fix(dashboard): enabled scraper access without existing data
This commit is contained in:
Sanjeev Kumar
parent
8ce832010f
commit
604a50ce17
1 changed files with 223 additions and 202 deletions
425
dashboard/app.py
425
dashboard/app.py
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@ -126,226 +126,247 @@ def main():
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empty_msg = "No scraped subreddits found."
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empty_msg = "No scraped subreddits found."
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icon = "📁"
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icon = "📁"
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selected_sub = None
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if not options:
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if not options:
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st.sidebar.warning(empty_msg)
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st.sidebar.warning(empty_msg)
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if source_type == "Subreddits":
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if source_type == "Subreddits":
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st.sidebar.code("python main.py <sub_name> ...")
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st.sidebar.info("Go to '⚙️ Scraper' tab to start scraping.")
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else:
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else:
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st.sidebar.code("python main.py <user> --user ...")
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st.sidebar.info("Go to '⚙️ Scraper' tab to start scraping users.")
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return # Stop execution if no data for selected type
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else:
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# Selector
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selected_sub = st.sidebar.selectbox(
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f"Select {source_type[:-1]}", # "Select Subreddit" or "Select User"
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options,
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format_func=lambda x: f"{icon} {x[2:] if x.startswith(('r_', 'u_')) else x}"
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)
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# Selector
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# Load data if selected
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selected_sub = st.sidebar.selectbox(
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posts_df = pd.DataFrame()
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f"Select {source_type[:-1]}", # "Select Subreddit" or "Select User"
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comments_df = pd.DataFrame()
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options,
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data_loaded = False
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format_func=lambda x: f"{icon} {x[2:] if x.startswith(('r_', 'u_')) else x}"
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)
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# Load data
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if selected_sub:
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data_dir = Path(__file__).parent.parent / 'data'
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data_dir = Path(__file__).parent.parent / 'data'
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sub_path = data_dir / selected_sub
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sub_path = data_dir / selected_sub
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data = load_subreddit_data(sub_path)
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data = load_subreddit_data(sub_path)
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if 'posts' not in data:
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st.error("No posts data found!")
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return
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posts_df = data['posts']
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comments_df = data.get('comments', pd.DataFrame())
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# Main content tabs
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tab1, tab2, tab3, tab4, tab5, tab6, tab7 = st.tabs([
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"📊 Overview", "📈 Analytics", "🔍 Search", "💬 Comments", "⚙️ Scraper", "📋 Job History", "🔌 Integrations"
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])
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with tab1:
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st.header(f"📊 Overview: {selected_sub}")
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# Metrics row
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if 'posts' in data:
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col1, col2, col3, col4, col5 = st.columns(5)
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posts_df = data['posts']
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comments_df = data.get('comments', pd.DataFrame())
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with col1:
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data_loaded = True
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st.metric("Total Posts", len(posts_df))
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with col2:
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st.metric("Total Comments", len(comments_df))
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with col3:
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total_score = posts_df['score'].sum() if 'score' in posts_df else 0
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st.metric("Total Score", f"{total_score:,}")
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with col4:
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avg_score = posts_df['score'].mean() if 'score' in posts_df else 0
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st.metric("Avg Score", f"{avg_score:.1f}")
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with col5:
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media_count = posts_df['has_media'].sum() if 'has_media' in posts_df else 0
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st.metric("Media Posts", int(media_count))
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st.divider()
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# Post type distribution
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col1, col2 = st.columns(2)
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with col1:
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st.subheader("📝 Post Types")
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if 'post_type' in posts_df:
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type_counts = posts_df['post_type'].value_counts()
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st.bar_chart(type_counts)
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with col2:
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st.subheader("📅 Posts Over Time")
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if 'created_utc' in posts_df:
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posts_df['date'] = pd.to_datetime(posts_df['created_utc']).dt.date
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daily = posts_df.groupby('date').size()
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st.line_chart(daily)
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st.divider()
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# Top posts
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st.subheader("🔥 Top Posts by Score")
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if 'score' in posts_df:
