
+                full_context += f"Answer: {user_inputs[i]}\n"
+                if i < step:
+                    full_context += f"AI Output: {output_boxes[i].value}\n\n"
+
+            result = process_with_llm(workflow_name, full_context, selected_workflow['prompts'][step], api_endpoint,
+                                      api_key)
+
+            prompt_updates = [gr.update() for _ in range(max_steps)]
+            input_updates = []
+            output_updates = [gr.update() for _ in range(max_steps)]
+            button_updates = []
+            regenerate_updates = []
+
+            for i in range(len(selected_workflow['prompts'])):
+                if i == step:
+                    regenerate_updates.append(gr.update(visible=True))
+                elif i == step + 1:
+                    input_updates.append(gr.update(interactive=True))
+                    button_updates.append(gr.update(visible=True))
+                    regenerate_updates.append(gr.update(visible=False))
+                elif i > step + 1:
+                    input_updates.append(gr.update(interactive=False))
+                    button_updates.append(gr.update(visible=False))
+                    regenerate_updates.append(gr.update(visible=False))
+                else:
+                    input_updates.append(gr.update(interactive=False))
+                    button_updates.append(gr.update(visible=False))
+                    regenerate_updates.append(gr.update(visible=True))
+
+            return [result] + prompt_updates + input_updates + output_updates + button_updates + regenerate_updates
+
+        # Set up event handlers
+        workflow_selector.change(
+            update_workflow_ui,
+            inputs=[workflow_selector],
+            outputs=prompt_displays + user_inputs + output_boxes + process_buttons + regenerate_buttons
+        )
+
+        # Set up process button click events
+        for i, button in enumerate(process_buttons):
+            button.click(
+                fn=lambda context, *user_inputs, wf_name, api_endpoint, api_key, step=i: process(context, user_inputs,
+                                                                                                 wf_name, api_endpoint,
+                                                                                                 api_key, step),
+                inputs=[context_input] + user_inputs + [workflow_selector, api_selector, api_key_input],
+                outputs=[output_boxes[
+                             i]] + prompt_displays + user_inputs + output_boxes + process_buttons + regenerate_buttons
+            )
 
-            def process(context, prompt, workflow_name, step):
-                selected_workflow = next(wf for wf in workflows if wf['name'] == workflow_name)
-                # Update context with previous outputs
-                for j in range(step):
-                    context += f"\n\n{output_boxes[j].value}"
-                result = process_with_llm(selected_workflow['name'], context, prompt)
-                return result
-
-            def update_prompt_sections(workflow_name):
-                selected_workflow = next(wf for wf in workflows if wf['name'] == workflow_name)
-                num_prompts = len(selected_workflow['prompts'])
-
-                for i in range(max_prompts):
-                    if i < num_prompts:
-                        prompt_inputs[i].visible = True
-                        prompt_inputs[i].value = selected_workflow['prompts'][i]
-                        process_buttons[i].visible = True
-                        output_boxes[i].visible = True
-                    else:
-                        prompt_inputs[i].visible = False
-                        process_buttons[i].visible = False
-                        output_boxes[i].visible = False
-
-            # Bind the workflow selector to update the UI
-            workflow_selector.change(update_prompt_sections, inputs=[workflow_selector], outputs=[])
-
-        return chat_workflows_block
+        # Set up regenerate button click events
+        for i, button in enumerate(regenerate_buttons):
+            button.click(
+                fn=lambda context, *user_inputs, wf_name, api_endpoint, api_key, step=i: process(context, user_inputs,
+                                                                                                 wf_name, api_endpoint,
+                                                                                                 api_key, step),
+                inputs=[context_input] + user_inputs + [workflow_selector, api_selector, api_key_input],
+                outputs=[output_boxes[
+                             i]] + prompt_displays + user_inputs + output_boxes + process_buttons + regenerate_buttons
+            )
+
+    return workflow_selector, api_selector, api_key_input, context_input, dynamic_components
+
+
+# Mock function to simulate LLM processing
+def process_with_llm(workflow, context, prompt, api_endpoint, api_key):
+    api_key_snippet = api_key[:5] + "..." if api_key else "Not provided"
+    return f"LLM output using {api_endpoint} (API Key: {api_key_snippet}) for {workflow} with context: {context[:30]}... and prompt: {prompt[:30]}..."
 
