f65b35f881
- Job history tracking with SQLite table - Dry-run mode (--dry-run) to test scrape rules - Plugin system with 3 built-in plugins (sentiment, dedupe, keywords) - REST API server (--api) for Metabase/Grafana integration - Parquet export (--export-parquet) for DuckDB/warehouses - SQLite maintenance (--backup, --vacuum) - Dashboard Integrations tab with external tools guides - Updated Dockerfile and docker-compose.yml for cloud deployment - Comprehensive README documentation
600 lines
21 KiB
Python
600 lines
21 KiB
Python
"""
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Reddit Scraper Dashboard - Streamlit Web UI
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Run with: streamlit run dashboard/app.py
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"""
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import streamlit as st
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import pandas as pd
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from pathlib import Path
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import sys
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from datetime import datetime
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# Add parent to path
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sys.path.insert(0, str(Path(__file__).parent.parent))
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from analytics.sentiment import (
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analyze_posts_sentiment, extract_keywords,
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calculate_engagement_metrics, find_best_posting_times
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)
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from search.query import search_all_data, advanced_search, get_top_posts
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# Page config
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st.set_page_config(
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page_title="Reddit Scraper Dashboard",
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page_icon="🤖",
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layout="wide",
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initial_sidebar_state="expanded"
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)
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# Custom CSS
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st.markdown("""
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<style>
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.main-header {
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font-size: 2.5rem;
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font-weight: 700;
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background: linear-gradient(90deg, #FF4500, #FF6B6B);
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-webkit-background-clip: text;
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-webkit-text-fill-color: transparent;
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margin-bottom: 1rem;
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}
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.metric-card {
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background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
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padding: 1rem;
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border-radius: 10px;
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color: white;
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}
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.stTabs [data-baseweb="tab-list"] {
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gap: 24px;
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}
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.stTabs [data-baseweb="tab"] {
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height: 50px;
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padding: 10px 20px;
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background-color: #262730;
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border-radius: 5px;
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}
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</style>
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""", unsafe_allow_html=True)
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def load_subreddit_data(subreddit_path):
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"""Load all data for a subreddit."""
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data = {}
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posts_file = subreddit_path / 'posts.csv'
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if posts_file.exists():
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data['posts'] = pd.read_csv(posts_file)
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comments_file = subreddit_path / 'comments.csv'
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if comments_file.exists():
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data['comments'] = pd.read_csv(comments_file)
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return data
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def get_available_subreddits():
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"""Get list of scraped subreddits."""
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data_dir = Path(__file__).parent.parent / 'data'
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subs = []
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if data_dir.exists():
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for sub_dir in data_dir.iterdir():
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if sub_dir.is_dir() and (sub_dir / 'posts.csv').exists():
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subs.append(sub_dir.name)
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return sorted(subs)
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def main():
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# Header
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st.markdown('<h1 class="main-header">🤖 Reddit Scraper Dashboard</h1>', unsafe_allow_html=True)
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# Sidebar
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st.sidebar.title("📊 Navigation")
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# Get available subreddits
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subreddits = get_available_subreddits()
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if not subreddits:
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st.warning("No scraped data found! Run the scraper first:")
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st.code("python main.py <subreddit> --mode full --limit 100")
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return
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# Subreddit selector
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selected_sub = st.sidebar.selectbox(
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"Select Subreddit",
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subreddits,
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format_func=lambda x: f"📁 {x}"
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)
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# Load 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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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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col1, col2, col3, col4, col5 = st.columns(5)
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with col1:
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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, use_container_width=True)
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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), use_container_width=True)
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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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col1, col2, col3 = st.columns(3)
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with col1:
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st.metric("Total Comments", len(comments_df))
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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("Avg Score", f"{avg_score:.1f}")
