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pages/🎥_Video_Data.py

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+from datetime import datetime
+
+import numpy
+import streamlit as st
+import plotly.express as px
+import plotly.graph_objects as go
+from streamlit_extras.chart_container import chart_container
+from textblob import TextBlob
+
+from streamlit_extras.metric_cards import style_metric_cards
+from streamlit_extras.switch_page_button import switch_page
+
+from analyze_comments import analyze_comments
+from channelVideoDataExtraction import *
+
+
+########################################################################################################################
+#                                       FUNCTIONS
+########################################################################################################################
+def get_comments():
+    comment_data = getVideoComments(api_key, video_id)
+    return comment_data
+
+
+def tag_list(tags):
+    tag_list_html = ""
+    for tag in tags:
+        tag_list_html += f'<span class="tag">{tag}</span>'
+    return tag_list_html
+
+
+def render_insight_card(title, names, emoji="💡"):
+    card_content = f"""
+    ### {emoji} {title}
+    {'<br>'.join([f"**{name}**" for name in names])}
+    """
+    return card_content
+
+
+########################################################################################################################
+#                                       PAGE CONFIGURATION
+########################################################################################################################
+st.set_page_config(page_title="Video Statistics",
+                   page_icon="📊",
+                   layout="wide")
+
+########################################################################################################################
+#                                       VIDEO STATISTICAL DATA CONFIGURATION
+########################################################################################################################
+if st.session_state['video_id'] is None:
+    st.error("No Video Has been selected to view statistics. Please select a video from the home page.")
+    if st.button("Go Home"):
+        switch_page("Home")
+else:
+    api_key = st.session_state.api_key
+    all_video_data = st.session_state.all_video_df
+    video_id = st.session_state['video_id']
+
+    video_row = all_video_data[all_video_data['id'] == video_id]
+
+    title = video_row['title'].values[0]
+    image_url = video_row['thumbnail'].values[0]
+    view_count = video_row['view_count'].values[0]
+    like_count = video_row['like_count'].values[0]
+    favourite_count = video_row['favorite_count'].values[0]
+    comment_count = video_row['comment_count'].values[0]
+    duration = round(video_row['duration_minutes'].values[0], 2)
+    publish_date = video_row['published_date'].values[0]
+    tags = video_row['tags'].values[0]
+
+    # Format view count and subscriber count with commas
+    view_count_formatted = "{:,}".format(view_count)
+    like_count_formatted = "{:,}".format(like_count)
+    comment_count_formatted = "{:,}".format(comment_count)
+
+    st.subheader(title, divider="green")
+
+    col1, col2, col3 = st.columns(3)
+
+    with col1:
+        st.image(image_url)
+        st.markdown(f"**Published on:**  {publish_date}")
+
+    with col2:
+        col2.metric("Total Views", view_count_formatted, "")
+        col2.metric("Total Likes", like_count_formatted, "")
+        col2.metric("Total Comments", comment_count_formatted, "")
+        style_metric_cards(background_color="#000000",
+                           border_left_color="#049204",
+                           border_color="#0E0E0E"
+                           )
+
+    with col3:
+        # Define the CSS style for the tags
+        css = """
+        <style>
+            .tag {
+                background-color: #4CAF50; /* Change the background color to green */
+                color: white;
+                padding: 4px 8px;
+                margin-right: 8px;
+                border-radius: 4px;
+                font-weight: bold;
+                display: inline-block; /* Prevent overlapping */
+                margin-bottom: 8px; /* Add some vertical spacing */
+            }
+        </style>
+        """
+
+        st.subheader("Video Tags")
+        # Display the tags
+        st.markdown(css, unsafe_allow_html=True)
+        st.markdown(tag_list(tags), unsafe_allow_html=True)
+
+        st.subheader("Duration")
+        st.markdown(f''':green[{duration}] Minutes''')
+
+########################################################################################################################
+#                                       COMMENT DATA CONFIGURATIONS
