app.py

#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
import time

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 add_24h_projection(results_dict, date_str):  # Add date_str as parameter
    combined_results = []
    for book_name, results in results_dict.items():
        combined_results.extend(results)

    num_results = len(combined_results)
    if num_results > 0:
        time_interval = timedelta(minutes=24 * 60 / num_results)
        current_datetime = datetime.combine(datetime.today(), datetime.min.time())
        for i in range(num_results):
            next_datetime = current_datetime + time_interval
            time_range_str = f"{current_datetime.strftime('%H:%M')}-{next_datetime.strftime('%H:%M')}"
            combined_results[i]['24h Projection'] = time_range_str
            current_datetime = next_datetime

    # Re-organize results back into their book dictionaries
    reorganized_results = defaultdict(list)
    for result in combined_results:
        book_name = result.get('book', 'Unknown') #Get book name to reorganize
        reorganized_results[book_name].append(result)

    return reorganized_results


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')
            start_date_range = Calendar(type="datetime", label="Start Date for ELS")
            end_date_range = Calendar(type="datetime", label="End Date for ELS")
            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")


        with gr.Row():
            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_rounds_combination(round_x, round_y):
        return f"{int(round_x)},{int(round_y)}"



    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(rounds_combination, tlang, strip_spaces, strip_in_braces, strip_diacritics_chk, include_torah, include_bible, include_quran, include_hindu, include_tripitaka, gematria_text, start_date, end_date, date_language_input):
        overall_start_time = time.time()

        combined_and_sorted_results = []
        most_frequent_phrases = {}

        current_date = start_date
        while current_date <= end_date:
            date_str = current_date.strftime("%Y-%m-%d")
            date_words = translate_date_to_words(current_date, date_language_input)

            step = calculate_gematria_sum(gematria_text, date_words)
            logger.debug(f"Calculated step for {date_str}: {step}")

            if step != 0 and rounds_combination != "0,0":
                # Process for the current date
                els_results_single_date = {}
                if include_torah:
                    els_results_single_date["Torah"] = cached_process_json_files(torah.process_json_files, 1, 39, step,
                                                                            rounds_combination, 0, tlang, strip_spaces,
                                                                            strip_in_braces, strip_diacritics_chk)
                if include_bible:
                    els_results_single_date["Bible"] = cached_process_json_files(bible.process_json_files, 40, 66, step,
                                                                            rounds_combination, 0, tlang, strip_spaces,
                                                                            strip_in_braces, strip_diacritics_chk)
                if include_quran:
                    els_results_single_date["Quran"] = cached_process_json_files(quran.process_json_files, 1, 114, step,
                                                                            rounds_combination, 0, tlang, strip_spaces,
                                                                            strip_in_braces, strip_diacritics_chk)
                if include_hindu:
                    els_results_single_date["Rig Veda"] = cached_process_json_files(hindu.process_json_files, 1, 10, step,
                                                                                rounds_combination, 0, tlang, False,
                                                                                strip_in_braces, strip_diacritics_chk)
                if include_tripitaka:
                    els_results_single_date["Tripitaka"] = cached_process_json_files(tripitaka.process_json_files, 1, 52,
                                                                                    step, rounds_combination, 0, tlang,
                                                                                    strip_spaces, strip_in_braces,
                                                                                    strip_diacritics_chk)

                # Add 24h projection *before* iterating through books
                els_results_single_date = add_24h_projection(els_results_single_date, date_str)

                for book_name, book_results in els_results_single_date.items():
                    logger.debug(f"Processing results for book: {book_name}")
                    if book_results:
                        most_frequent_phrases[book_name] = ""
                        for result in book_results:
                            try:
                                gematria_sum = calculate_gematria(result['result_text'])
                                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:
                                    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 ""

                                result['Most Frequent Phrase'] = most_frequent_phrases[book_name]
                                result['date'] = date_str
                                if 'book' in result:
                                    if isinstance(result['book'], int):
                                        result['book'] = f"{book_name} {result['book']}."

                            except KeyError as e:
                                print(f"DEBUG: KeyError - Key '{e.args[0]}' not found in result. Skipping this result.")
                                continue


                    combined_and_sorted_results.extend(book_results)



            current_date += timedelta(days=1)

        # --- Batch Translation ---
        translation_start_time = time.time()
        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).")

        phrases_to_translate = []
        phrases_source_langs = []
        results_to_translate = []
        results_source_langs = []

        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], result.get('result_text', ''))
            result['Translated Most Frequent Phrase'] = translated_phrases.get(phrases_to_translate[i],
                                                                            result.get('Most Frequent Phrase', ''))

        translation_end_time = time.time()
        logger.debug(f"Batch translation took: {translation_end_time - translation_start_time} seconds")

        # --- Time projections ---
        time_projections_start_time = time.time()
        for result in combined_and_sorted_results:
            selected_date = datetime.strptime(result['date'], '%Y-%m-%d')
            book_name = result.get('book', 'Unknown')
            projection_input = {book_name: [result]}

            updated_date_results = add_24h_projection(projection_input, result['date'])

            result.update(updated_date_results[book_name][0])

        combined_and_sorted_results = sort_results(combined_and_sorted_results)
        time_projections_end_time = time.time()
        logger.debug(
            f"Time projections took: {time_projections_end_time - time_projections_start_time} seconds")

        # --- Dataframe and JSON creation ---
        dataframe_json_start_time = time.time()

        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)

        search_config = {
            "rounds_combination": rounds_combination,  # No more 'step'
            "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,
            "start_date": start_date.strftime("%Y-%m-%d"),
            "end_date": end_date.strftime("%Y-%m-%d")
        }

        output_data = {
            "search_configuration": search_config,
            "results": combined_and_sorted_results
        }

        json_data = output_data
        combined_most_frequent = "\n".join(
            f"{book}: {phrase}" for book, phrase in most_frequent_phrases.items() if phrase)

        dataframe_json_end_time = time.time()
        logger.debug(
            f"Dataframe and JSON creation took: {dataframe_json_end_time - dataframe_json_start_time} seconds")

        overall_end_time = time.time()
        logger.debug(f"Overall process took: {overall_end_time - overall_start_time} seconds")

        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)


    def update_rounds_combination(round_x, round_y):
        return f"{int(round_x)},{int(round_y)}"


    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)


    translate_btn.click(
        perform_search,
        inputs=[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, start_date_range, end_date_range, date_language_input],
        outputs=[markdown_output, most_frequent_phrase_output, json_output]
    )



if __name__ == "__main__":
    app.launch(share=False)