import whisper
import os
import random
import openai
from openai import OpenAI
import yt_dlp
from pytube import YouTube, extract
import pandas as pd
import plotly_express as px
import nltk
import plotly.graph_objects as go
from optimum.onnxruntime import ORTModelForSequenceClassification
from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification, AutoModelForSeq2SeqLM, AutoModelForTokenClassification
from sentence_transformers import SentenceTransformer, CrossEncoder, util
import streamlit as st
import en_core_web_lg
import validators
import re
import itertools
import numpy as np
from bs4 import BeautifulSoup   
import base64, time
from annotated_text import annotated_text
import pickle, math
import torch
from pydub import AudioSegment
from langchain.docstore.document import Document
from langchain.embeddings import HuggingFaceEmbeddings, HuggingFaceBgeEmbeddings, HuggingFaceInstructEmbeddings
from langchain.vectorstores import FAISS
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chat_models import ChatOpenAI
from langchain.chains import QAGenerationChain

from langchain.callbacks import StreamlitCallbackHandler
from langchain.agents import OpenAIFunctionsAgent, AgentExecutor
from langchain.agents.agent_toolkits import create_retriever_tool
from langchain.agents.openai_functions_agent.agent_token_buffer_memory import (
    AgentTokenBufferMemory,
)
from langchain.prompts import MessagesPlaceholder

from langchain.prompts.chat import (
    ChatPromptTemplate,
    SystemMessagePromptTemplate,
    AIMessagePromptTemplate,
    HumanMessagePromptTemplate,
)
from langchain.schema import (
    AIMessage,
    HumanMessage,
    SystemMessage
)

from langchain.prompts import PromptTemplate

from langsmith import Client

client = Client()
openai_audio = OpenAI()
nltk.download('punkt')


from nltk import sent_tokenize

OPEN_AI_KEY = os.environ.get('OPEN_AI_KEY')
time_str = time.strftime("%d%m%Y-%H%M%S")
HTML_WRAPPER = """<div style="overflow-x: auto; border: 1px solid #e6e9ef; border-radius: 0.25rem; padding: 1rem; 
margin-bottom: 2.5rem">{}</div> """


###################### Functions #######################################################################################

#load all required models and cache
@st.cache_resource
def load_models():

    '''Load and cache all the models to be used'''
    q_model = ORTModelForSequenceClassification.from_pretrained("nickmuchi/quantized-optimum-finbert-tone")
    ner_model = AutoModelForTokenClassification.from_pretrained("xlm-roberta-large-finetuned-conll03-english")
    q_tokenizer = AutoTokenizer.from_pretrained("nickmuchi/quantized-optimum-finbert-tone")
    ner_tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large-finetuned-conll03-english")
    sent_pipe = pipeline("text-classification",model=q_model, tokenizer=q_tokenizer)
    sum_pipe = pipeline("summarization",model="philschmid/flan-t5-base-samsum",clean_up_tokenization_spaces=True)
    ner_pipe = pipeline("ner", model=ner_model, tokenizer=ner_tokenizer, grouped_entities=True)
    cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-12-v2') #cross-encoder/ms-marco-MiniLM-L-12-v2
    sbert = SentenceTransformer('all-MiniLM-L6-v2')
    
    return sent_pipe, sum_pipe, ner_pipe, cross_encoder, sbert

@st.cache_data
def load_asr_model(model_name):

    '''Load the open source  whisper model in cases where the API is not working'''
    model = whisper.load_model(model_name)

    return model

@st.cache_resource
def get_spacy():
    nlp = en_core_web_lg.load()
    return nlp
    
nlp = get_spacy()    

sent_pipe, sum_pipe, ner_pipe, cross_encoder, sbert  = load_models()

@st.cache_data
def get_yt_audio(url):

    '''Get YT video from given URL link'''
    yt = YouTube(url)

    title = yt.title

    # Get the first available audio stream and download it
    audio_stream =  yt.streams.filter(only_audio=True).first().download()

    return audio_stream, title

@st.cache_data
def get_yt_audio_dl(url):

    '''Back up for when pytube is down'''
    
    temp_audio_file = os.path.join('output', 'audio')

    ydl_opts = {
        'format': 'bestaudio/best',
        'postprocessors': [{
            'key': 'FFmpegExtractAudio',
            'preferredcodec': 'mp3',
            'preferredquality': '192',
        }],
        'outtmpl': temp_audio_file,
        'quiet': True,
    }