+########################################################################################################################
+
+    st.subheader("Top 10 Comments", divider="green")
+
+    with st.spinner("Getting Comment Data...."):
+        comment_data = get_comments()
+        top_10_comments_df = comment_data.head(10)
+        st.table(top_10_comments_df)
+
+    st.subheader("All Commenters List", divider="green")
+    unique_commenters = comment_data['author'].unique()
+    st.markdown(f'''Total Number of Commenters: :green[{len(unique_commenters)}]''')
+    with st.expander("Click to see all commenters"):
+        commenters_text = "\n".join(unique_commenters)
+        st.text_area("List of Commenters", commenters_text, height=200)
+
+########################################################################################################################
+#                                       COMMENT TRENDS AND SENTIMENT ANALYSIS
+########################################################################################################################
+
+    st.subheader("Comment Trends Over Time & Sentiment Analysis", divider="green")
+    col1, col2 = st.columns(2)
+
+    with col1:
+        comment_data['comment_date'] = pd.to_datetime(comment_data['comment_date'])
+        comment_data_grouped = comment_data.groupby(comment_data['comment_date'].dt.date).agg(
+            {"comment_id": "count", "like_count": "sum"}).reset_index()
+
+        fig = go.Figure()
+
+        # Add traces for comments and likes
+        fig.add_trace(go.Scatter(x=comment_data_grouped['comment_date'],
+                                 y=comment_data_grouped['comment_id'],
+                                 mode='lines+markers',
+                                 name='Number of Comments',
+                                 line=dict(color='blue')))
+        fig.add_trace(go.Scatter(x=comment_data_grouped['comment_date'],
+                                 y=comment_data_grouped['like_count'],
+                                 mode='lines+markers',
+                                 name='Like Count',
+                                 line=dict(color='orange')))
+
+        # Update layout for better appearance
+        fig.update_layout(title='Comment and Like Trends Over Time',
+                          xaxis_title='Date',
+                          yaxis_title='Count',
+                          template="plotly_dark")
+
+        st.plotly_chart(fig, use_container_width=True)
+
+    with col2:
+        def get_sentiment(text):
+            analysis = TextBlob(text)
+            # Classify the polarity of the text
+            if analysis.sentiment.polarity > 0:
+                return 'Positive'
+            elif analysis.sentiment.polarity == 0:
+                return 'Neutral'
+            else:
+                return 'Negative'
+
+
+        comment_data['Sentiment'] = comment_data['comment_text'].apply(get_sentiment)
+        sentiment_counts = comment_data['Sentiment'].value_counts()
+
+        with chart_container(comment_data):
+            fig = go.Figure(go.Pie(
+                labels=sentiment_counts.index,
+                values=sentiment_counts.values,
+                hole=0.3
+            ))
+
+            fig.update_layout(title_text="Sentiment Analysis of Comments")
+            st.plotly_chart(fig, use_container_width=True)
+
+########################################################################################################################
+#                                       COMMENT NETWORK ANALYSIS
+########################################################################################################################
+    with st.spinner("Applying Network Analysis to Comments"):
+        # Analyze the comments and display the results
+        st.title("Comments Network Analysis & Community Detection")
+
+        centrality_df, fig_subgraph, fig_communities, no_of_communities = analyze_comments(comment_data)
+
+        # Display the centrality measures within an expander
+        with st.expander("Top 10 Comment Author Centrality Measures"):
+            st.table(centrality_df.head(10))
+
+        st.subheader("📊 Network Insights")
+
+        # Arrange cards in columns
+        col1, col2 = st.columns(2)
+
+        with col1:
+            st.markdown(render_insight_card("Top Influencers",
+                                            centrality_df.nlargest(5, 'Degree Centrality')['Author'].tolist(), "🌟"),
+                        unsafe_allow_html=True)
+            st.markdown(render_insight_card("Most Active Responders",
+                                            centrality_df.nlargest(5, 'Out-Degree Centrality')['Author'].tolist(), "💬"),
+                        unsafe_allow_html=True)
+
+        with col2:
+            st.markdown(render_insight_card("Key Information Spreaders",
+                                            centrality_df.nlargest(5, 'Betweenness Centrality')['Author'].tolist(),
+                                            "🌐"), unsafe_allow_html=True)
+            st.markdown(render_insight_card("Most Responded-To Authors",
+                                            centrality_df.nlargest(5, 'In-Degree Centrality')['Author'].tolist(), "🎯"),
+                        unsafe_allow_html=True)
+
+        st.markdown("---")  # Divider
+
+        # Graphical Insights
+        col1, col2 = st.columns(2)
+
+        with col1:
+            # Display the subgraph visualization with a brief title/description
+            st.subheader("🔗 Sub Network Visualization")
+            st.caption("Top 50 Authors based on Degree Centrality")
+            st.pyplot(fig_subgraph)
+
+        with col2:
+            # Display the communities visualization with a brief title/description
+            st.subheader("👥 Community Visualization")
+            st.caption(f"Communities in Sample of 500 Nodes: {no_of_communities} detected")
+            st.pyplot(fig_communities)

pages/📅_Post_Scheduler.py

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+import streamlit as st
+import pandas as pd
+from datetime import datetime, timedelta
+
+
+########################################################################################################################
+#                                       FUNCTIONS
+########################################################################################################################
+# Function to suggest the next publishing date
+def suggest_next_publish_date(video_data):
+    video_data['published_date'] = pd.to_datetime(video_data['published_date'])
+    df_sorted = video_data.sort_values(by='published_date', ascending=False)
+    average_diff = (df_sorted['published_date'] - df_sorted['published_date'].shift(-1)).mean()
+    return df_sorted['published_date'].iloc[0] + average_diff
+
+
+########################################################################################################################
+#                                       SCHEDULED POST DB CONFIG
+########################################################################################################################
+# Excel filepath
+EXCEL_DB = 'scheduled_posts.xlsx'
+
+# Try to read the Excel file, if it doesn't exist, create a new DataFrame
+try:
+    df = pd.read_excel(EXCEL_DB)
+except FileNotFoundError:
+    columns = ["title", "description", "date", "time"]
+    df = pd.DataFrame(columns=columns)
+    df.to_excel(EXCEL_DB, index=False)  # Create the Excel file with the columns
+
+########################################################################################################################
+#                                       PAGE CONFIGURATION
+########################################################################################################################
+st.set_page_config(page_title="Content Publishing Calender",
+                   page_icon="📊",
+                   layout="wide")
+
+# Load video data
+video_data = pd.read_excel('all_video_Data.xlsx')  # Replace with your video data file path
+
+# Get the suggested date
+suggested_date = suggest_next_publish_date(video_data)
+
+########################################################################################################################
+#                                       PAGE CONTENT CONFIGURATION
+########################################################################################################################
+# Display the suggested date with enhanced styling
+st.markdown(f"""
+<style>
+    .suggested-date {{
+        background-color: #4CAF50;
+        color: white;
+        padding: 10px 20px;
+        border-radius: 10px;
+        font-size: 20px;
+        text-align: center;
+    }}
+</style>
+<div class="suggested-date">Suggested next publishing date: {suggested_date.strftime('%Y-%m-%d %H:%M:%S')}</div>
+""", unsafe_allow_html=True)
+
+# Input fields
+video_title = st.text_input("Video Title")
+video_description = st.text_area("Video Description")
+schedule_date = st.date_input("Schedule Date", suggested_date.date())  # Default to the suggested date
+schedule_time = st.time_input("Schedule Time")
+
+if st.button("Schedule Video"):
+    # Append to DataFrame and save back to Excel
+    df = df.append({
+        "title": video_title,
+        "description": video_description,
+        "date": schedule_date,
+        "time": schedule_time
+    }, ignore_index=True)
+    df.to_excel(EXCEL_DB, index=False)
+    st.success("Video scheduled!")
+
+# Display scheduled posts
+st.subheader("Scheduled Videos")
+st.table(df)