import streamlit as st
from variables import *
from optimum.onnxruntime import ORTModelForSequenceClassification
from transformers import pipeline, AutoTokenizer
from optimum.pipelines import pipeline
import tweepy
import pandas as pd
import numpy as np
import plotly_express as px
import plotly.graph_objects as go
from datetime import datetime as dt
from st_aggrid import GridOptionsBuilder, AgGrid, GridUpdateMode, DataReturnMode
from datasets import Dataset
from huggingface_hub import Repository

st.set_page_config(
    page_title="Live FinTwitter Analysis",
    page_icon="📈",
    layout="wide",
)

st.sidebar.header("Sentiment Analysis Score")

extract_time = dt.strftime(dt.today(),"%d_%B_%y_%H_%M")

DATASET_REPO_URL = "https://huggingface.co/datasets/nickmuchi/fin_tweets"

DATA_FILENAME = "tweets_data.csv"
DATA_FILE = os.path.join("data", DATA_FILENAME)
tweet_file = os.path.join("tweets", DATA_FILE)

repo = Repository(
    local_dir="tweets", clone_from=DATASET_REPO_URL
)
    
sentiment_classifier, topic_classifier = load_models()
    
st.title('Live FinTwitter Sentiment & Topic Analysis with Tweepy and Transformers')

st.markdown(
    """
    This app uses Tweepy to extract tweets from twitter based on a list of popular accounts that tweet about markets/finance: 
    - The stream of tweets is processed via HuggingFace models for finance tweet sentiment and topic analysis:  
        - [Topic Classification](https://huggingface.co/nickmuchi/finbert-tone-finetuned-finance-topic-classification)  
        - [Sentiment Analysis](https://huggingface.co/nickmuchi/finbert-tone-finetuned-fintwitter-classification)  
    - The resulting sentiments and corresponding tweets are displayed, with graphs tracking the live sentiment and topics of financial market tweets in the Visualisation tab.  
    """
    )
    
refresh_stream = st.button('Refresh Stream')

if "update_but" not in st.session_state:
    st.session_state.update_but = False

if refresh_stream or st.session_state.update_but:
    st.session_state.update_but = True
    
    client = tweepy.Client(CONFIG['bearer_token'], wait_on_rate_limit=True)
    
    users = []
    all_tweets = []
    
    for res in tweepy.Paginator(client.get_list_tweets,
        id="1083517925049266176",
        user_fields=['username'],
        tweet_fields=['created_at','text'],
        expansions=['author_id'],
        max_results=100):
        
        all_tweets.append(res)
    

    with st.spinner('Generating sentiment and topic classification of tweets...'):

        tweets = [response.data for response in all_tweets]
        users = [response.includes['users'] for response in all_tweets]

        flat_users = [x for i in users for x in i]
        flat_tweets = [x for i in tweets for x in i]
        
        data = [(tweet.data['author_id'],tweet.data['text'],tweet.data['created_at']) for tweet in flat_tweets]
        df = pd.DataFrame(data,columns=['author','tweet','creation_time'])
        
        df['tweet'] = df['tweet'].replace(r'https?://\S+', '', regex=True).replace(r'www\S+', '', regex=True)
               
        users = client.get_users(ids=df['author'].unique().tolist())

        df_users = pd.DataFrame(data=list(set([(user.id,user.username) for user in users.data])),columns=['author','username'])

        df_tweets = process_tweets(df,df_users)

        #appending the new dataframe to csv
        df_tweets.to_csv(tweet_file, mode='a', header=False, index=False)
        
        # Get all tweets
        tweet_list = df_tweets['tweet'].tolist()

        st.session_state['tlist'] = df_tweets
    
        st.session_state['tdf'] = df_tweets

    with st.container():

        st.write("Table of Influential FinTweets")

        gb = GridOptionsBuilder.from_dataframe(df_tweets)
        gb.configure_pagination(paginationPageSize=30,paginationAutoPageSize=False) #Add pagination
        gb.configure_side_bar() #Add a sidebar
        gb.configure_selection('multiple', use_checkbox=True, groupSelectsChildren="Group checkbox select children")
        gb.configure_column('tweet',wrapText=True,autoHeight=True)#Enable multi-row selection
        gridOptions = gb.build()
        
        AgGrid(
            df_tweets,
            gridOptions=gridOptions,
            data_return_mode='AS_INPUT', 
            update_mode='MODEL_CHANGED', 
            fit_columns_on_grid_load=False,
            enable_enterprise_modules=True,
            theme='streamlit', #Add theme color to the table
            height=550, 
            width='100%'        
        )
        
    ## Display sentiment score
    pos_perc = df_tweets[df_tweets['sentiment']=='Bullish'].count()[0]*100/df_tweets.shape[0]
    neg_perc = df_tweets[df_tweets['sentiment']=='Bearish'].count()[0]*100/df_tweets.shape[0]
    neu_perc = df_tweets[df_tweets['sentiment']=='Neutral'].count()[0]*100/df_tweets.shape[0]
    
    sentiment_score = neu_perc+pos_perc-neg_perc
    
    fig_1 = go.Figure()
    
    fig_1.add_trace(go.Indicator(
        mode = "delta",
        value = sentiment_score,
        domain = {'row': 1, 'column': 1}))
    
    fig_1.update_layout(
        template = {'data' : {'indicator': [{
            'title': {'text': "Sentiment Score"},
            'mode' : "number+delta+gauge",
            'delta' : {'reference': 50}}]
                             }},
        autosize=False,
        width=250,
        height=250,
        margin=dict(
            l=5,
            r=5,
            b=5,
            pad=2
        )
    )
    
    with st.sidebar:
    
        st.plotly_chart(fig_1)

st.markdown("![visitor badge](https://visitor-badge.glitch.me/badge?page_id=nickmuchi-fintweet-sentiment-analysis)")