import pandas as pd
import numpy as np
import re
import snscrape.modules.twitter as sntwitter
from transformers import pipeline
import plotly.express as px
import joblib
from sklearn.metrics import classification_report, confusion_matrix


import nltk

nltk.download("punkt")
nltk.download("stopwords")
from nltk.tokenize import word_tokenize


import tweepy


CONSUMER_KEY = "i2ddrPeTiP8jQA5K3sEiTc5eP"
CONSUMER_SECRET = "E12e5uzXL2QLCAqpuJh4Gq4X7NHRcwhemnHK6776gWJvAJ5bgS"
ACCESS_TOKEN = "1490990154647216130-h5aGUUh2WxQIKrXfQ86YG2w4XNBAEQ"
ACCESS_TOKEN_SECRET = "LsKD8BNebh7vs3QADhXEjWKYM4XqFLrNYP6uXrFHu91cS"


def get_tweets(username, length=10, option=None):
    # Creating list to append tweet data to
    query = username + " -filter:links filter:replies lang:id"
    if option == "Advanced":
        query = username
    tweets = []

    # Authenticate to Twitter API
    auth = tweepy.OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)
    auth.set_access_token(ACCESS_TOKEN, ACCESS_TOKEN_SECRET)
    api = tweepy.API(auth)

    # Fetch tweets
    for i, tweet in enumerate(tweepy.Cursor(api.search_tweets, q=query).items(length)):
        tweets.append([tweet.text])

    # Creating a dataframe from the tweets list above
    tweets_df = pd.DataFrame(tweets, columns=["content"])
    tweets_df["content"] = tweets_df["content"].str.replace("@[^\s]+", "")
    tweets_df["content"] = tweets_df["content"].str.replace("#[^\s]+", "")
    tweets_df["content"] = tweets_df["content"].str.replace("http\S+", "")
    tweets_df["content"] = tweets_df["content"].str.replace("pic.twitter.com\S+", "")
    tweets_df["content"] = tweets_df["content"].str.replace("RT", "")
    tweets_df["content"] = tweets_df["content"].str.replace("amp", "")
    # remove emoticon
    tweets_df["content"] = tweets_df["content"].str.replace(
        "[^\w\s#@/:%.,_-]", "", flags=re.UNICODE
    )

    # remove whitespace leading & trailing
    tweets_df["content"] = tweets_df["content"].str.strip()

    # remove multiple whitespace into single whitespace
    tweets_df["content"] = tweets_df["content"].str.replace("\s+", " ")

    # remove row with empty content
    tweets_df = tweets_df[tweets_df["content"] != ""]
    return tweets_df


def get_sentiment(df, option_model):
    id2label = {0: "negatif", 1: "netral", 2: "positif"}
    if option_model == "IndoBERT (Accurate,Slow)":
        classifier = pipeline("sentiment-analysis", model="indobert")
        df["sentiment"] = df["content"].apply(
            lambda x: id2label[classifier(x)[0]["label"]]
        )
    elif option_model == "Logistic Regression (Less Accurate,Fast)":
        df_model = joblib.load("assets/df_model.pkl")
        classifier = df_model[
            df_model.model_name == "Logistic Regression"
        ].model.values[0]
        df["sentiment"] = df["content"].apply(
            lambda x: id2label[classifier.predict([x])[0]]
        )
    else:
        df_model = joblib.load("assets/df_model.pkl")
        classifier = df_model[df_model.model_name == option_model].model.values[0]
        df["sentiment"] = df["content"].apply(
            lambda x: id2label[classifier.predict([x])[0]]
        )
    # change order sentiment to first column
    cols = df.columns.tolist()
    cols = cols[-1:] + cols[:-1]
    df = df[cols]

    return df


def get_bar_chart(df):
    df = df.groupby(["sentiment"]).count().reset_index()
    # plot barchart sentiment
    # plot barchart sentiment
    fig = px.bar(
        df,
        x="sentiment",
        y="content",
        color="sentiment",
        text="content",
        color_discrete_map={
            "positif": "#00cc96",
            "negatif": "#ef553b",
            "netral": "#636efa",
        },
    )
    # hide legend
    fig.update_layout(showlegend=False)
    # set margin top
    fig.update_layout(margin=dict(t=0, b=150, l=0, r=0))
    # set title in center
    # set annotation in bar
    fig.update_traces(textposition="outside")
    fig.update_layout(uniformtext_minsize=8, uniformtext_mode="hide")

    # set y axis title
    fig.update_yaxes(title_text="Jumlah Komentar")

    return fig


def plot_model_summary(df_model):
    df_scatter = df_model[df_model.set_data == "test"][["score", "time", "model_name"]]
    # plot scatter
    fig = px.scatter(
        df_scatter, x="time", y="score", color="model_name", hover_data=["model_name"]
    )
    # set xlabel to time (s)
    fig.update_xaxes(title_text="time (s)")
    # set ylabel to accuracy
    fig.update_yaxes(title_text="accuracy")

    # set point size
    fig.update_traces(marker=dict(size=10))
    fig.update_layout(autosize=False, margin=dict(t=0, l=0, r=0), height=400)
    return fig


def plot_clfr(df_model, option_model, df):
    df_clfr = pd.DataFrame(
        classification_report(df["label"], df[f"{option_model}_pred"], output_dict=True)
    )
    # heatmap using plotly
    df_clfr.columns = [
        "positif",
        "netral",
        "negatif",
        "accuracy",
        "macro_avg",
        "weighted_avg",
    ]
    fig = px.imshow(
        df_clfr.T.iloc[:, :-1],
        x=df_clfr.T.iloc[:, :-1].columns,
        y=df_clfr.T.iloc[:, :-1].index,
    )
    # remove colorbar
    fig.update_layout(coloraxis_showscale=False)
    fig.update_layout(coloraxis_colorscale="gnbu")
    # get annot
    annot = df_clfr.T.iloc[:, :-1].values
    # add annot and set font size
    fig.update_traces(text=annot, texttemplate="%{text:.2f}", textfont_size=12)
    # set title to classification report
    fig.update_layout(title_text="📄 Classification Report")
    return fig


def plot_confusion_matrix(df_model, option_model, df):
    # plot confusion matrix
    cm = confusion_matrix(df["label"], df[f"{option_model}_pred"])
    fig = px.imshow(
        cm, x=["negatif", "netral", "positif"], y=["negatif", "netral", "positif"]
    )
    # remove colorbar
    fig.update_layout(coloraxis_showscale=False)
    fig.update_layout(coloraxis_colorscale="gnbu", title_text="📊 Confusion Matrix")
    # get annot
    annot = cm
    # add annot
    fig.update_traces(text=annot, texttemplate="%{text:.0f}", textfont_size=15)
    return fig