#TODO: Quran results have numbers import logging logger = logging.getLogger(__name__) logging.basicConfig(level=logging.INFO) import gradio as gr import torah import bible import quran import hindu import tripitaka from utils import number_to_ordinal_word, custom_normalize, date_to_words, translate_date_to_words from gematria import calculate_gematria, strip_diacritics import pandas as pd from deep_translator import GoogleTranslator from gradio_calendar import Calendar from datetime import datetime, timedelta import math import json import re import sqlite3 from collections import defaultdict from typing import List, Tuple import rich from fuzzywuzzy import fuzz import calendar import translation_utils import hashlib translation_utils.create_translation_table() # Create a translator instance *once* globally translator = GoogleTranslator(source='auto', target='auto') LANGUAGES_SUPPORTED = translator.get_supported_languages(as_dict=True) # Corrected dictionary name LANGUAGE_CODE_MAP = LANGUAGES_SUPPORTED # Use deep_translator's mapping directly # --- Constants --- DATABASE_FILE = 'gematria.db' MAX_PHRASE_LENGTH_LIMIT = 20 ELS_CACHE_DB = "els_cache.db" DATABASE_TIMEOUT = 60 # --- Database Initialization --- def initialize_database(): global conn conn = sqlite3.connect(DATABASE_FILE) cursor = conn.cursor() cursor.execute(''' CREATE TABLE IF NOT EXISTS results ( gematria_sum INTEGER, words TEXT, translation TEXT, book TEXT, chapter INTEGER, verse INTEGER, phrase_length INTEGER, word_position TEXT, PRIMARY KEY (gematria_sum, words, book, chapter, verse, word_position) ) ''') cursor.execute(''' CREATE INDEX IF NOT EXISTS idx_results_gematria ON results (gematria_sum) ''') cursor.execute(''' CREATE TABLE IF NOT EXISTS processed_books ( book TEXT PRIMARY KEY, max_phrase_length INTEGER ) ''') conn.commit() # --- Initialize Database --- initialize_database() # --- ELS Cache Functions --- def create_els_cache_table(): with sqlite3.connect(ELS_CACHE_DB) as conn: conn.execute(''' CREATE TABLE IF NOT EXISTS els_cache ( query_hash TEXT PRIMARY KEY, results TEXT ) ''') def get_query_hash(func, *args, **kwargs): key = (func.__name__, args, tuple(sorted(kwargs.items()))) return hashlib.sha256(json.dumps(key).encode()).hexdigest() def cached_process_json_files(func, *args, **kwargs): query_hash = get_query_hash(func, *args, **kwargs) try: with sqlite3.connect(ELS_CACHE_DB, timeout=DATABASE_TIMEOUT) as conn: cursor = conn.cursor() cursor.execute("SELECT results FROM els_cache WHERE query_hash = ?", (query_hash,)) result = cursor.fetchone() if result: logger.info(f"Cache hit for query: {query_hash}") return json.loads(result[0]) except sqlite3.Error as e: logger.error(f"Database error checking cache: {e}") logger.info(f"Cache miss for query: {query_hash}") results = func(*args, **kwargs) try: with sqlite3.connect(ELS_CACHE_DB, timeout=DATABASE_TIMEOUT) as conn: cursor = conn.cursor() cursor.execute("INSERT INTO els_cache (query_hash, results) VALUES (?, ?)", (query_hash, json.dumps(results))) conn.commit() except sqlite3.Error as e: logger.error(f"Database error caching results: {e}") return results # --- Helper Functions (from Network app.py) --- def flatten_text(text: List) -> str: if isinstance(text, list): return " ".join(flatten_text(item) if isinstance(item, list) else item for item in text) return text def search_gematria_in_db(gematria_sum: int, max_words: int) -> List[Tuple[str, str, int, int, int, str]]: global conn with sqlite3.connect(DATABASE_FILE) as conn: cursor = conn.cursor() cursor.execute(''' SELECT words, book, chapter, verse, phrase_length, word_position FROM results WHERE gematria_sum = ? AND phrase_length <= ? ''', (gematria_sum, max_words)) results = cursor.fetchall() return results def get_most_frequent_phrase(results): phrase_counts = defaultdict(int) for words, book, chapter, verse, phrase_length, word_position in results: phrase_counts[words] += 1 most_frequent_phrase = max(phrase_counts, key=phrase_counts.get) if phrase_counts else None # Handle empty results return most_frequent_phrase # --- Functions from BOS app.py --- def create_language_dropdown(label, default_value='English', show_label=True): # Default value must be in LANGUAGE_CODE_MAP return gr.Dropdown( choices=list(LANGUAGE_CODE_MAP.keys()), # Correct