    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
        
        info = ydl.extract_info(url, download=False)
        title = info.get('title', None)
        ydl.download([url])

    #with open(temp_audio_file+'.mp3', 'rb') as file:
    audio_file = os.path.join('output', 'audio.mp3')

    return audio_file, title

    
@st.cache_data
def load_whisper_api(audio):

    '''Transcribe YT audio to text using Open AI API'''
    file = open(audio, "rb")
    transcript = openai_audio.audio.transcriptions.create(model="whisper-1", file=file,response_format="text")

    return transcript

@st.cache_data
def transcribe_yt_video(link, py_tube=True):
    '''Transcribe YouTube video'''

    if py_tube:

        audio_file, title = get_yt_audio(link)

        print(f'audio_file:{audio_file}')

        st.session_state['audio'] = audio_file

        print(f"audio_file_session_state:{st.session_state['audio'] }")

        #Get size of audio file
        audio_size = round(os.path.getsize(st.session_state['audio'])/(1024*1024),1)

        #Check if file is > 24mb, if not then use Whisper API
        if audio_size <= 25:

            st.info("`Transcribing YT audio...`")
            
            #Use whisper API
            results = load_whisper_api(st.session_state['audio'])

        else:

            st.warning('File size larger than 24mb, applying chunking and transcription',icon="⚠️")

            song = AudioSegment.from_file(st.session_state['audio'], format='mp3')

            # PyDub handles time in milliseconds
            twenty_minutes = 20 * 60 * 1000
            
            chunks = song[::twenty_minutes]
            
            transcriptions = []

            video_id = extract.video_id(link)

            print(video_id)
            
            for i, chunk in enumerate(chunks):
                chunk.export(f'output/chunk_{i}_{video_id}.mp4', format='mp3')
                transcriptions.append(load_whisper_api(f'output/chunk_{i}_{video_id}.mp3'))

            results = ','.join(transcriptions)

            print(results)

    else:

        audio_file, title = get_yt_audio_dl(link)

        print(f'audio_file:{audio_file}')

        st.session_state['audio'] = audio_file

        print(f"audio_file_session_state:{st.session_state['audio'] }")

        #Get size of audio file
        audio_size = round(os.path.getsize(st.session_state['audio'])/(1024*1024),1)

        #Check if file is > 24mb, if not then use Whisper API
        if audio_size <= 25:

            st.info("`Transcribing YT audio...`")
            
            #Use whisper API
            results = load_whisper_api(st.session_state['audio'])

        else:

            st.warning('File size larger than 24mb, applying chunking and transcription',icon="⚠️")

            song = AudioSegment.from_file(st.session_state['audio'], format='mp3')

            # PyDub handles time in milliseconds
            twenty_minutes = 20 * 60 * 1000
            
            chunks = song[::twenty_minutes]
            
            transcriptions = []

            video_id = extract.video_id(link)
            
            for i, chunk in enumerate(chunks):
                chunk.export(f'output/chunk_{i}_{video_id}.mp3', format='mp3')
                transcriptions.append(load_whisper_api(f'output/chunk_{i}_{video_id}.mp3'))

            results = ','.join(transcriptions)


    st.info("`YT Video transcription process complete...`")

    return results, title

@st.cache_data
def inference(link, upload):
    '''Convert Youtube video or Audio upload to text'''
    
    try:
        
        if validators.url(link):

            st.info("`Downloading YT audio...`")
            
            results, title = transcribe_yt_video(link)

            return results, title

        elif _upload:

            #Get size of audio file
            audio_size = round(os.path.getsize(_upload)/(1024*1024),1)
    
            #Check if file is > 24mb, if not then use Whisper API
            if audio_size <= 25:

                st.info("`Transcribing uploaded audio...`")
                
                #Use whisper API
                results = load_whisper_api(_upload)['text']
    
            else:
    
                st.write('File size larger than 24mb, applying chunking and transcription')
    
                song = AudioSegment.from_file(_upload)
    
                # PyDub handles time in milliseconds
                twenty_minutes = 20 * 60 * 1000
                
                chunks = song[::twenty_minutes]
                
                transcriptions = []

                st.info("`Transcribing uploaded audio...`")

                for i, chunk in enumerate(chunks):
                    chunk.export(f'output/chunk_{i}.mp4', format='mp4')
                    transcriptions.append(load_whisper_api(f'output/chunk_{i}.mp4')['text'])
    
                results = ','.join(transcriptions)

            st.info("`Uploaded audio transcription process complete...`")

            return results, "Transcribed Earnings Audio"
                
    except Exception as e:

        st.error(f'''PyTube Error: {e}, 
                    Using yt_dlp module, might take longer than expected''',icon="🚨")

        results, title = transcribe_yt_video(link, py_tube=False)
        