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top_posts = posts_df.nlargest(10, 'score')[['title', 'score', 'num_comments', 'post_type', 'created_utc']]
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st.dataframe(top_posts)
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with tab2:
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st.header("📈 Analytics")
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# Sentiment Analysis
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st.subheader("😀 Sentiment Analysis")
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if st.button("Run Sentiment Analysis"):
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with st.spinner("Analyzing sentiment..."):
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posts_list = posts_df.to_dict('records')
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analyzed_posts, sentiment_counts = analyze_posts_sentiment(posts_list)
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col1, col2, col3 = st.columns(3)
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col1.metric("Positive", sentiment_counts['positive'], delta=None)
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col2.metric("Neutral", sentiment_counts['neutral'], delta=None)
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col3.metric("Negative", sentiment_counts['negative'], delta=None)
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# Pie chart
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sentiment_df = pd.DataFrame({
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'Sentiment': ['Positive', 'Neutral', 'Negative'],
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'Count': [sentiment_counts['positive'], sentiment_counts['neutral'], sentiment_counts['negative']]
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})
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st.bar_chart(sentiment_df.set_index('Sentiment'))
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st.divider()
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# Keywords
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st.subheader("☁️ Top Keywords")
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texts = posts_df['title'].tolist()
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if 'selftext' in posts_df:
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texts.extend(posts_df['selftext'].dropna().tolist())
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keywords = extract_keywords(texts, top_n=30)
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if keywords:
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kw_df = pd.DataFrame(keywords, columns=['Word', 'Count'])
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st.bar_chart(kw_df.set_index('Word').head(20))
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st.divider()
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# Best posting times
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st.subheader("⏰ Best Posting Times")
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if 'created_utc' in posts_df:
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timing_data = find_best_posting_times(posts_df.to_dict('records'))
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if timing_data['best_hours']:
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st.write("**Best Hours to Post:**")
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for hour, avg_score in timing_data['best_hours']:
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st.write(f"• {hour}:00 - Avg Score: {avg_score:.1f}")
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if timing_data['best_days']:
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st.write("**Best Days to Post:**")
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for day, avg_score in timing_data['best_days']:
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st.write(f"• {day} - Avg Score: {avg_score:.1f}")
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with tab3:
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st.header("🔍 Search Posts")
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# Search form
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col1, col2 = st.columns([3, 1])
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with col1:
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search_query = st.text_input("Search query", placeholder="Enter keywords...")
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with col2:
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min_score = st.number_input("Min Score", min_value=0, value=0)
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col3, col4, col5 = st.columns(3)
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with col3:
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if 'post_type' in posts_df:
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post_types = ['All'] + posts_df['post_type'].dropna().unique().tolist()
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selected_type = st.selectbox("Post Type", post_types)
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with col4:
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if 'author' in posts_df:
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authors = ['All'] + posts_df['author'].dropna().unique().tolist()[:50]
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selected_author = st.selectbox("Author", authors)
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with col5:
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sort_by = st.selectbox("Sort by", ['score', 'num_comments', 'created_utc'])
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# Search button
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if st.button("🔍 Search"):
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filtered = posts_df.copy()
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if search_query:
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mask = filtered['title'].str.contains(search_query, case=False, na=False)
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if 'selftext' in filtered:
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mask |= filtered['selftext'].str.contains(search_query, case=False, na=False)
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filtered = filtered[mask]
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if min_score > 0:
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filtered = filtered[filtered['score'] >= min_score]
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if selected_type != 'All' and 'post_type' in filtered:
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filtered = filtered[filtered['post_type'] == selected_type]
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if selected_author != 'All' and 'author' in filtered:
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filtered = filtered[filtered['author'] == selected_author]
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filtered = filtered.sort_values(sort_by, ascending=False)
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st.write(f"Found {len(filtered)} results")
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st.dataframe(filtered[['title', 'score', 'num_comments', 'post_type', 'author', 'created_utc']].head(50))
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with tab4:
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st.header("💬 Comments Analysis")
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if len(comments_df) == 0:
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st.warning("No comments data found for this subreddit")
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else:
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else:
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col1, col2, col3 = st.columns(3)
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st.error("No posts data found for selected item!")
