Error loading dependencies message when trying to get a datatable from callback function

Dash Python
1

Hello, I am getting error loading dependencies error when running this code. This is supposed to be a webscraper where the user inputs the search term and amount of text to scrape.

import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output, State
import dash_table
import pandas as pd
import praw #for Reddit threads
from psaw import PushshiftAPI #for Reddit comments
import twint #for tweets
import nest_asyncio

nest_asyncio.apply()

app = dash.Dash()
app.scripts.config.serve_locally = True
app.css.config.serve_locally = True
app.config['suppress_callback_exceptions'] = True

app.layout = html.Div([
    html.Div([

        html.Div([
            dcc.Input(
    id = "ScreenName_Input",   
    placeholder='Enter a word',
    type='text',
    value=''
),
            dcc.Input(
    id = "numby",
    placeholder='Enter a number',
    type='number',
    value=''
)
        ],style={'width': '48%', 'float': 'right', 'display': 'inline-block'})
    ]),
    
    html.Button(id='screenNames_submit_button', children='Submit'),
    
    dash_table.DataTable(
        id='tweet_table',
        columns=[
        {'name': 'Date', 'id': 'column1'},
        {'name': 'Score', 'id': 'column2'},
        {'name': 'Text', 'id': 'column3'},
        {'name': 'Upvote Ratio', 'id': 'column4'},
        {'name': 'Type', 'id': 'column5'},
        {'name': 'Likes', 'id': 'column6'},
        {'name': 'Retweets', 'id': 'column7'},
         ],
        data=[])


])

@app.callback(
    Output(component_id='tweet_table', component_property='data'),
    [Input(component_id='screenNames_submit_button', component_property='n_clicks_timestamp')],
    [State(component_id='ScreenName_Input', component_property='value')],
    [State(component_id='numby', component_property='value')]
)

def ctoscraper(button, text, number):
    #Part 1: for Reddit threads
    reddit = praw.Reddit(client_id='x6e1LTj2OQnGYw', 
                     client_secret='cKPjQfnskfy1w5IlwLi6Aos-DMU', 
                     user_agent='trialnew', 
                     username='opposity', 
                     password='redacted') 
    all = reddit.subreddit("technology+tech+futurology+engineering+army+navy+airforce+geek+military+scifi+science")
    topics_dict = { "Text":[], "Date":[],"Score":[],"Upvote_Ratio":[]}
    for submission in all.search(text, limit = number):
        topics_dict["Text"].append(submission.title)
        topics_dict["Date"].append(submission.created_utc)
        topics_dict["Score"].append(submission.score)
        topics_dict["Upvote_Ratio"].append(submission.upvote_ratio)
    topics_data = pd.DataFrame(topics_dict)
    topics_data['Date'] = (pd.to_datetime(topics_data['Date'], unit='s'))
    listeforlabel = ['Reddit Thread'] * number
    dflisteforlabel = pd.Series(listeforlabel)
    upnewdf = pd.concat([topics_data, dflisteforlabel], axis=1)

    #Part 2: for Reddit comments
    subbies = ["technology","tech","futurology","engineering","army","navy","airforce","geek","military","scifi","science"]
    api = PushshiftAPI()
    gen = api.search_comments(q = text, subreddit = subbies)
    cache = []
    for c in gen:
        cache.append(c)
        if len(cache) >= number:
            break
    comments_dict = { "Text":[], "Date":[]}
    for x in cache:
        comments_dict["Text"].append(str(x[14]))
        comments_dict["Date"].append(x[16])
    commentdf = pd.DataFrame(comments_dict)
    commentdf['Date'] = (pd.to_datetime(commentdf['Date'], unit='s', errors = "coerce"))
    listforlabel = ['Reddit Comment'] * number
    dflistforlabel = pd.Series(listforlabel)
    newdf = pd.concat([commentdf, dflistforlabel], axis=1)
    
    #Part 3: for Tweets
    c = twint.Config()
    c.Search = text
    c.Limit = number
    c.Pandas = True
    #c.Since 
    #c.Until
    twint.run.Search(c)
    Tweets_df = twint.storage.panda.Tweets_df
    necessary_text = Tweets_df.tweet
    necessary_date = Tweets_df.date
    necessary_likes = Tweets_df.nlikes
    necessary_retweets = Tweets_df.nretweets 
    tweetlabel = ['Tweet'] * len(necessary_text)
    tweforlabel = pd.Series(tweetlabel)
    lastlabel = pd.concat([necessary_text, necessary_date], axis=1)
    finallabel = pd.concat([lastlabel,tweforlabel], axis = 1)
    finallabel.rename(columns = {"tweet":"Text","date":"Date"}, inplace = True)
    final2label = pd.concat([finallabel, necessary_likes], axis=1)
    final3label = pd.concat([final2label, necessary_retweets], axis=1)
    
    #Combine Part 1, Part 2, Part 3
    frames = [upnewdf, newdf,final3label]
    result = pd.concat(frames)
    result.columns = ["Date","Score","Text", "Upvote Ratio", "Type","Likes","Retweets"]
    return result.to_dict(orient = 'records')




if __name__ == '__main__':
    app.run_server(debug=True)