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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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st.metric("Unique Commenters", unique_authors)
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st.divider()
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# Top comments
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st.subheader("🔥 Top Comments by Score")
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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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for _, row in top_comments.iterrows():
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with st.expander(f"⬆️ {row['score']} - by u/{row['author']}"):
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st.write(row['body'][:500])
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st.divider()
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# Top commenters
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st.subheader("👥 Top Commenters")
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if 'author' in comments_df:
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top_authors = comments_df['author'].value_counts().head(10)
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st.bar_chart(top_authors)
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with tab5:
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st.header("⚙️ Scraper Controls")
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st.subheader("🚀 Start New Scrape")
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col1, col2 = st.columns(2)
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with col1:
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new_sub = st.text_input("Subreddit/User name", placeholder="e.g. python")
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is_user = st.checkbox("Is a User (not subreddit)")
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with col2:
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limit = st.number_input("Post Limit", min_value=10, max_value=5000, value=100)
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mode = st.selectbox("Mode", ['full', 'history'])
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no_media = st.checkbox("Skip media download")
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no_comments = st.checkbox("Skip comments")
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if st.button("🚀 Start Scraping"):
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st.info(f"Run this command in terminal:")
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cmd = f"python main.py {new_sub} --mode {mode} --limit {limit}"
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if is_user:
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cmd += " --user"
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if no_media:
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cmd += " --no-media"
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if no_comments:
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cmd += " --no-comments"
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st.code(cmd)
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st.divider()
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# Export options
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st.subheader("📤 Export Data")
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export_format = st.selectbox("Format", ['CSV', 'JSON', 'Excel'])
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if st.button("📥 Download Posts"):
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if export_format == 'CSV':
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csv = posts_df.to_csv(index=False)
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st.download_button(
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"Download CSV",
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csv,
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f"{selected_sub}_posts.csv",
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"text/csv"
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)
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elif export_format == 'JSON':
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json_data = posts_df.to_json(orient='records', indent=2)
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st.download_button(
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"Download JSON",
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json_data,
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f"{selected_sub}_posts.json",
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"application/json"
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)
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with tab6:
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st.header("📋 Job History")
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try:
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from export.database import get_job_history, get_job_stats
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# Job stats
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stats = get_job_stats()
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col1, col2, col3, col4 = st.columns(4)
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with col1:
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st.metric("Total Jobs", stats.get('total_jobs', 0))
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with col2:
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st.metric("Completed", stats.get('completed', 0))
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with col3:
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st.metric("Failed", stats.get('failed', 0))
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with col4:
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avg_dur = stats.get('avg_duration')
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st.metric("Avg Duration", f"{avg_dur:.1f}s" if avg_dur else "-")
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st.divider()
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# Job history table
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st.subheader("Recent Jobs")
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col1, col2 = st.columns(2)
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with col1:
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filter_status = st.selectbox("Filter by Status", ['All', 'completed', 'failed', 'running'])
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with col2:
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limit = st.number_input("Show last N jobs", min_value=10, max_value=100, value=20)
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status_filter = None if filter_status == 'All' else filter_status
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jobs = get_job_history(limit=limit, status=status_filter)
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if jobs:
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jobs_df = pd.DataFrame(jobs)
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# Format for display
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display_cols = ['job_id', 'target', 'mode', 'status', 'posts_scraped',
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'comments_scraped', 'duration_seconds', 'started_at', 'dry_run']
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display_cols = [c for c in display_cols if c in jobs_df.columns]
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st.dataframe(jobs_df[display_cols], use_container_width=True)
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# Success rate chart
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st.subheader("Success Rate")
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if 'status' in jobs_df.columns:
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status_counts = jobs_df['status'].value_counts()
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st.bar_chart(status_counts)
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else:
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st.info("No job history found. Run some scrapes first!")
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except Exception as e:
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st.error(f"Failed to load job history: {e}")
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st.info("Make sure the database is initialized.")