 #
 # End of Chat_ui.py

App_Function_Libraries/Gradio_UI/Export_Functionality.py

@@ -8,7 +8,7 @@ import shutil
 import tempfile
 from typing import List, Dict, Optional, Tuple
 import gradio as gr
-from App_Function_Libraries.DB_Manager import DatabaseError, create_automated_backup, db_path, backup_dir
+from App_Function_Libraries.DB.DB_Manager import DatabaseError, create_automated_backup, db_path, backup_dir
 from App_Function_Libraries.Gradio_UI.Gradio_Shared import fetch_item_details, fetch_items_by_keyword, browse_items
 
 logger = logging.getLogger(__name__)

App_Function_Libraries/Gradio_UI/Gradio_Shared.py

@@ -12,9 +12,9 @@ from typing import List, Tuple
 import gradio as gr
 #
 # Local Imports
-from App_Function_Libraries.DB_Manager import list_prompts, db, search_and_display, fetch_prompt_details
-from App_Function_Libraries.SQLite_DB import DatabaseError
-from App_Function_Libraries.Utils import format_transcription
+from App_Function_Libraries.DB.DB_Manager import list_prompts, db, search_and_display, fetch_prompt_details
+from App_Function_Libraries.DB.SQLite_DB import DatabaseError
+from App_Function_Libraries.Utils.Utils import format_transcription
 #
 ##############################################################################################################
 #

App_Function_Libraries/Gradio_UI/Import_Functionality.py

@@ -16,7 +16,7 @@ import gradio as gr
 import pypandoc
 #
 # Local Imports
-from App_Function_Libraries.DB_Manager import insert_prompt_to_db, load_preset_prompts, import_obsidian_note_to_db, \
+from App_Function_Libraries.DB.DB_Manager import insert_prompt_to_db, load_preset_prompts, import_obsidian_note_to_db, \
     add_media_to_database
 from App_Function_Libraries.Prompt_Handling import import_prompt_from_file, import_prompts_from_zip#
 from App_Function_Libraries.Summarization_General_Lib import perform_summarization

App_Function_Libraries/Gradio_UI/Introduction_tab.py

@@ -6,7 +6,7 @@
 # External Imports
 import gradio as gr
 
-from App_Function_Libraries.DB_Manager import get_db_config
+from App_Function_Libraries.DB.DB_Manager import get_db_config
 
 
 #

App_Function_Libraries/Gradio_UI/Keywords.py

@@ -10,7 +10,7 @@
 import gradio as gr
 #
 # Internal Imports
-from App_Function_Libraries.DB_Manager import add_keyword, delete_keyword, keywords_browser_interface, export_keywords_to_csv
+from App_Function_Libraries.DB.DB_Manager import add_keyword, delete_keyword, keywords_browser_interface, export_keywords_to_csv
 #
 #
 ######################################################################################################################

App_Function_Libraries/Gradio_UI/Media_edit.py

@@ -9,10 +9,10 @@ import uuid
 import gradio as gr
 #
 # Local Imports
-from App_Function_Libraries.DB_Manager import add_prompt, update_media_content, db, add_or_update_prompt, \
+from App_Function_Libraries.DB.DB_Manager import add_prompt, update_media_content, db, add_or_update_prompt, \
     load_prompt_details
 from App_Function_Libraries.Gradio_UI.Gradio_Shared import update_dropdown, update_prompt_dropdown
-from App_Function_Libraries.SQLite_DB import fetch_item_details
+from App_Function_Libraries.DB.SQLite_DB import fetch_item_details
 
 
 def create_media_edit_tab():
@@ -173,7 +173,7 @@ def create_media_edit_and_clone_tab():
 
 
 def create_prompt_edit_tab():
-    with gr.TabItem("Edit Prompts"):
+    with gr.TabItem("Add & Edit Prompts"):
         with gr.Row():
             with gr.Column():
                 prompt_dropdown = gr.Dropdown(

