app.py

import os
from git import Repo
import streamlit as st 
import time
from PIL import Image
import base64
from transformers import pipeline
import spacy
import googleapiclient
import numpy as np
from sentence_transformers import SentenceTransformer
from matplotlib import colormaps
from matplotlib.colors import ListedColormap

GITHUB_PAT = os.environ['GITHUB']
SENTIMENT = os.environ['SENTIMENT']
EMBEDDING = os.environ['EMBEDDING']

if not os.path.exists('repo_directory'):
 try:
 Repo.clone_from(f'https://marcus-t-s:{GITHUB_PAT}@github.com/marcus-t-s/yt-comment-analyser.git', 'repo_directory' ) 
 except:
 st.error("Error: Oops there's an issue on our end, please wait a moment and try again.")
 st.stop()

from repo_directory.all_utils import *


# Streamlit configuration
st.set_page_config(
 page_title="ViewerVoice | YouTube Comment Analyser",
 layout="wide",
 page_icon=Image.open('page_icon.png')
)


# Define and load cached resources
@st.cache_resource
def load_models():
 sentiment_pipeline = pipeline("sentiment-analysis", model=f'{SENTIMENT}')
 embedding_model = SentenceTransformer(f'{EMBEDDING}')
 spacy_nlp = spacy.load("en_core_web_sm")
 add_custom_stopwords(spacy_nlp, {"bring", "know", "come"})
 return sentiment_pipeline, embedding_model, spacy_nlp


@st.cache_resource
def load_colors_image():
 mask = np.array(Image.open('youtube_icon.jpg'))
 Reds = colormaps['Reds']
 colors = ListedColormap(Reds(np.linspace(0.4, 0.8, 256)))
 with open("viewervoice_logo_crop.png", "rb") as img_file:
 logo_image = base64.b64encode(img_file.read()).decode("utf-8")
 return mask, colors, logo_image


sentiment_pipeline, embedding_model, spacy_nlp = load_models()
mask, colors, logo_image = load_colors_image()


# Hide line at the top and "made with streamlit" text
hide_decoration_bar_style = """
 <style>
 header {visibility: hidden;}
 footer {visibility: hidden;}
 </style>
"""
st.markdown(hide_decoration_bar_style, unsafe_allow_html=True)

main_page = st.container()

if 'YouTubeParser' not in st.session_state:
 st.session_state['YouTubeParser'] = YoutubeCommentParser()
if 'comment_fig' not in st.session_state:
 st.session_state["comment_fig"] = None
 st.session_state["wordcloud_fig"] = None
 st.session_state["topic_fig"] = None
 st.session_state["sentiment_fig"] = None
if 'rerun_button' not in st.session_state:
 st.session_state['rerun_button'] = "INIT"
if 'topic_filter' not in st.session_state:
 st.session_state['topic_filter'] = False
if 'sentiment_filter' not in st.session_state:
 st.session_state['sentiment_filter'] = False
if 'filter_state' not in st.session_state:
 st.session_state['filter_state'] = "INIT"
if 'video_link' not in st.session_state:
 st.session_state["video_link"] = None
if 'num_comments' not in st.session_state:
 st.session_state['num_comments'] = None

# Set reference to YouTubeParser object for more concise code
yt_parser = st.session_state['YouTubeParser']


def query_comments_button():
 # Delete larger objects from session state to later replace
 del st.session_state["comment_fig"]
 del st.session_state["wordcloud_fig"]
 del st.session_state["topic_fig"]
 del st.session_state["sentiment_fig"]
 del st.session_state["YouTubeParser"]

 # Reset session state variables back to placeholder values
 st.session_state.rerun_button = "QUERYING"
 st.session_state['filter_state'] = "INIT"
 st.session_state["topic_filter"] = False
 st.session_state["sentiment_filter"] = False
 st.session_state["semantic_filter"] = False
 st.session_state["figures_built"] = False
 st.session_state["comment_fig"] = None
 st.session_state["wordcloud_fig"] = None
 st.session_state["topic_fig"] = None
 st.session_state["sentiment_fig"] = None
 st.session_state["YouTubeParser"] = YoutubeCommentParser()


def filter_visuals_button():
 st.session_state["filter_state"] = "FILTERING"


with st.sidebar:
 st.session_state["api_key"] = st.text_input('YouTube API key', value="", type='password')
 st.session_state["video_link"] = st.text_input('YouTube Video URL', value="")
 st.session_state["max_comments"] = st.slider(label="Maximum number of comments to query",
 min_value=100,
 max_value=3000,
 step=100)
 st.session_state["max_topics"] = st.slider(label="Maximum number of topics",
 min_value=5,
 max_value=20,
 step=1)
 st.button('Query comments :left_speech_bubble:', on_click=query_comments_button)

with main_page:
 # Reduce space at the top
 reduce_header_height_style = """
 <style>
 div.block-container {padding-top:0rem;}
 div.block-container {padding-bottom:1rem;}
 div.block-container {padding-left:1.5rem;}
 </style>
 """
 st.markdown(reduce_header_height_style, unsafe_allow_html=True)