        # results = _asr_model.transcribe(st.session_state['audio'], task='transcribe', language='en')
      
        return results, title

@st.cache_resource
def send_feedback(run_id, score):
    client.create_feedback(run_id, "user_score", score=score)
    
@st.cache_data
def clean_text(text):
    '''Clean all text after inference'''

    text = text.encode("ascii", "ignore").decode()  # unicode
    text = re.sub(r"https*\S+", " ", text)  # url
    text = re.sub(r"@\S+", " ", text)  # mentions
    text = re.sub(r"#\S+", " ", text)  # hastags
    text = re.sub(r"\s{2,}", " ", text)  # over spaces
    
    return text

@st.cache_data
def chunk_long_text(text,threshold,window_size=3,stride=2):
    '''Preprocess text and chunk for sentiment analysis'''
    
    #Convert cleaned text into sentences
    sentences = sent_tokenize(text)
    out = []

    #Limit the length of each sentence to a threshold
    for chunk in sentences:
        if len(chunk.split()) < threshold:
            out.append(chunk)
        else:
            words = chunk.split()
            num = int(len(words)/threshold)
            for i in range(0,num*threshold+1,threshold):
                out.append(' '.join(words[i:threshold+i]))
    
    passages = []
    
    #Combine sentences into a window of size window_size
    for paragraph in [out]:
        for start_idx in range(0, len(paragraph), stride):
            end_idx = min(start_idx+window_size, len(paragraph))
            passages.append(" ".join(paragraph[start_idx:end_idx]))
            
    return passages  

@st.cache_data
def sentiment_pipe(earnings_text):
    '''Determine the sentiment of the text'''
    
    earnings_sentences = chunk_long_text(earnings_text,150,1,1)
    earnings_sentiment = sent_pipe(earnings_sentences)
    
    return earnings_sentiment, earnings_sentences 

@st.cache_data
def chunk_and_preprocess_text(text, model_name= 'philschmid/flan-t5-base-samsum'):

    '''Chunk and preprocess text for summarization'''
    
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    sentences = sent_tokenize(text)
    
    # initialize
    length = 0
    chunk = ""
    chunks = []
    count = -1
    
    for sentence in sentences:
        count += 1
        combined_length = len(tokenizer.tokenize(sentence)) + length # add the no. of sentence tokens to the length counter
    
        if combined_length  <= tokenizer.max_len_single_sentence: # if it doesn't exceed
            chunk += sentence + " " # add the sentence to the chunk
            length = combined_length # update the length counter
    
            # if it is the last sentence
            if count == len(sentences) - 1:
                chunks.append(chunk) # save the chunk
      
        else: 
            chunks.append(chunk) # save the chunk
            # reset 
            length = 0 
            chunk = ""
        
            # take care of the overflow sentence
            chunk += sentence + " "
            length = len(tokenizer.tokenize(sentence))

    return chunks

@st.cache_data
def summarize_text(text_to_summarize,max_len,min_len):
    '''Summarize text with HF model'''
    
    summarized_text = sum_pipe(text_to_summarize,
                               max_length=max_len,
                               min_length=min_len,
                               do_sample=False, 
                               early_stopping=True,
                              num_beams=4)
    summarized_text = ' '.join([summ['summary_text'] for summ in summarized_text])
     
    return summarized_text 

@st.cache_data 	
def get_all_entities_per_sentence(text):
    doc = nlp(''.join(text))

    sentences = list(doc.sents)

    entities_all_sentences = []
    for sentence in sentences:
        entities_this_sentence = []

        # SPACY ENTITIES
        for entity in sentence.ents:
            entities_this_sentence.append(str(entity))

        # XLM ENTITIES
        entities_xlm = [entity["word"] for entity in ner_pipe(str(sentence))]
        for entity in entities_xlm:
            entities_this_sentence.append(str(entity))

        entities_all_sentences.append(entities_this_sentence)

    return entities_all_sentences

@st.cache_data 
def get_all_entities(text):
    all_entities_per_sentence = get_all_entities_per_sentence(text)
    return list(itertools.chain.from_iterable(all_entities_per_sentence))