App_Function_Libraries/Gradio_UI/PDF_ingestion_tab.py

@@ -10,7 +10,7 @@ import tempfile
 import gradio as gr
 #
 # Local Imports
-from App_Function_Libraries.DB_Manager import load_preset_prompts
+from App_Function_Libraries.DB.DB_Manager import load_preset_prompts
 from App_Function_Libraries.Gradio_UI.Chat_ui import update_user_prompt
 from App_Function_Libraries.PDF_Ingestion_Lib import extract_metadata_from_pdf, extract_text_and_format_from_pdf, \
     process_and_cleanup_pdf

App_Function_Libraries/Gradio_UI/Podcast_tab.py

@@ -9,7 +9,7 @@ import gradio as gr
 #
 # Local Imports
 from App_Function_Libraries.Audio_Files import process_podcast
-from App_Function_Libraries.DB_Manager import load_preset_prompts
+from App_Function_Libraries.DB.DB_Manager import load_preset_prompts
 from App_Function_Libraries.Gradio_UI.Gradio_Shared import whisper_models, update_user_prompt
 
 

App_Function_Libraries/Gradio_UI/Re_summarize_tab.py

@@ -10,12 +10,12 @@ import gradio as gr
 #
 # Local Imports
 from App_Function_Libraries.Chunk_Lib import improved_chunking_process
-from App_Function_Libraries.DB_Manager import update_media_content, load_preset_prompts
+from App_Function_Libraries.DB.DB_Manager import update_media_content, load_preset_prompts
 from App_Function_Libraries.Gradio_UI.Chat_ui import update_user_prompt
 from App_Function_Libraries.Gradio_UI.Gradio_Shared import fetch_item_details, fetch_items_by_keyword, \
     fetch_items_by_content, fetch_items_by_title_or_url
 from App_Function_Libraries.Summarization_General_Lib import summarize_chunk
-from App_Function_Libraries.Utils import load_comprehensive_config
+from App_Function_Libraries.Utils.Utils import load_comprehensive_config
 #
 #
 ######################################################################################################################

App_Function_Libraries/Gradio_UI/Search_Tab.py

@@ -10,9 +10,9 @@ import sqlite3
 # External Imports
 import gradio as gr
 
-from App_Function_Libraries.DB_Manager import view_database, search_and_display_items
+from App_Function_Libraries.DB.DB_Manager import view_database, search_and_display_items
 from App_Function_Libraries.Gradio_UI.Gradio_Shared import update_dropdown, update_detailed_view
-from App_Function_Libraries.RAG_Libary_2 import rag_search
+from App_Function_Libraries.RAG.RAG_Libary_2 import rag_search
 
 #
 # Local Imports
@@ -96,7 +96,7 @@ def create_embeddings_tab():
         def create_embeddings(api_choice):
             try:
                 # Assuming you have a function that handles the creation of embeddings
-                from App_Function_Libraries.ChromaDB_Library import create_all_embeddings
+                from App_Function_Libraries.RAG.ChromaDB_Library import create_all_embeddings
                 status = create_all_embeddings(api_choice)
                 return status
             except Exception as e:

App_Function_Libraries/Gradio_UI/Transcript_comparison.py

@@ -8,9 +8,9 @@ import logging
 # External Imports
 import gradio as gr
 
-from App_Function_Libraries.DB_Manager import get_transcripts
+from App_Function_Libraries.DB.DB_Manager import get_transcripts
 from App_Function_Libraries.Gradio_UI.Gradio_Shared import browse_items
-from App_Function_Libraries.Utils import format_transcription
+from App_Function_Libraries.Utils.Utils import format_transcription
 
 
 #

App_Function_Libraries/Gradio_UI/Trash.py

@@ -9,7 +9,7 @@ import sqlite3
 import gradio as gr
 #
 # Local Imports
-from App_Function_Libraries.DB_Manager import delete_prompt, empty_trash, get_trashed_items, user_delete_item
+from App_Function_Libraries.DB.DB_Manager import delete_prompt, empty_trash, get_trashed_items, user_delete_item
 
 
 def delete_item(media_id, force):