 # Title and intro section
 markdown_content = f"""
 <div style='display: flex; align-items: center; justify-content: center;'>
 <img src='data:image/png;base64,{logo_image}' height='135px';/>
 </div>
 """
 st.markdown(markdown_content, unsafe_allow_html=True)

 # LinkedIn links
 lnk = '<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css">'
 st.markdown(lnk + """
 <div style="display: flex; justify-content: center; align-items: center; flex-direction: column;">
 <br>
 <p style="text-align: center;"><b>Made by</b>
 <b>
 <a href='https://www.linkedin.com/in/afiba-7715ab166/' style="text-decoration: none">
 <i class='fa fa-linkedin-square'></i>&nbsp;<span style='color: #000000'>Afiba Annor</span></a> 
 <a href='https://www.linkedin.com/in/marcus-singh-305927172/' style="text-decoration: none">
 <i class='fa fa-linkedin-square'></i>&nbsp;<span style='color: #000000'>Marcus Singh</span></a>
 </b></p>
 </div>

 """, unsafe_allow_html=True)

 st.markdown("<hr>", unsafe_allow_html=True)
 # Notes section
 st.markdown("<p style='font-size: 1.3rem;'><b>📝 Notes</b></p>", unsafe_allow_html=True)

 html_content = """
 <ul>
 <li style='font-size: 0.95rem;'>This dashboard is still under development; further updates will be implemented 
 in due course.</li>
 <li style='font-size: 0.95rem;'>Kindly refer to the instructions provided in this 
 <a href='https://developers.google.com/youtube/v3/getting-started'>link</a>. 
 This will guide you in acquiring your API key to retrieve comments.</li>
 <li style='font-size: 0.95rem;'>Please be aware that each API key facilitates up to 10,000 API calls within 
 a 24-hour period.</li>
 <li style='font-size: 0.95rem;'>Currently, the dashboard caters to comments in English and does not 
 include comment replies.</li>
 <li style='font-size: 0.95rem;'>Comments undergo cleaning and pre-processing to optimise modelling. As a result, 
 the returned comment count may fall short of the maximum queried amount.</li>
 <li style='font-size: 0.95rem;'>Please note that the sentiment analysis currently does not take emojis into 
 account.</li>
 <li style='font-size: 0.95rem;'>For optimal performance of the current topic model, we recommend retrieving 
 thousands of comments.</li>
 <li style='font-size: 0.95rem;'>Please anticipate that querying comments and running the models may require 
 a few minutes to complete.</li>
 </ul>
 <hr>
 """
 # Display the HTML content using st.markdown()
 st.markdown(html_content, unsafe_allow_html=True)

# Query comments section
if (st.session_state.rerun_button == "QUERYING") and (st.session_state["video_link"] is not None):
 with st.spinner('Querying comments and running models'):
 yt_parser = st.session_state["YouTubeParser"]
 try:
 yt_parser.build_youtube_api(st.session_state['api_key'])
 except:
 st.error("Error: Unable to query comments, please check your API key")
 st.stop()
 try:
 yt_parser.query_comments(st.session_state['video_link'], st.session_state['max_comments'])
 except googleapiclient.errors.HttpError:
 st.error("Error: Unable to query comments, please check your API key.")
 st.stop()
 except:
 st.error("Error: Unable to query comments, incorrect YouTube URL or maximum \
 API call limit reached.")
 st.stop()

 # Run formatting and models
 yt_parser.format_comments()
 yt_parser.clean_comments()
 yt_parser.run_sentiment_pipeline(sentiment_pipeline)
 yt_parser.run_topic_modelling_pipeline(embedding_model,
 nlp=spacy_nlp,
 max_topics=st.session_state['max_topics'])
 # Set "QUERY COMPLETE" to bypass running this section on script re-run
 st.session_state.rerun_button = "QUERY COMPLETE"

# Once comments are queried, build charts ready to visualise
if st.session_state.rerun_button == "QUERY COMPLETE":
 # Check for built figures:
 if (not st.session_state["figures_built"]) or (st.session_state.filter_state == "FILTERING"):
 # Select colors for wordcloud

 # If filtering button pressed
 if st.session_state.filter_state == "FILTERING":
 df_filtered = yt_parser.df_comments.copy()
 if st.session_state["topic_filter"]:
 df_filtered = df_filtered.query(f"Topic == {st.session_state.topic_filter}")
 if st.session_state["sentiment_filter"]:
 df_filtered = df_filtered.query(f"Sentiment == {st.session_state.sentiment_filter}")
 if st.session_state["semantic_filter"]:
 df_filtered = semantic_search(df=df_filtered, query=st.session_state["semantic_filter"],
 embedding_model=embedding_model,
 text_col='Comment_Clean')
 if len(df_filtered) == 0:
 st.session_state['num_comments'] = 0

 else:
 st.session_state['num_comments'] = len(df_filtered)
 # Build filtered table figure
 st.session_state["table_fig"] = comments_table(df_filtered,
 ['publishedAt', 'Comment_Formatted', 'Likes',
 'Sentiment', 'Topic'],
 {'publishedAt': 'Date', 'Comment_Formatted': 'Comment'})

