plots.py

import numpy as np
import pandas as pd
import streamlit as st
import altair as alt
from wordcloud import WordCloud
import plotly.express as px
from textblob import TextBlob
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
import pickle

loaded_df = None

def loadData():
    query = "select * from TweetInformation"
    # df = db_execute_fetch(query, dbName="tweets", rdf=True)
    df = pd.read_csv("./st_dashboard/processed_global_data_tweets.csv") #For deployed version
    loaded_df = df
    return df

def barChart(data, title, X, Y):
    title = title.title()
    st.title(f'{title} Chart')
    msgChart = (alt.Chart(data).mark_bar().encode(alt.X(f"{X}:N", sort=alt.EncodingSortField(field=f"{Y}", op="values",
                order='ascending')), y=f"{Y}:Q"))
    st.altair_chart(msgChart, use_container_width=True)


def userMentionbarChart():
    df = loadData() if loaded_df is None else loaded_df
    df['user_mentions'] = df['user_mentions'].fillna("no_mention")
    user_mentions_list_df = df.loc[df["user_mentions"] != ""]
    user_mentions_list_df = user_mentions_list_df.loc[df["user_mentions"] != "no_mention"]
    user_mentions_list_df = user_mentions_list_df['user_mentions']
    splitted_user_mentions = []
    for mentions_list in user_mentions_list_df:
        mentions_list = mentions_list.split("++++")
        for user_mentions in mentions_list:
            if user_mentions != '':
                splitted_user_mentions.append(user_mentions)
    # print(splitted_user_mentions)
    splitted_user_mentions_df = pd.DataFrame(splitted_user_mentions, columns=['user_mentions'])
    dfUserMentionsCount = pd.DataFrame({'Tweet_count': splitted_user_mentions_df.value_counts()}).reset_index()
    # print(splitted_user_mentions_df['user_mentions'].value())
    # print(dfUserMentionsCount.head())
    dfUserMentionsCount = dfUserMentionsCount.sort_values("Tweet_count", ascending=False)
    num = st.slider("Select number of Rankings", 0, 50, 5, key=22)
    title = f"Top {num} user mentions"
    barChart(dfUserMentionsCount.head(num), title, "user_mentions", "Tweet_count")

def stBarChart():
    df = loadData() if loaded_df is None else loaded_df
    dfCount = pd.DataFrame({'Tweet_count': df.groupby(['original_author'])['full_text'].count()}).reset_index()
    dfCount["original_author"] = dfCount["original_author"].astype(str)
    dfCount = dfCount.sort_values("Tweet_count", ascending=False)

    num = st.slider("Select number of Rankings", 0, 50, 5)
    title = f"Top {num} Ranking By Number of tweets"
    barChart(dfCount.head(num), title, "original_author", "Tweet_count")

def sentimentPie():
    df = loadData() if loaded_df is None else loaded_df
    dfSentimentCount = pd.DataFrame({'Tweet_count': df.groupby(['sentiment'])['full_text'].count()}).reset_index()
    dfSentimentCount['source'] = dfSentimentCount['sentiment'].astype(str)
    dfSentimentCount = dfSentimentCount.sort_values("Tweet_count", ascending=False)
    dfSentimentCount.loc[dfSentimentCount['Tweet_count'] < 10, 'sentiment'] = 'Other Value'
    st.title("Tweet sentiment pie chart")
    fig = px.pie(dfSentimentCount, values='Tweet_count', names='sentiment', width=500, height=350)
    fig.update_traces(textposition='inside', textinfo='percent+label')

    colB1, colB2 = st.columns([2.5, 1])

    with colB1:
        st.plotly_chart(fig)
    with colB2:
        st.write(dfSentimentCount)

def locationPie():
    df = loadData() if loaded_df is None else loaded_df
    df = df[df['place']!='not_known']
    df = df[df['place']!=' ']
    dfLocationCount = pd.DataFrame({'Tweet_count': df.groupby(['place'])['full_text'].count()}).reset_index()
    dfLocationCount['place'] = dfLocationCount['place'].astype(str)
    dfLocationCount = dfLocationCount[dfLocationCount['Tweet_count']>9]
    dfLocationCount = dfLocationCount.sort_values("Tweet_count", ascending=False)
    # dfLocationCount.loc[dfLocationCount['Tweet_count'] < 10, 'place'] = 'Other sources'
    st.title("Top 15 tweet location pie chart")
    fig = px.pie(dfLocationCount.head(15), values='Tweet_count', names='place', width=500, height=350)
    fig.update_traces(textposition='inside', textinfo='percent+label')

    colB1, colB2 = st.columns([2.5, 1])

    with colB1:
        st.plotly_chart(fig)
    with colB2:
        st.write(dfLocationCount)

def sourcePie():
    df = loadData() if loaded_df is None else loaded_df
    dfSourceCount = pd.DataFrame({'Tweet_count': df.groupby(['source'])['full_text'].count()}).reset_index()
    dfSourceCount['source'] = dfSourceCount['source'].astype(str)
    dfSourceCount = dfSourceCount.sort_values("Tweet_count", ascending=False)
    dfSourceCount.loc[dfSourceCount['Tweet_count'] < 10, 'source'] = 'Other sources'
    st.title("Tweet source pie chart")
    fig = px.pie(dfSourceCount, values='Tweet_count', names='source', width=500, height=350)
    fig.update_traces(textposition='inside', textinfo='percent+label')

    colB1, colB2 = st.columns([2.5, 1])

    with colB1:
        st.plotly_chart(fig)
    with colB2:
        st.write(dfSourceCount)

def langPie():
    df = loadData() if loaded_df is None else loaded_df
    #For deployed version replace all "language" with "lang"
    dfLangCount = pd.DataFrame({'Tweet_count': df.groupby(['lang'])['full_text'].count()}).reset_index()
    dfLangCount["lang"] = dfLangCount["lang"].astype(str)
    dfLangCount = dfLangCount.sort_values("Tweet_count", ascending=False)
    dfLangCount.loc[dfLangCount['Tweet_count'] < 10, 'lang'] = 'Other languages'
    st.title(" Tweets Language pie chart")
    fig = px.pie(dfLangCount, values='Tweet_count', names='lang', width=500, height=350)
    fig.update_traces(textposition='inside', textinfo='percent+label')

    colB1, colB2 = st.columns([2.5, 1])

    with colB1:
        st.plotly_chart(fig)
    with colB2:
        st.write(dfLangCount)