App_Function_Libraries/Gradio_UI/Utilities.py

@@ -6,7 +6,7 @@ from pathlib import Path
 import gradio as gr
 import yt_dlp
 
-from App_Function_Libraries.Utils import sanitize_filename, downloaded_files
+from App_Function_Libraries.Utils.Utils import sanitize_filename, downloaded_files
 
 
 def create_utilities_yt_video_tab():

App_Function_Libraries/Gradio_UI/Video_transcription_tab.py

@@ -9,14 +9,16 @@ import os
 # External Imports
 import gradio as gr
 import yt_dlp
+
+from App_Function_Libraries.Confabulation_check import simplified_geval
 #
 # Local Imports
-from App_Function_Libraries.DB_Manager import load_preset_prompts, add_media_to_database
+from App_Function_Libraries.DB.DB_Manager import load_preset_prompts, add_media_to_database
 from App_Function_Libraries.Gradio_UI.Gradio_Shared import whisper_models, update_user_prompt
 from App_Function_Libraries.Gradio_UI.Gradio_Shared import error_handler
 from App_Function_Libraries.Summarization_General_Lib import perform_transcription, perform_summarization, \
     save_transcription_and_summary
-from App_Function_Libraries.Utils import convert_to_seconds, safe_read_file, format_transcription, \
+from App_Function_Libraries.Utils.Utils import convert_to_seconds, safe_read_file, format_transcription, \
     create_download_directory, generate_unique_identifier, extract_text_from_segments
 from App_Function_Libraries.Video_DL_Ingestion_Lib import parse_and_expand_urls, extract_metadata, download_video
 #
@@ -122,7 +124,7 @@ def create_video_transcription_tab():
                 summarize_recursively = gr.Checkbox(label="Enable Recursive Summarization", value=False)
                 use_cookies_input = gr.Checkbox(label="Use cookies for authenticated download", value=False)
                 use_time_input = gr.Checkbox(label="Use Start and End Time", value=False)
-
+                confab_checkbox = gr.Checkbox(label="Perform Confabulation Check of Summary", value=False)
                 with gr.Row(visible=False) as time_input_box:
                     gr.Markdown("### Start and End time")
                     with gr.Column():
@@ -176,6 +178,7 @@ def create_video_transcription_tab():
                 progress_output = gr.Textbox(label="Progress")
                 error_output = gr.Textbox(label="Errors", visible=False)
                 results_output = gr.HTML(label="Results")
+                confabulation_output = gr.Textbox(label="Confabulation Check Results", visible=False)
                 download_transcription = gr.File(label="Download All Transcriptions as JSON")
                 download_summary = gr.File(label="Download All Summaries as Text")
 
@@ -422,7 +425,7 @@ def create_video_transcription_tab():
                                        chunk_method, max_chunk_size, chunk_overlap, use_adaptive_chunking,
                                        use_multi_level_chunking, chunk_language, summarize_recursively, api_name,
                                        api_key, keywords, use_cookies, cookies, batch_size,
-                                       timestamp_option, keep_original_video):
+                                       timestamp_option, keep_original_video, confab_checkbox):
                 global result
                 try:
                     logging.info("process_videos_wrapper(): process_videos_wrapper called")
@@ -450,42 +453,41 @@ def create_video_transcription_tab():
 
                     if not inputs:
                         raise ValueError("No input provided. Please enter URLs or upload a video file.")
-                    try:
-                        result = process_videos_with_error_handling(
-                            inputs, start_time, end_time, diarize, whisper_model,
-                            custom_prompt_checkbox, custom_prompt, chunking_options_checkbox,
-                            chunk_method, max_chunk_size, chunk_overlap, use_adaptive_chunking,
-                            use_multi_level_chunking, chunk_language, api_name,
-                            api_key, keywords, use_cookies, cookies, batch_size,
-                            timestamp_option, keep_original_video, summarize_recursively
-                        )
-                    except Exception as e:
-                        logging.error(
-                            f"process_videos_wrapper(): Error in process_videos_with_error_handling: {str(e)}",
-                            exc_info=True)
-
-                    logging.info("process_videos_wrapper(): process_videos_with_error_handling completed")
+
+                    result = process_videos_with_error_handling(
+                        inputs, start_time, end_time, diarize, whisper_model,
+                        custom_prompt_checkbox, custom_prompt, chunking_options_checkbox,
+                        chunk_method, max_chunk_size, chunk_overlap, use_adaptive_chunking,
+                        use_multi_level_chunking, chunk_language, api_name,
+                        api_key, keywords, use_cookies, cookies, batch_size,
+                        timestamp_option, keep_original_video, summarize_recursively
+                    )
+
+                    confabulation_result = None
+                    if confab_checkbox:
+                        logging.info("Confabulation check enabled")
+                        # Assuming result[1] contains the transcript and result[2] contains the summary
+                        confabulation_result = simplified_geval(result[1], result[2], api_name, api_key)
+                        logging.info(f"Simplified G-Eval result: {confabulation_result}")
 