 # Build filtered wordcloud figure
 st.session_state["wordcloud_fig"] = comment_wordcloud(df_filtered, mask, colors)

 # Build filtered topic figure
 st.session_state["topic_fig"] = topic_treemap(df_filtered, "Topic")

 # Build filtered sentiment figure
 st.session_state["sentiment_fig"] = sentiment_chart(df_filtered, "Sentiment")

 st.session_state["figures_built"] = True

 st.session_state.filter_state = "FILTERED"

 # No filtering selected
 else:
 st.session_state['num_comments'] = len(yt_parser.df_comments)

 # Can only build graphs if we have comments
 if st.session_state['num_comments'] > 0:
 try:
 # Build unfiltered table figure
 st.session_state["table_fig"] = comments_table(yt_parser.df_comments,
 ['publishedAt', 'Comment_Formatted', 'Likes',
 'Sentiment', 'Topic'],
 {'publishedAt': 'Date',
 'Comment_Formatted': 'Comment'})
 # Build unfiltered wordcloud figure
 st.session_state["wordcloud_fig"] = comment_wordcloud(yt_parser.df_comments,
 mask, colors)
 # Build unfiltered topic figure
 st.session_state["topic_fig"] = topic_treemap(yt_parser.df_comments, "Topic")
 # Build unfiltered sentiment figure
 st.session_state["sentiment_fig"] = sentiment_chart(yt_parser.df_comments, "Sentiment")

 st.session_state["figures_built"] = True
 except:
 st.error("Error: Oops there's an issue on our end, please wait a moment and try again.")
 st.stop()

with main_page:
 if st.session_state.rerun_button == "QUERY COMPLETE":
 st.subheader(f"{yt_parser.title}")
 st.markdown("<hr><br>", unsafe_allow_html=True)

 if st.session_state['num_comments'] > 0:
 table_col, word_cloud_col = st.columns([0.55, 0.45])
 with table_col:
 st.markdown(f"""<p style='font-size: 1.3rem;
 display: flex; align-items: center; justify-content: center;'><b>
 Comments</b></p>""", unsafe_allow_html=True)
 st.plotly_chart(st.session_state["table_fig"], use_container_width=True)

 with word_cloud_col:
 st.markdown(f"""<p style='font-size: 1.3rem;
 display: flex; align-items: center; justify-content: center;'><b>
 Word Cloud</b></p>""", unsafe_allow_html=True)

 st.pyplot(st.session_state["wordcloud_fig"], use_container_width=True)

 treemap_col, sentiment_donut_col = st.columns([0.55, 0.45])

 with treemap_col:
 st.markdown(f"""<p style='font-size: 1.3rem;
 display: flex; align-items: center; justify-content: center;'><b>
 Topic Proportions</b></p>""", unsafe_allow_html=True)

 st.plotly_chart(st.session_state["topic_fig"], use_container_width=True)

 with sentiment_donut_col:
 st.markdown(f"""<p style='font-size: 1.3rem;
 display: flex; align-items: center; justify-content: center;'><b>
 Sentiment Distribution</b></p>""", unsafe_allow_html=True)
 st.plotly_chart(st.session_state["sentiment_fig"], use_container_width=True)

 # st.table(yt_parser.df_comments.head())
 else:
 st.write("Unfortunately we couldn't find any comments for this set of filters, please try "
 "editing the filters and try again")

with st.sidebar:
 # Define the HTML and CSS for the button-style container
 if st.session_state['num_comments'] is not None:
 num_comments = st.session_state['num_comments']
 else:
 num_comments = 0
 htmlstr = f"""
 <p style='background-color: rgb(255, 255, 255, 0.75); 
 color: rgb(0, 0, 0, 0.75); 
 font-size: 40px; 
 border-radius: 7px; 
 padding-top: 25px; 
 padding-bottom: 25px;
 padding-right: 25px; 
 padding-left: 25px; 
 line-height:25px;
 display: flex;
 align-items: center;
 justify-content: center;
 box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);'>
 &nbsp;{num_comments}</p>
 """
 # Display the button-style container with number of comments
 st.subheader("Number of comments")
 st.markdown(htmlstr, unsafe_allow_html=True)

 # Filters section
 st.subheader("Filters")

 if yt_parser.df_comments is not None:
 st.session_state["topic_filter"] = st.multiselect("Topic",
 options=sorted(list(yt_parser.df_comments['Topic'].unique())))
 st.session_state["sentiment_filter"] = st.multiselect("Sentiment",
 options=list(yt_parser.df_comments['Sentiment'].unique()))
 st.session_state["semantic_filter"] = st.text_input("Keyword search",
 max_chars=30)
 st.button('Filter visualisations :sleuth_or_spy:', on_click=filter_visuals_button)

 else:
 st.multiselect("Topic",
 options=["Please query comments from a video"],
 disabled=True)
 st.multiselect("Sentiment",
 options=["Please query comments from a video"],
 disabled=True)
 st.text_input("Keyword search",
 disabled=True)
 st.button('Please query comments before filtering',
 disabled=True)