@st.cache_data    
def get_and_compare_entities(article_content,summary_output):
    
    all_entities_per_sentence = get_all_entities_per_sentence(article_content)
    entities_article = list(itertools.chain.from_iterable(all_entities_per_sentence))
   
    all_entities_per_sentence = get_all_entities_per_sentence(summary_output)
    entities_summary = list(itertools.chain.from_iterable(all_entities_per_sentence))
   
    matched_entities = []
    unmatched_entities = []
    for entity in entities_summary:
        if any(entity.lower() in substring_entity.lower() for substring_entity in entities_article):
            matched_entities.append(entity)
        elif any(
                np.inner(sbert.encode(entity, show_progress_bar=False),
                         sbert.encode(art_entity, show_progress_bar=False)) > 0.9 for
                art_entity in entities_article):
            matched_entities.append(entity)
        else:
            unmatched_entities.append(entity)

    matched_entities = list(dict.fromkeys(matched_entities))
    unmatched_entities = list(dict.fromkeys(unmatched_entities))

    matched_entities_to_remove = []
    unmatched_entities_to_remove = []

    for entity in matched_entities:
        for substring_entity in matched_entities:
            if entity != substring_entity and entity.lower() in substring_entity.lower():
                matched_entities_to_remove.append(entity)

    for entity in unmatched_entities:
        for substring_entity in unmatched_entities:
            if entity != substring_entity and entity.lower() in substring_entity.lower():
                unmatched_entities_to_remove.append(entity)

    matched_entities_to_remove = list(dict.fromkeys(matched_entities_to_remove))
    unmatched_entities_to_remove = list(dict.fromkeys(unmatched_entities_to_remove))

    for entity in matched_entities_to_remove:
        matched_entities.remove(entity)
    for entity in unmatched_entities_to_remove:
        unmatched_entities.remove(entity)

    return matched_entities, unmatched_entities

@st.cache_data 
def highlight_entities(article_content,summary_output):
   
    markdown_start_red = "<mark class=\"entity\" style=\"background: rgb(238, 135, 135);\">"
    markdown_start_green = "<mark class=\"entity\" style=\"background: rgb(121, 236, 121);\">"
    markdown_end = "</mark>"

    matched_entities, unmatched_entities = get_and_compare_entities(article_content,summary_output)
    
    for entity in matched_entities:
        summary_output = re.sub(f'({entity})(?![^rgb\(]*\))',markdown_start_green + entity + markdown_end,summary_output)

    for entity in unmatched_entities:
        summary_output = re.sub(f'({entity})(?![^rgb\(]*\))',markdown_start_red + entity + markdown_end,summary_output)
    
    print("")
    print("")
    
    soup = BeautifulSoup(summary_output, features="html.parser")

    return HTML_WRAPPER.format(soup)

def summary_downloader(raw_text):
    '''Download the summary generated'''
    
    b64 = base64.b64encode(raw_text.encode()).decode()
    new_filename = "new_text_file_{}_.txt".format(time_str)
    st.markdown("#### Download Summary as a File ###")
    href = f'<a href="data:file/txt;base64,{b64}" download="{new_filename}">Click to Download!!</a>'
    st.markdown(href,unsafe_allow_html=True)

@st.cache_data
def generate_eval(raw_text, N, chunk):

    # Generate N questions from context of chunk chars
    # IN: text, N questions, chunk size to draw question from in the doc
    # OUT: eval set as JSON list

    # raw_text = ','.join(raw_text)
    
    update = st.empty()
    ques_update = st.empty()
    update.info("`Generating sample questions ...`")
    n = len(raw_text)
    starting_indices = [random.randint(0, n-chunk) for _ in range(N)]
    sub_sequences = [raw_text[i:i+chunk] for i in starting_indices]
    chain = QAGenerationChain.from_llm(ChatOpenAI(temperature=0))
    eval_set = []
    
    for i, b in enumerate(sub_sequences):
        try:
            qa = chain.run(b)
            eval_set.append(qa)
            ques_update.info(f"Creating Question: {i+1}")

        except Exception as e:
            print(e)
            st.warning(f'Error in generating Question: {i+1}...', icon="⚠️")
            continue
        
    eval_set_full = list(itertools.chain.from_iterable(eval_set))
    
    update.empty()
    ques_update.empty()