                     # Ensure that result is a tuple with 5 elements
                     if not isinstance(result, tuple) or len(result) != 5:
                         raise ValueError(
                             f"process_videos_wrapper(): Expected 5 outputs, but got {len(result) if isinstance(result, tuple) else 1}")
 
-                    return result
+                    # Return the confabulation result along with other outputs
+                    return (*result, confabulation_result)
+
                 except Exception as e:
                     logging.error(f"process_videos_wrapper(): Error in process_videos_wrapper: {str(e)}", exc_info=True)
-                    # Return a tuple with 5 elements in case of any error
+                    # Return a tuple with 6 elements in case of any error (including None for simple_geval_result)
                     return (
-                        # progress_output
-                        f"process_videos_wrapper(): An error occurred: {str(e)}",
-                        # error_output
-                        str(e),
-                        # results_output
-                        f"<div class='error'>Error: {str(e)}</div>",
-                        # download_transcription
-                        None,
-                        # download_summary
-                        None
+                        f"process_videos_wrapper(): An error occurred: {str(e)}",  # progress_output
+                        str(e),  # error_output
+                        f"<div class='error'>Error: {str(e)}</div>",  # results_output
+                        None,  # download_transcription
+                        None,  # download_summary
+                        None  # simple_geval_result
                     )
 
             # FIXME - remove dead args for process_url_with_metadata
@@ -590,7 +592,6 @@ def create_video_transcription_tab():
                     with open(segments_json_path, 'w') as f:
                         json.dump(segments_with_metadata, f, indent=2)
 
-                    # FIXME - why isnt this working?
                     # Delete the .wav file after successful transcription
                     files_to_delete = [audio_file_path]
                     for file_path in files_to_delete:
@@ -677,6 +678,14 @@ def create_video_transcription_tab():
                     logging.error(f"Error in process_url_with_metadata: {str(e)}", exc_info=True)
                     return None, None, None, None, None, None
 
+            def toggle_confabulation_output(checkbox_value):
+                return gr.update(visible=checkbox_value)
+
+            confab_checkbox.change(
+                fn=toggle_confabulation_output,
+                inputs=[confab_checkbox],
+                outputs=[confabulation_output]
+            )
             process_button.click(
                 fn=process_videos_wrapper,
                 inputs=[
@@ -685,7 +694,7 @@ def create_video_transcription_tab():
                     chunk_method, max_chunk_size, chunk_overlap, use_adaptive_chunking,
                     use_multi_level_chunking, chunk_language, summarize_recursively, api_name_input, api_key_input,
                     keywords_input, use_cookies_input, cookies_input, batch_size_input,
-                    timestamp_option, keep_original_video
+                    timestamp_option, keep_original_video, confab_checkbox
                 ],
-                outputs=[progress_output, error_output, results_output, download_transcription, download_summary]
+                outputs=[progress_output, error_output, results_output, download_transcription, download_summary, confabulation_output]
             )

App_Function_Libraries/Gradio_UI/Website_scraping_tab.py

@@ -7,7 +7,7 @@
 import gradio as gr
 
 from App_Function_Libraries.Article_Summarization_Lib import scrape_and_summarize_multiple
-from App_Function_Libraries.DB_Manager import load_preset_prompts
+from App_Function_Libraries.DB.DB_Manager import load_preset_prompts
 from App_Function_Libraries.Gradio_UI.Chat_ui import update_user_prompt