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app.py

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+import os
+import re
+import streamlit as st
+import googleapiclient.discovery
+import pandas as pd
+from transformers import pipeline
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+st.title('Анализатор комментариев :red[YouTube] :sunglasses:')
+
+
+# Инициализируем модель Hugging Face для анализа тональности текста
+# Кэшируем ресурс для одной загрузки модели на все сессии
+#@st.cache_resource
+def load_model():
+    """
+    Loads the 'blanchefort/rubert-base-cased-sentiment' model from HuggingFace
+    and saves to cache for consecutive loads.
+    """
+    model = pipeline(
+        "sentiment-analysis",
+        "blanchefort/rubert-base-cased-sentiment")
+    return model
+
+
+def extract_video_id(url: str) -> str:
+    """
+    Extracts the video ID from a YouTube video URL.
+    Args:       url (str): The YouTube video URL.
+    Returns:    str: The extracted video ID,
+                or an empty string if the URL is not valid.
+    """
+    pattern = r"(?<=v=)[\w-]+(?=&|\b)"
+    match = re.search(pattern, url)
+    if match:
+        return match.group()
+    else:
+        return ""
+
+
+def download_comments(video_id: str) -> pd.DataFrame:
+    """
+    Downloads comments from a YouTube video based on the provided video ID
+    and returns them as a DataFrame.
+    Args: video_id (str): The video ID of the YouTube video.
+    Returns: DataFrame: A DataFrame containing the downloaded comments from the video.
+    """
+    DEV_KEY = os.getenv('API_KEY_YOUTUBE')    
+    youtube = googleapiclient.discovery.build("youtube",
+                                              "v3",
+                                              developerKey=DEV_KEY)
+    request = youtube.commentThreads().list(part="snippet",
+                                            videoId=video_id,
+                                            maxResults=100)
+    response = request.execute()
+    comments = []
+    for item in response['items']:
+        comment = item['snippet']['topLevelComment']['snippet']
+        comments.append([comment['authorDisplayName'],
+                        comment['publishedAt'],
+                        comment['updatedAt'],
+                        comment['likeCount'],
+                        comment['textDisplay'],])
+    return pd.DataFrame(comments,
+                        columns=['author',
+                                'published_at',
+                                'updated_at',
+                                'like_count',
+                                'text',])
+
+
+def analyze_emotions_in_comments(df: pd.DataFrame) -> tuple:
+    """
+    Takes a DataFrame with comments,
+    processes the emotional sentiment of each comment in the DataFrame
+    Args: dataframe (pandas.DataFrame): DataFrame containing comments to analyze.
+    Returns: tuple: containing the updated DataFrame with the added 'Emotional Sentiment' column
+    and the total count of processed comments.
+    """
+    model = load_model()
+    selected_columns = ['text', 'author', 'published_at']
+    df = df[selected_columns]
+    res_list = []
+    res_list = model(df['text'][:513].to_list()) 
+    full_df = pd.concat([pd.DataFrame(res_list), df], axis=1)
+    return (full_df, len(res_list))
+
+
+def plot_heatmap_from_dataframe(df: pd.DataFrame) -> plt:
+    """
+    Visualizes the data from the input DataFrame and returns a matplotlib plot object.
+    Args: df (DataFrame): The input DataFrame containing the data to be visualized.
+    Returns: plt: A matplotlib plot object showing the visualization of the data.
+    """
+    df['published_at'] = pd.to_datetime(df['published_at'])
+    df['Date'] = df['published_at'].dt.date
+    df['Hour'] = df['published_at'].dt.hour
+    pivot_table = df.pivot_table(index='Hour',
+                                columns='Date',
+                                values='text',
+                                aggfunc='count')
+    plt.figure(figsize=(10, 6))
+    sns.heatmap(pivot_table,
+                cmap='YlGnBu')
+    plt.title('Количество комментариев по часам и датам')
+    plt.xlabel('Дата')
+    plt.ylabel('Час')
+    return plt
+
+
+def visualize_data(df: pd.DataFrame):
+    """
+    Visualizes the data from the input DataFrame and returns a matplotlib figure object.
+    Args: df (DataFrame): The input DataFrame containing the data to be visualized.
+    Returns: fig: A matplotlib figure object
+    """
+    data = df['label'].value_counts()
+    fig, ax = plt.subplots()
+    plt.title("Эмоциональная окраска комментариев на YouTube")
+    label = data.index
+    ax.pie(data, labels=label, autopct='%1.1f%%')
+    return fig
+
+
+def change_url():
+    st.session_state.start = False
+
+
+if "start" not in st.session_state:
+    st.session_state.start = False
+
+# Получаем id видеоролика из URL для отправки запроса
+url = st.text_input(label="Enter URL from YouTube", on_change=change_url)
+video_id = extract_video_id(url)
+if  video_id != "":
+    if btn_start := st.button('Загрузить комментарии'):
+        st.session_state.start = True
+
+if st.session_state.start:
+    # Выводим таблицу с результатами на странице
+    comments_df = download_comments(video_id)
+    with st.spinner('Analyzing comments...'):
+        full_df,  num_comments = analyze_emotions_in_comments(comments_df)
+        st.success(f'Готово! Обработано {num_comments} комментариев.')
+    st.write(full_df)
+    st.markdown('***')
+
+    # Выводим heatmap комментариев по часам и датам
+    st.pyplot(plot_heatmap_from_dataframe(full_df))
+    st.markdown('***')
+
+    # Выводим круговую диаграмму
+    st.pyplot(visualize_data(full_df))