    return eval_set_full

@st.cache_resource
def create_prompt_and_llm():
    '''Create prompt'''

    llm = ChatOpenAI(temperature=0, streaming=True, model="gpt-4o")

    message = SystemMessage(
        content=(
            "You are a helpful chatbot who is tasked with answering questions acuurately about earnings call transcript provided. "
            "Unless otherwise explicitly stated, it is probably fair to assume that questions are about the earnings call transcript. "
            "If there is any ambiguity, you probably assume they are about that."
            "Do not use any information not provided in the earnings context and remember you are a to speak like a finance expert."
            "If you don't know the answer, just say 'There is no relevant answer in the given earnings call transcript'" 
            "don't try to make up an answer"
        )
    )

    prompt = OpenAIFunctionsAgent.create_prompt(
        system_message=message,
        extra_prompt_messages=[MessagesPlaceholder(variable_name="history")],
    )

    return prompt, llm
    
@st.cache_resource
def gen_embeddings(embedding_model):

    '''Generate embeddings for given model'''
    
    if 'hkunlp' in embedding_model:
        
        embeddings = HuggingFaceInstructEmbeddings(model_name=embedding_model,
                                           query_instruction='Represent the Financial question for retrieving supporting paragraphs: ',
                                           embed_instruction='Represent the Financial paragraph for retrieval: ')

    elif 'mpnet' in embedding_model:
        
        embeddings = HuggingFaceEmbeddings(model_name=embedding_model)

    elif 'FlagEmbedding' in embedding_model:

        encode_kwargs = {'normalize_embeddings': True}
        embeddings = HuggingFaceBgeEmbeddings(model_name=embedding_model,
                                                   encode_kwargs = encode_kwargs
                                                   )

    return embeddings
    
@st.cache_data
def create_vectorstore(corpus, title, embedding_model, chunk_size=1000, overlap=50):

    '''Process text for Semantic Search'''
    
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size,chunk_overlap=overlap)

    texts = text_splitter.split_text(corpus)

    embeddings = gen_embeddings(embedding_model)

    vectorstore = FAISS.from_texts(texts, embeddings, metadatas=[{"source": i} for i in range(len(texts))])

    return vectorstore

@st.cache_resource
def create_memory_and_agent(_docsearch):
    
    '''Embed text and generate semantic search scores'''

    #create vectorstore
    vectorstore = _docsearch.as_retriever(search_kwargs={"k": 4})

    #create retriever tool
    tool = create_retriever_tool(
    vectorstore,
    "earnings_call_search",
    "Searches and returns documents using the earnings context provided as a source, relevant to the user input question.",
    )

    tools = [tool]

    prompt,llm = create_prompt_and_llm()

    agent = OpenAIFunctionsAgent(llm=llm, tools=tools, prompt=prompt)
    
    agent_executor = AgentExecutor(
        agent=agent,
        tools=tools,
        verbose=True,
        return_intermediate_steps=True,
    )
    
    memory = AgentTokenBufferMemory(llm=llm)
        
    return memory, agent_executor

@st.cache_data
def gen_sentiment(text):
    '''Generate sentiment of given text'''
    return sent_pipe(text)[0]['label']

@st.cache_data 
def gen_annotated_text(df):
    '''Generate annotated text'''
    
    tag_list=[]
    for row in df.itertuples():
        label = row[2]
        text = row[1]
        if label == 'Positive':
            tag_list.append((text,label,'#8fce00'))
        elif label == 'Negative':
            tag_list.append((text,label,'#f44336'))
        else:
            tag_list.append((text,label,'#000000'))
        
    return tag_list
    
    
def display_df_as_table(model,top_k,score='score'):
    '''Display the df with text and scores as a table'''
    
    df = pd.DataFrame([(hit[score],passages[hit['corpus_id']]) for hit in model[0:top_k]],columns=['Score','Text'])
    df['Score'] = round(df['Score'],2)
    
    return df   

      
def make_spans(text,results):
    results_list = []
    for i in range(len(results)):
        results_list.append(results[i]['label'])
    facts_spans = []
    facts_spans = list(zip(sent_tokenizer(text),results_list))
    return facts_spans

##Fiscal Sentiment by Sentence
def fin_ext(text):
    results = remote_clx(sent_tokenizer(text))
    return make_spans(text,results)

## Knowledge Graphs code

def get_article(url):
    article = Article(url)
    article.download()
    article.parse()
    return article