choices label=label, value=default_value, show_label=show_label ) def calculate_gematria_sum(text, date_words): if text or date_words: combined_input = f"{text} {date_words}" logger.info(f"searching for input: {combined_input}") numbers = re.findall(r'\d+', combined_input) text_without_numbers = re.sub(r'\d+', '', combined_input) number_sum = sum(int(number) for number in numbers) text_gematria = calculate_gematria(strip_diacritics(text_without_numbers)) total_sum = text_gematria + number_sum return total_sum else: return None def perform_els_search(step, rounds_combination, tlang, strip_spaces, strip_in_braces, strip_diacritics_chk, include_torah, include_bible, include_quran, include_hindu, include_tripitaka): if step == 0 or rounds_combination == "0,0": return None results = {} length = 0 selected_language_long = tlang # From the Gradio dropdown (long form) tlang = LANGUAGES_SUPPORTED.get(selected_language_long) #Get the short code. if tlang is None: # Handle unsupported languages tlang = "en" logger.warning(f"Unsupported language selected: {selected_language_long}. Defaulting to English (en).") if include_torah: logger.debug(f"Arguments for Torah: {(1, 39, step, rounds_combination, length, tlang, strip_spaces, strip_in_braces, strip_diacritics_chk)}") results["Torah"] = cached_process_json_files(torah.process_json_files, 1, 39, step, rounds_combination, length, tlang, strip_spaces, strip_in_braces, strip_diacritics_chk) else: results["Torah"] = [] if include_bible: results["Bible"] = cached_process_json_files(bible.process_json_files, 40, 66, step, rounds_combination, length, tlang, strip_spaces, strip_in_braces, strip_diacritics_chk) else: results["Bible"] = [] if include_quran: results["Quran"] = cached_process_json_files(quran.process_json_files, 1, 114, step, rounds_combination, length, tlang, strip_spaces, strip_in_braces, strip_diacritics_chk) else: results["Quran"] = [] if include_hindu: results["Rig Veda"] = cached_process_json_files(hindu.process_json_files, 1, 10, step, rounds_combination, length, tlang, False, strip_in_braces, strip_diacritics_chk) else: results["Rig Veda"] = [] if include_tripitaka: results["Tripitaka"] = cached_process_json_files(tripitaka.process_json_files, 1, 52, step, rounds_combination, length, tlang, strip_spaces, strip_in_braces, strip_diacritics_chk) else: results["Tripitaka"] = [] return results def add_24h_projection(results_dict): #Now takes a dictionary of results for book_name, results in results_dict.items(): # Iterate per book num_results = len(results) if num_results > 0: time_interval = timedelta(minutes=24 * 60 / num_results) current_time = datetime.min.time() for i in range(num_results): next_time = (datetime.combine(datetime.min, current_time) + time_interval).time() time_range_str = f"{current_time.strftime('%H:%M')}-{next_time.strftime('%H:%M')}" results[i]['24h Projection'] = time_range_str current_time = next_time return results_dict def add_monthly_projection(results_dict, selected_date): if selected_date is None: return results_dict # Return if no date is selected for book_name, results in results_dict.items(): # Iterate per book num_results = len(results) if num_results > 0: days_in_month = calendar.monthrange(selected_date.year, selected_date.month)[1] total_seconds = (days_in_month - 1) * 24 * 3600 seconds_interval = total_seconds / num_results start_datetime = datetime(selected_date.year, selected_date.month, 1) current_datetime = start_datetime for i in range(num_results): next_datetime = current_datetime + timedelta(seconds=seconds_interval) current_date = current_datetime.date() # Moved assignment inside loop next_date = next_datetime.date() date_range_str = f"{current_date.strftime('%h %d')} - {next_date.strftime('%h %d')}" results[i]['Monthly Projection'] = date_range_str current_datetime = next_datetime # Add this current_date = next_datetime.date() # Add this too return results_dict def add_yearly_projection(results_dict, selected_date): #Correct name, handle dictionary input if selected_date is None: return results_dict # Return if no date is selected for book_name, results in results_dict.items(): # Iterate per book num_results = len(results) if num_results > 0: days_in_year = 366 if calendar.isleap(selected_date.year) else 365 total_seconds = (days_in_year - 1) * 24 * 3600 seconds_interval = total_seconds / num_results start_datetime = datetime(selected_date.year, 1, 1) current_datetime = start_datetime for i in range(num_results): next_datetime = current_datetime + timedelta(seconds=seconds_interval) current_date = current_datetime.date() # Move assignment inside loop next_date = next_datetime.date() date_range_str = f"{current_date.strftime('%b %d')} - {next_date.strftime('%b %d')}" results[i]['Yearly Projection'] = date_range_str current_datetime = next_datetime # Update current datetime for next iteration return results_dict def sort_results(results): def parse_time(time_str): try: hours, minutes = map(int, time_str.split(':')) return hours * 60 + minutes # Convert to total minutes except ValueError: return 24 * 60 # Sort invalid times to the end return sorted(results, key=lambda x: ( parse_time(x.get('24h Projection', '23:59').split('-')[0]), # Sort by start time first parse_time(x.get('24h Projection', '23:59').split('-')[1]) # Then by end time )) # --- Main Gradio App --- with gr.Blocks() as app: with gr.Column(): with gr.Row(): tlang = create_language_dropdown("Target Language for Result Translation", default_value='english') selected_date = Calendar(type="datetime", label="Date to investigate (optional)", info="Pick a date from the calendar") use_day = gr.Checkbox(label="Use Day", info="Check to include day in search", value=True) use_month = gr.Checkbox(label="Use Month", info="Check to include month in search", value=True) use_year = gr.Checkbox(label="Use Year", info="Check to include year in search", value=True) date_language_input = create_language_dropdown("Language of the person/topic (optional) (Date Word Language)", default_value='english') with gr.Row(): gematria_text = gr.Textbox(label="Name and/or Topic (required)", value="Hans Albert Einstein Mileva Marity-Einstein") date_words_output = gr.Textbox(label="Date in Words Translated (optional)") gematria_result = gr.Number(label="Journal Sum") #with gr.Row(): with gr.Row(): step = gr.Number(label="Jump Width (Steps) for ELS") float_step = gr.Number(visible=False, value=1) half_step_btn = gr.Button("Steps / 2") double_step_btn = gr.Button("Steps * 2") with gr.Column(): round_x = gr.Number(label="Round (1)", value=1) round_y = gr.Number(label="Round (2)", value=-1) rounds_combination = gr.Textbox(label="Combined Rounds", value="1,-1") with gr.Row(): include_torah_chk = gr.Checkbox(label="Include Torah", value=True) include_bible_chk = gr.Checkbox(label="Include Bible", value=True) include_quran_chk = gr.Checkbox(label="Include Quran", value=True) include_hindu_chk = gr.Checkbox(label="Include Rigveda", value=True) include_tripitaka_chk = gr.Checkbox(label="Include Tripitaka", value=True) merge_results_chk = gr.Checkbox(label="Merge Results (Torah-Bible-Quran)", value=True) strip_spaces = gr.Checkbox(label="Strip Spaces from Books", value=True) strip_in_braces = gr.Checkbox(label="Strip Text in Braces from Books", value=True) strip_diacritics_chk = gr.Checkbox(label="Strip Diacritics from Books", value=True) translate_btn = gr.Button("Search with ELS") # --- Output Components --- markdown_output = gr.Dataframe(label="ELS Results") most_frequent_phrase_output = gr.Textbox(label="Most Frequent Phrase in Network Search") json_output = gr.JSON(label="JSON Output") # --- Event Handlers --- def update_date_words(selected_date, date_language_input, use_day, use_month, use_year): if selected_date is None: return "" if not use_year and not use_month and not use_day: return translate_date_to_words(selected_date, date_language_input) year = selected_date.year if use_year else None month = selected_date.month if use_month else None day = selected_date.day if use_day else None if year is not None and month is not None and day is not None: date_obj = selected_date elif year is not None and month is not None: date_obj = str(f"{year}-{month}") elif year is not None: date_obj = str(f"{year}") else: # Return empty string if no date components are selected return "" date_in_words = date_to_words(date_obj) translator = GoogleTranslator(source='auto', target=date_language_input) translated_date_words = translator.translate(date_in_words) return custom_normalize(translated_date_words) def