import os
import re
import requests
import json
import numpy as np
import pandas as pd
from bs4 import BeautifulSoup
from database import execute_query
from aksharamukha import transliterate
from sentence_transformers import util
from llama_index.embeddings.nomic import NomicEmbedding

nomic_api_key = os.getenv('NOMIC_API_KEY')
#nomic embed model used for similarity scores
nomic_embed_model = NomicEmbedding(
    api_key=nomic_api_key,
    dimensionality=128,
    model_name="nomic-embed-text-v1.5",
)



def get_list_meaning_word(word):
    pada_meanings = {'pada': word,
                     'Monier-Williams Sanskrit-English Dictionary (1899)': [],
                     'Shabda-Sagara (1900)': [],
                     'Apte-Practical Sanskrit-English Dictionary (1890)': [],
                     }
    url = f"https://ambuda.org/tools/dictionaries/mw,shabdasagara,apte/{word}"

    try:
        # Fetch HTML content
        response = requests.get(url)
        response.raise_for_status()

        # Parse HTML with BeautifulSoup
        soup = BeautifulSoup(response.text, 'html.parser')

        # Extracting text from different tags
        divs = soup.find_all('div', class_='my-4', attrs={'x-show': 'show'})
        
        try:
            # Find all list items <li> within the specified <ul> tag
            div_items_0 = divs[0].find('ul').find_all('li', class_='dict-entry mw-entry')
            # Print the text content of each list item
            dive_text_0 = [li_tag.get_text(strip=True) for li_tag in div_items_0]
            text_0_trans = [transliterate.process(src='Devanagari', tgt='IAST', txt=text) for text in dive_text_0]
            pada_meanings['Monier-Williams Sanskrit-English Dictionary (1899)'] = text_0_trans
        except :
            print("Error: Unable to find Monier-Williams Sanskrit-English Dictionary (1899) data.")

        try:
            div_items_1 = divs[1].find_all('div')
            dive_text_1 = [item.get_text(strip=True) for item in div_items_1]
            text_1_trans = [transliterate.process(src='Devanagari', tgt='IAST', txt=text) for text in dive_text_1]
            pada_meanings['Shabda-Sagara (1900)'] = text_1_trans
        except :
            print("Error: Unable to find Shabda-Sagara (1900) data.")

        try:
            apte_meanings = []
            for tag in divs[2].find_all('b'):
                if tag.text.strip() != '—':
                    text1 = tag.text.strip()  # English text within <b> tag
                    sibling = tag.find_next_sibling()  # Text following <b> tag
                    text2 = tag.next_sibling.strip() + ' ' # English text following <b> tag
                    while sibling.name != 'div':
                        if sibling.name is None:  # Handling non-tag text
                            text2 += " "
                        elif sibling.name == 'span':  # Handling <b> tag
                            IAST_text = transliterate.process(src='Devanagari', tgt='IAST', txt=sibling.text.strip()) 
                            text2 += IAST_text  +  ' ' + sibling.next_sibling.strip()
                        else:
                            text2 += sibling.text.strip() +  ' ' +  sibling.next_sibling.strip()
                        sibling = sibling.find_next_sibling()
                    apte_meanings.append(text2)
            pada_meanings['Apte-Practical Sanskrit-English Dictionary (1890)'] = apte_meanings[:-1]
        except:
            print("Error: Unable to find Apte-Practical Sanskrit-English Dictionary (1890) data.")

    except requests.exceptions.RequestException as e:
        print(f"Error: Failed to fetch data from {url}. {e}")

    return pada_meanings

#get similarity scores
def word_sentence_similarity(meanings, root_stem_word):
    # Check if the word embeddings are not empty
    if not meanings or not root_stem_word:
        return None
    
    meaning_embedding = np.array(nomic_embed_model.get_text_embedding(meanings))
    all_meanings = []
    word_score_pair = []
    all_meanings.extend(get_list_meaning_word(root_stem_word)['Monier-Williams Sanskrit-English Dictionary (1899)'])
    all_meanings.extend(get_list_meaning_word(root_stem_word)['Shabda-Sagara (1900)'])
    for word_meaning in all_meanings:
        root_stem_word_meaning_embedding = np.array(nomic_embed_model.get_text_embedding(word_meaning))
        # Calculate cosine similarity
        similarity_score = util.pytorch_cos_sim(meaning_embedding, root_stem_word_meaning_embedding).item()
        word_score_pair.append((word_meaning,similarity_score))
    # Sort the list in descending order based on similarity scores
    sorted_word_score_pairs = sorted(word_score_pair, key=lambda x: x[1], reverse=True)
    return sorted_word_score_pairs

#extract the adhibautic meaning of the mantra from the vedamantra
def extract_meaning_by_language(data_list, target_language='English'):
    for data_dict in data_list:
        if data_dict.get('languageName') == target_language:
            return data_dict.get('mahatma', {})
    return None  

#mantra_json_details
def get_details_mantra_json(query):
    description, data = execute_query(query)
    print(data)
    df = pd.DataFrame(data)
    df.columns = [x[0] for x in description]
    mantra_json = df['mantra_json'].values[0]
    cleaned_data = re.sub('<[^<]+?>', '', mantra_json)
    return json.loads(cleaned_data)

def iast_process(input_text):
    output_text = re.sub('[\u0951-\u0954,\u200d,\u0331]', '', input_text)
    return output_text