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# Define Tabs
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# Data tabs only if data loaded
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tab_list = []
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if data_loaded:
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tab_list.extend(["📊 Overview", "📈 Analytics", "🔍 Search", "💬 Comments"])
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# Always present tabs
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tab_list.extend(["⚙️ Scraper", "📋 Job History", "🔌 Integrations"])
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# Create tabs
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tabs = st.tabs(tab_list)
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# Map tabs to variables for easy access
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tab_map = {name: tabs[i] for i, name in enumerate(tab_list)}
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# --- RENDER TABS ---
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if data_loaded:
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with tab_map["📊 Overview"]:
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st.header(f"📊 Overview: {selected_sub}")
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# Metrics row
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col1, col2, col3, col4, col5 = st.columns(5)
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with col1:
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with col1:
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st.metric("Total Comments", len(comments_df))
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st.metric("Total Posts", len(posts_df))
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with col2:
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with col2:
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avg_score = comments_df['score'].mean() if 'score' in comments_df else 0
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st.metric("Total Comments", len(comments_df))
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st.metric("Avg Score", f"{avg_score:.1f}")
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with col3:
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with col3:
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unique_authors = comments_df['author'].nunique() if 'author' in comments_df else 0
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total_score = posts_df['score'].sum() if 'score' in posts_df else 0
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st.metric("Unique Commenters", unique_authors)
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st.metric("Total Score", f"{total_score:,}")
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with col4:
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avg_score = posts_df['score'].mean() if 'score' in posts_df else 0
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st.metric("Avg Score", f"{avg_score:.1f}")
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with col5:
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media_count = posts_df['has_media'].sum() if 'has_media' in posts_df else 0
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st.metric("Media Posts", int(media_count))
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st.divider()
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st.divider()
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# Top comments
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# Post type distribution
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st.subheader("🔥 Top Comments by Score")
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col1, col2 = st.columns(2)
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if 'score' in comments_df:
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top_comments = comments_df.nlargest(10, 'score')[['body', 'score', 'author', 'created_utc']]
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with col1:
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for _, row in top_comments.iterrows():
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st.subheader("📝 Post Types")
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with st.expander(f"⬆️ {row['score']} - by u/{row['author']}"):
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if 'post_type' in posts_df:
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st.write(row['body'][:500])
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type_counts = posts_df['post_type'].value_counts()
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st.bar_chart(type_counts)
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with col2:
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st.subheader("📅 Posts Over Time")
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if 'created_utc' in posts_df:
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posts_df['date'] = pd.to_datetime(posts_df['created_utc']).dt.date
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daily = posts_df.groupby('date').size()
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st.line_chart(daily)
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st.divider()
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st.divider()
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# Top commenters
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# Top posts
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st.subheader("👥 Top Commenters")
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st.subheader("🔥 Top Posts by Score")
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if 'author' in comments_df:
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if 'score' in posts_df:
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top_authors = comments_df['author'].value_counts().head(10)
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top_posts = posts_df.nlargest(10, 'score')[['title', 'score', 'num_comments', 'post_type', 'created_utc']]
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st.bar_chart(top_authors)
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st.dataframe(top_posts)
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with tab5:
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with tab_map["📈 Analytics"]:
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st.header("📈 Analytics")
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# Sentiment Analysis
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st.subheader("😀 Sentiment Analysis")
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if st.button("Run Sentiment Analysis"):
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with st.spinner("Analyzing sentiment..."):
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posts_list = posts_df.to_dict('records')
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analyzed_posts, sentiment_counts = analyze_posts_sentiment(posts_list)
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col1, col2, col3 = st.columns(3)
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col1.metric("Positive", sentiment_counts['positive'], delta=None)
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col2.metric("Neutral", sentiment_counts['neutral'], delta=None)
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col3.metric("Negative", sentiment_counts['negative'], delta=None)
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# Pie chart
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sentiment_df = pd.DataFrame({
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'Sentiment': ['Positive', 'Neutral', 'Negative'],
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'Count': [sentiment_counts['positive'], sentiment_counts['neutral'], sentiment_counts['negative']]
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})
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st.bar_chart(sentiment_df.set_index('Sentiment'))
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st.divider()
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# Keywords
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st.subheader("☁️ Top Keywords")
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texts = posts_df['title'].tolist()
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if 'selftext' in posts_df:
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texts.extend(posts_df['selftext'].dropna().tolist())
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keywords = extract_keywords(texts, top_n=30)
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if keywords:
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kw_df = pd.DataFrame(keywords, columns=['Word', 'Count'])
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st.bar_chart(kw_df.set_index('Word').head(20))
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st.divider()
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# Best posting times
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st.subheader("⏰ Best Posting Times")
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if 'created_utc' in posts_df:
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timing_data = find_best_posting_times(posts_df.to_dict('records'))
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if timing_data['best_hours']:
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st.write("**Best Hours to Post:**")
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for hour, avg_score in timing_data['best_hours']:
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st.write(f"• {hour}:00 - Avg Score: {avg_score:.1f}")
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if timing_data['best_days']:
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st.write("**Best Days to Post:**")
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for day, avg_score in timing_data['best_days']:
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st.write(f"• {day} - Avg Score: {avg_score:.1f}")
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with tab_map["🔍 Search"]:
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st.header("🔍 Search Posts")
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# Search form
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col1, col2 = st.columns([3, 1])
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with col1:
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search_query = st.text_input("Search query", placeholder="Enter keywords...")