update_journal_sum(gematria_text, date_words_output): sum_value = calculate_gematria_sum(gematria_text, date_words_output) return sum_value, sum_value, sum_value def update_rounds_combination(round_x, round_y): return f"{int(round_x)},{int(round_y)}" def update_step_half(float_step): new_step = math.ceil(float_step / 2) return new_step, float_step / 2 def update_step_double(float_step): new_step = math.ceil(float_step * 2) return new_step, float_step * 2 def find_closest_phrase(target_phrase, phrases): best_match = None best_score = 0 logging.debug(f"Target phrase for similarity search: {target_phrase}") # Log target phrase for phrase, _, _, _, _, _ in phrases: word_length_diff = abs(len(target_phrase.split()) - len(phrase.split())) similarity_score = fuzz.ratio(target_phrase, phrase) combined_score = similarity_score - word_length_diff logging.debug(f"Comparing with phrase: {phrase}") # Log each phrase being compared logging.debug( f"Word Length Difference: {word_length_diff}, Similarity Score: {similarity_score}, Combined Score: {combined_score}") # Log scores if combined_score > best_score: best_score = combined_score best_match = phrase logging.debug(f"Closest phrase found: {best_match} with score: {best_score}") # Log the best match return best_match def perform_search(step, rounds_combination, tlang, strip_spaces, strip_in_braces, strip_diacritics_chk, include_torah, include_bible, include_quran, include_hindu, include_tripitaka, gematria_text, date_words_output, selected_date): # Inside perform_search els_results = perform_els_search(step, rounds_combination, tlang, strip_spaces, strip_in_braces, strip_diacritics_chk, include_torah, include_bible, include_quran, include_hindu, include_tripitaka) # --- Network Search Integration --- most_frequent_phrases = {} combined_and_sorted_results = [] # Combined list to hold all results for book_name, book_results in els_results.items(): if book_results: # Add this check to ensure book_results is not empty most_frequent_phrases[book_name] = "" # Default value for result in book_results: try: gematria_sum = calculate_gematria(result['result_text']) # Calculate gematria max_words = len(result['result_text'].split()) matching_phrases = search_gematria_in_db(gematria_sum, max_words) max_words_limit = 20 while not matching_phrases and max_words < max_words_limit: # Increase max_words for more results max_words += 1 matching_phrases = search_gematria_in_db(gematria_sum, max_words) if matching_phrases: most_frequent_phrase = get_most_frequent_phrase(matching_phrases) most_frequent_phrases[book_name] = most_frequent_phrase else: closest_phrase = find_closest_phrase(result['result_text'], search_gematria_in_db(gematria_sum, max_words_limit)) most_frequent_phrases[ book_name] = closest_phrase or "" # Update most frequent phrases even if no phrase found result['Most Frequent Phrase'] = most_frequent_phrases[book_name] if 'book' in result: if isinstance(result['book'], int): # Torah, Bible, Quran case result['book'] = f"{book_name} {result['book']}." combined_and_sorted_results.append(result) except KeyError as e: print(f"DEBUG: KeyError - Key '{e.args[0]}' not found in result. Skipping this result.") continue # --- Batch Translation --- selected_language_long = tlang tlang_short = LANGUAGES_SUPPORTED.get(selected_language_long) if tlang_short is None: tlang_short = "en" logger.warning(f"Unsupported language selected: {selected_language_long}. Defaulting to English (en).") # Prepare lists for batch translation, including source language phrases_to_translate = [] phrases_source_langs = [] # Source languages for phrases results_to_translate = [] results_source_langs = [] # Source languages for results for result in combined_and_sorted_results: phrases_to_translate.append(result.get('Most Frequent Phrase', '')) phrases_source_langs.append(result.get("source_language", "auto")) results_to_translate.append(result.get('result_text', '')) results_source_langs.append(result.get("source_language", "auto")) translated_phrases = translation_utils.batch_translate(phrases_to_translate, tlang_short, phrases_source_langs) translated_result_texts = translation_utils.batch_translate(results_to_translate, tlang_short, results_source_langs) for i, result in