stanlys96 commited on
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f263c8a
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1 Parent(s): 3233c7f

Upload youtube.py

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Files changed (1) hide show
  1. youtube.py +13 -4
youtube.py CHANGED
@@ -1,7 +1,12 @@
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- from helper import extract_youtube_id, get_all_comments
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  import streamlit as st
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  import random
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  import pandas as pd
 
 
 
 
 
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  st.header("💬 Youtube Comments Sentiment Analysis")
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@@ -30,8 +35,12 @@ if st.button('Submit', type="secondary"):
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  the_youtube_id = extract_youtube_id(user_input)
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  if the_youtube_id:
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  with st.spinner("Please wait while we're loading the data..."):
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- the_data = get_all_comments(the_youtube_id)
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- print(the_data)
 
 
 
 
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  st.balloons()
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  st.markdown(f"""<p style="color: green; padding: 0; margin: 0;">Total comments: {len(the_data)}</p>""", unsafe_allow_html=True)
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  for data in the_data:
@@ -42,7 +51,7 @@ if st.button('Submit', type="secondary"):
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  <p style="font-size: 18px; line-height: 1.6; color: #333; font-family: 'Arial', sans-serif;">
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  {data["comment"]}
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  </p>
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- <p style="font-size: 16px; margin-top: 15px; font-family: 'Arial', sans-serif; font-weight: bold;">
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  Sentiment Analysis: <span style="color: {sentiment_color}; font-size: 18px; font-weight: bold; padding: 5px 10px; background-color: {sentiment_color + "33"}; border-radius: 5px;">
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  {sentiment}
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  </span>
 
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+ from helper import extract_youtube_id, get_all_comments, text_preprocessing, slang_dict
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  import streamlit as st
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  import random
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  import pandas as pd
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+ import tensorflow as tf
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+ import tensorflow_hub as tf_hub
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+
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+ def load_model():
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+ return tf.keras.models.load_model('model_lstm.h5', custom_objects={'KerasLayer': tf_hub.KerasLayer})
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  st.header("💬 Youtube Comments Sentiment Analysis")
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  the_youtube_id = extract_youtube_id(user_input)
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  if the_youtube_id:
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  with st.spinner("Please wait while we're loading the data..."):
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+ the_data, comment_only = get_all_comments(the_youtube_id)
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+ the_comments = pd.DataFrame({"comment": comment_only})
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+ the_comments['text_processed'] = the_comments['comment'].apply(lambda x: text_preprocessing(x))
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+ the_comments['text_processed'] = the_comments['text_processed'].apply(lambda x: x if len(x.split()) > 0 else None)
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+ the_comments = the_comments.dropna(subset=['text_processed'])
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+ print(the_comments['text_processed'])
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  st.balloons()
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  st.markdown(f"""<p style="color: green; padding: 0; margin: 0;">Total comments: {len(the_data)}</p>""", unsafe_allow_html=True)
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  for data in the_data:
 
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  <p style="font-size: 18px; line-height: 1.6; color: #333; font-family: 'Arial', sans-serif;">
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  {data["comment"]}
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  </p>
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+ <p style="font-size: 16px; color: gray; margin-top: 15px; font-family: 'Arial', sans-serif; font-weight: bold;">
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  Sentiment Analysis: <span style="color: {sentiment_color}; font-size: 18px; font-weight: bold; padding: 5px 10px; background-color: {sentiment_color + "33"}; border-radius: 5px;">
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  {sentiment}
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  </span>