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with col2:
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min_score = st.number_input("Min Score", min_value=0, value=0)
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col3, col4, col5 = st.columns(3)
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with col3:
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if 'post_type' in posts_df:
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post_types = ['All'] + posts_df['post_type'].dropna().unique().tolist()
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selected_type = st.selectbox("Post Type", post_types)
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with col4:
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||||||
|
if 'author' in posts_df:
|
||||||
|
authors = ['All'] + posts_df['author'].dropna().unique().tolist()[:50]
|
||||||
|
selected_author = st.selectbox("Author", authors)
|
||||||
|
|
||||||
|
with col5:
|
||||||
|
sort_by = st.selectbox("Sort by", ['score', 'num_comments', 'created_utc'])
|
||||||
|
|
||||||
|
# Search button
|
||||||
|
if st.button("🔍 Search"):
|
||||||
|
filtered = posts_df.copy()
|
||||||
|
|
||||||
|
if search_query:
|
||||||
|
mask = filtered['title'].str.contains(search_query, case=False, na=False)
|
||||||
|
if 'selftext' in filtered:
|
||||||
|
mask |= filtered['selftext'].str.contains(search_query, case=False, na=False)
|
||||||
|
filtered = filtered[mask]
|
||||||
|
|
||||||
|
if min_score > 0:
|
||||||
|
filtered = filtered[filtered['score'] >= min_score]
|
||||||
|
|
||||||
|
if selected_type != 'All' and 'post_type' in filtered:
|
||||||
|
filtered = filtered[filtered['post_type'] == selected_type]
|
||||||
|
|
||||||
|
if selected_author != 'All' and 'author' in filtered:
|
||||||
|
filtered = filtered[filtered['author'] == selected_author]
|
||||||
|
|
||||||
|
filtered = filtered.sort_values(sort_by, ascending=False)
|
||||||
|
|
||||||
|
st.write(f"Found {len(filtered)} results")
|
||||||
|
st.dataframe(filtered[['title', 'score', 'num_comments', 'post_type', 'author', 'created_utc']].head(50))
|
||||||
|
|
||||||
|
with tab_map["💬 Comments"]:
|
||||||
|
st.header("💬 Comments Analysis")
|
||||||
|
|
||||||
|
if len(comments_df) == 0:
|
||||||
|
st.warning("No comments data found for this subreddit")
|
||||||
|
else:
|
||||||
|
col1, col2, col3 = st.columns(3)
|
||||||
|
|
||||||
|
with col1:
|
||||||
|
st.metric("Total Comments", len(comments_df))
|
||||||
|
with col2:
|
||||||
|
avg_score = comments_df['score'].mean() if 'score' in comments_df else 0
|
||||||
|
st.metric("Avg Score", f"{avg_score:.1f}")
|
||||||
|
with col3:
|
||||||
|
unique_authors = comments_df['author'].nunique() if 'author' in comments_df else 0
|
||||||
|
st.metric("Unique Commenters", unique_authors)
|
||||||
|
|
||||||
|
st.divider()
|
||||||
|
|
||||||
|
# Top comments
|
||||||
|
st.subheader("🔥 Top Comments by Score")
|
||||||
|
if 'score' in comments_df:
|
||||||
|
top_comments = comments_df.nlargest(10, 'score')[['body', 'score', 'author', 'created_utc']]
|
||||||
|
for _, row in top_comments.iterrows():
|
||||||
|
with st.expander(f"⬆️ {row['score']} - by u/{row['author']}"):
|
||||||
|
st.write(row['body'][:500])
|
||||||
|
|
||||||
|
st.divider()
|
||||||
|
|
||||||
|
# Top commenters
|
||||||
|
st.subheader("👥 Top Commenters")
|
||||||
|
if 'author' in comments_df:
|
||||||
|
top_authors = comments_df['author'].value_counts().head(10)
|
||||||
|
st.bar_chart(top_authors)
|
||||||
|
|
||||||
|
# Scraper Tab (Always visible)
|
||||||
|
with tab_map["⚙️ Scraper"]:
|
||||||
|
|
||||||
st.header("⚙️ Scraper Controls")
|
st.header("⚙️ Scraper Controls")
|
||||||
|
|
||||||
# Persistence logic
|
# Persistence logic
|
||||||
|
|
|
||||||
Loading…
Reference in a new issue