enumerate(combined_and_sorted_results): result['translated_text'] = translated_result_texts.get(results_to_translate[i], None) result['Translated Most Frequent Phrase'] = translated_phrases.get(phrases_to_translate[i], None) # Time Projections (using els_results dictionary) updated_els_results = add_24h_projection(els_results) # Use original els_results dictionary updated_els_results = add_monthly_projection(updated_els_results, selected_date) # Call correct functions with correct params updated_els_results = add_yearly_projection(updated_els_results, selected_date) combined_and_sorted_results = [] for book_results in updated_els_results.values(): # Combine results for dataframe and json combined_and_sorted_results.extend(book_results) combined_and_sorted_results = sort_results(combined_and_sorted_results) # sort combined results df = pd.DataFrame(combined_and_sorted_results) df.index = range(1, len(df) + 1) df.reset_index(inplace=True) df.rename(columns={'index': 'Result Number'}, inplace=True) for i, result in enumerate(combined_and_sorted_results): # Iterate through the combined list result['Result Number'] = i + 1 search_config = { "step": step, "rounds_combination": rounds_combination, "target_language": tlang, "strip_spaces": strip_spaces, "strip_in_braces": strip_in_braces, "strip_diacritics": strip_diacritics_chk, "include_torah": include_torah, "include_bible": include_bible, "include_quran": include_quran, "include_hindu": include_hindu, "include_tripitaka": include_tripitaka, "gematria_text": gematria_text, "date_words": date_words_output } output_data = { "search_configuration": search_config, "results": combined_and_sorted_results # Use the combined list here } json_data = output_data # --- Return results --- combined_most_frequent = "\n".join( f"{book}: {phrase}" for book, phrase in most_frequent_phrases.items()) # Combine phrases return df, combined_most_frequent, json_data # --- Event Triggers --- round_x.change(update_rounds_combination, inputs=[round_x, round_y], outputs=rounds_combination) round_y.change(update_rounds_combination, inputs=[round_x, round_y], outputs=rounds_combination) selected_date.change(update_date_words, inputs=[selected_date, date_language_input, use_day, use_month, use_year], outputs=[date_words_output]) date_language_input.change(update_date_words, inputs=[selected_date, date_language_input, use_day, use_month, use_year], outputs=[date_words_output]) gematria_text.change(update_journal_sum, inputs=[gematria_text, date_words_output], outputs=[gematria_result, step, float_step]) date_words_output.change(update_journal_sum, inputs=[gematria_text, date_words_output], outputs=[gematria_result, step, float_step]) half_step_btn.click(update_step_half, inputs=[float_step], outputs=[step, float_step]) double_step_btn.click(update_step_double, inputs=[float_step], outputs=[step, float_step]) translate_btn.click( perform_search, inputs=[step, rounds_combination, tlang, strip_spaces, strip_in_braces, strip_diacritics_chk, include_torah_chk, include_bible_chk, include_quran_chk, include_hindu_chk, include_tripitaka_chk, gematria_text, date_words_output, selected_date], outputs=[markdown_output, most_frequent_phrase_output, json_output] ) app.load( update_date_words, inputs=[selected_date, date_language_input, use_day, use_month, use_year], # Include all 5 inputs outputs=[date_words_output] ) use_day.change( update_date_words, inputs=[selected_date, date_language_input, use_day, use_month, use_year], outputs=[date_words_output] ) use_month.change( update_date_words, inputs=[selected_date, date_language_input, use_day, use_month, use_year], outputs=[date_words_output] ) use_year.change( update_date_words, inputs=[selected_date, date_language_input, use_day, use_month, use_year], outputs=[date_words_output] ) def checkbox_behavior(use_day_value, use_month_value): if use_day_value: # Tick month and year automatically when day is ticked. return True, True return use_month_value, True # return month value unchanged and automatically tick year if month is checked use_day.change(checkbox_behavior, inputs=[use_day, use_month], outputs=[use_month, use_year]) use_month.change(checkbox_behavior, inputs=[use_day, use_month], outputs=[use_month, use_year]) #No need for use_day here, day won't be changed by month if __name__ == "__main__": app.launch(share=False)