# Gradio_Shared.py # Gradio UI functions that are shared across multiple tabs # # Imports import logging import sqlite3 import traceback from functools import wraps from typing import List, Tuple # # External Imports import gradio as gr # # Local Imports 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 # ############################################################################################################## # # Functions: whisper_models = ["small", "medium", "small.en", "medium.en", "medium", "large", "large-v1", "large-v2", "large-v3", "distil-large-v2", "distil-medium.en", "distil-small.en"] # Sample data prompts_category_1 = [ "What are the key points discussed in the video?", "Summarize the main arguments made by the speaker.", "Describe the conclusions of the study presented." ] prompts_category_2 = [ "How does the proposed solution address the problem?", "What are the implications of the findings?", "Can you explain the theory behind the observed phenomenon?" ] all_prompts = prompts_category_1 + prompts_category_2 #FIXME - SQL Functions that need to be addressed/added to DB manager def search_media(query, fields, keyword, page): try: results = search_and_display(query, fields, keyword, page) return results except Exception as e: logger = logging.getLogger() logger.error(f"Error searching media: {e}") return str(e) def fetch_items_by_title_or_url(search_query: str, search_type: str): try: with db.get_connection() as conn: cursor = conn.cursor() if search_type == 'Title': cursor.execute("SELECT id, title, url FROM Media WHERE title LIKE ?", (f'%{search_query}%',)) elif search_type == 'URL': cursor.execute("SELECT id, title, url FROM Media WHERE url LIKE ?", (f'%{search_query}%',)) results = cursor.fetchall() return results except sqlite3.Error as e: raise DatabaseError(f"Error fetching items by {search_type}: {e}") def fetch_items_by_keyword(search_query: str): try: with db.get_connection() as conn: cursor = conn.cursor() cursor.execute(""" SELECT m.id, m.title, m.url FROM Media m JOIN MediaKeywords mk ON m.id = mk.media_id JOIN Keywords k ON mk.keyword_id = k.id WHERE k.keyword LIKE ? """, (f'%{search_query}%',)) results = cursor.fetchall() return results except sqlite3.Error as e: raise DatabaseError(f"Error fetching items by keyword: {e}") # FIXME - Raw SQL not using DB_Manager... def fetch_items_by_content(search_query: str): try: with db.get_connection() as conn: cursor = conn.cursor() cursor.execute("SELECT id, title, url FROM Media WHERE content LIKE ?", (f'%{search_query}%',)) results = cursor.fetchall() return results except sqlite3.Error as e: raise DatabaseError(f"Error fetching items by content: {e}") # FIXME - RAW SQL not using DB_Manager... def fetch_item_details_single(media_id: int): try: with db.get_connection() as conn: cursor = conn.cursor() cursor.execute(""" SELECT prompt, summary FROM MediaModifications WHERE media_id = ? ORDER BY modification_date DESC LIMIT 1 """, (media_id,)) prompt_summary_result = cursor.fetchone() cursor.execute("SELECT content FROM Media WHERE id = ?", (media_id,)) content_result = cursor.fetchone() prompt = prompt_summary_result[0] if prompt_summary_result else "" summary = prompt_summary_result[1] if prompt_summary_result else "" content = content_result[0] if content_result else "" return prompt, summary, content except sqlite3.Error as e: raise Exception(f"Error fetching item details: {e}") # FIXME - RAW SQL not using DB_Manager... def fetch_item_details(media_id: int): try: with db.get_connection() as conn: cursor = conn.cursor() cursor.execute(""" SELECT prompt, summary FROM MediaModifications WHERE media_id = ? ORDER BY modification_date DESC LIMIT 1 """, (media_id,)) prompt_summary_result = cursor.fetchone() cursor.execute("SELECT content FROM Media WHERE id = ?", (media_id,)) content_result = cursor.fetchone() prompt = prompt_summary_result[0] if prompt_summary_result else "" summary = prompt_summary_result[1] if prompt_summary_result else "" content = content_result[0] if content_result else "" return content, prompt, summary except sqlite3.Error as e: logging.error(f"Error fetching item details: {e}") return "", "", "" # Return empty strings if there's an error # Handle prompt selection def handle_prompt_selection(prompt): return f"You selected: {prompt}" def update_user_prompt(preset_name): details = fetch_prompt_details(preset_name) if details: # Return a dictionary with all details return { "title": details[0], "author": details[1], "details": details[2], "system_prompt": details[3], "user_prompt": details[4] if len(details) > 3 else "", } return {"title": "", "details": "", "system_prompt": "", "user_prompt": "", "author": ""} def browse_items(search_query, search_type): if search_type == 'Keyword': results = fetch_items_by_keyword(search_query) elif search_type == 'Content': results = fetch_items_by_content(search_query) else: results = fetch_items_by_title_or_url(search_query, search_type) return results def update_dropdown(search_query, search_type): results = browse_items(search_query, search_type) item_options = [f"{item[1]} ({item[2]})" for item in results] new_item_mapping = {f"{item[1]} ({item[2]})": item[0] for item in results} print(f"Debug - Update Dropdown - New Item Mapping: {new_item_mapping}") return gr.update(choices=item_options), new_item_mapping def get_media_id(selected_item, item_mapping): return item_mapping.get(selected_item) def update_detailed_view(item, item_mapping): # Function to update the detailed view based on selected item if item: item_id = item_mapping.get(item) if item_id: content, prompt, summary = fetch_item_details(item_id) if content or prompt or summary: details_html = "
{prompts['details']}
" system_str = f"{prompts['system_prompt']}
" user_str = f"{prompts['user_prompt']}
" if prompts['user_prompt'] else "" return details_str + system_str + user_str return "No details available." def search_media_database(query: str) -> List[Tuple[int, str, str]]: return browse_items(query, 'Title') def load_media_content(media_id: int) -> dict: try: print(f"Debug - Load Media Content - Media ID: {media_id}") item_details = fetch_item_details(media_id) print(f"Debug - Load Media Content - Item Details: \n\n{item_details}\n\n\n\n") if isinstance(item_details, tuple) and len(item_details) == 3: content, prompt, summary = item_details else: print(f"Debug - Load Media Content - Unexpected item_details format: \n\n{item_details}\n\n\n\n") content, prompt, summary = "", "", "" return { "content": content or "No content available", "prompt": prompt or "No prompt available", "summary": summary or "No summary available" } except Exception as e: print(f"Debug - Load Media Content - Error: {str(e)}") return {"content": "", "prompt": "", "summary": ""} def error_handler(func): @wraps(func) def wrapper(*args, **kwargs): try: return func(*args, **kwargs) except Exception as e: error_message = f"Error in {func.__name__}: {str(e)}" logging.error(f"{error_message}\n{traceback.format_exc()}") return {"error": error_message, "details": traceback.format_exc()} return wrapper def create_chunking_inputs(): chunk_text_by_words_checkbox = gr.Checkbox(label="Chunk Text by Words", value=False, visible=True) max_words_input = gr.Number(label="Max Words", value=300, precision=0, visible=True) chunk_text_by_sentences_checkbox = gr.Checkbox(label="Chunk Text by Sentences", value=False, visible=True) max_sentences_input = gr.Number(label="Max Sentences", value=10, precision=0, visible=True) chunk_text_by_paragraphs_checkbox = gr.Checkbox(label="Chunk Text by Paragraphs", value=False, visible=True) max_paragraphs_input = gr.Number(label="Max Paragraphs", value=5, precision=0, visible=True) chunk_text_by_tokens_checkbox = gr.Checkbox(label="Chunk Text by Tokens", value=False, visible=True) max_tokens_input = gr.Number(label="Max Tokens", value=1000, precision=0, visible=True) gr_semantic_chunk_long_file = gr.Checkbox(label="Semantic Chunking by Sentence similarity", value=False, visible=True) gr_semantic_chunk_long_file_size = gr.Number(label="Max Chunk Size", value=2000, visible=True) gr_semantic_chunk_long_file_overlap = gr.Number(label="Max Chunk Overlap Size", value=100, visible=True) return [chunk_text_by_words_checkbox, max_words_input, chunk_text_by_sentences_checkbox, max_sentences_input, chunk_text_by_paragraphs_checkbox, max_paragraphs_input, chunk_text_by_tokens_checkbox, max_tokens_input]