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with tab7:
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st.header("🔌 Integrations & Settings")
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# REST API Section
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st.subheader("🚀 REST API")
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col1, col2 = st.columns(2)
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with col1:
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st.markdown("""
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**Start the API server:**
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```bash
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python main.py --api
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```
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""")
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with col2:
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api_port = st.number_input("API Port", value=8000, min_value=1000, max_value=65535)
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st.code(f"http://localhost:{api_port}/docs")
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st.markdown("""
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**Available Endpoints:**
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| Endpoint | Description |
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|----------|-------------|
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| `/posts` | List posts with filters |
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| `/comments` | List comments |
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| `/subreddits` | All scraped subreddits |
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| `/jobs` | Job history |
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| `/query?sql=...` | Raw SQL queries |
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| `/docs` | Interactive Swagger UI |
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""")
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st.divider()
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# External Tools
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st.subheader("📊 External Tools Integration")
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tool_tabs = st.tabs(["📈 Metabase", "📊 Grafana", "🔗 DreamFactory", "🧦 DuckDB"])
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with tool_tabs[0]:
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st.markdown("""
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**Metabase Setup:**
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1. Start API: `python main.py --api`
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2. In Metabase: New Question → Native Query
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3. Use HTTP datasource with `http://localhost:8000`
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4. Query: `/posts?subreddit=python&limit=100`
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**Or use raw SQL:**
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```
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/query?sql=SELECT title, score FROM posts ORDER BY score DESC
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```
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""")
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with tool_tabs[1]:
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st.markdown("""
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**Grafana Setup:**
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1. Install "JSON API" or "Infinity" plugin
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2. Add datasource: `http://localhost:8000`
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3. Use `/grafana/query` for time-series
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**Example Panel Query:**
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```sql
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SELECT date(created_utc) as time, COUNT(*) as posts
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FROM posts GROUP BY date(created_utc)
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```
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""")
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with tool_tabs[2]:
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st.markdown("""
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**DreamFactory Setup:**
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1. Point to SQLite file: `data/reddit_scraper.db`
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2. Or use REST API: `http://localhost:8000`
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3. Auto-generates API for all tables
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""")
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with tool_tabs[3]:
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st.markdown("""
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**DuckDB (Analytics):**
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1. Export to Parquet first (see below)
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2. Query directly:
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```python
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import duckdb
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duckdb.query("SELECT * FROM 'data/parquet/*.parquet'").df()
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```
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""")
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st.divider()
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# Parquet Export
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st.subheader("📦 Parquet Export")
|
|
|
|
col1, col2 = st.columns(2)
|
|
with col1:
|
|
export_sub = st.selectbox("Select subreddit to export", subreddits, key="parquet_export")
|
|
with col2:
|
|
if st.button("📦 Export to Parquet"):
|
|
st.info(f"Run: `python main.py --export-parquet {export_sub.replace('r_', '').replace('u_', '')}`")
|
|
|
|
# List existing parquet files
|
|
from pathlib import Path
|
|
parquet_dir = Path("data/parquet")
|
|
if parquet_dir.exists():
|
|
parquet_files = list(parquet_dir.glob("*.parquet"))
|
|
if parquet_files:
|
|
st.write("**Existing Parquet files:**")
|
|
for f in parquet_files[:10]:
|
|
size_mb = f.stat().st_size / (1024 * 1024)
|
|
st.text(f" • {f.name} ({size_mb:.2f} MB)")
|
|
|
|
st.divider()
|
|
|
|
# Database Maintenance
|
|
st.subheader("🛠️ Database Maintenance")
|
|
|
|
col1, col2, col3 = st.columns(3)
|
|
|
|
with col1:
|
|
if st.button("💾 Backup Database"):
|
|
st.info("Run: `python main.py --backup`")
|
|
|
|
with col2:
|
|
if st.button("🧹 Vacuum/Optimize"):
|
|
st.info("Run: `python main.py --vacuum`")
|
|
|
|
with col3:
|
|
try:
|
|
from export.database import get_database_info
|
|
db_info = get_database_info()
|
|
st.metric("DB Size", f"{db_info.get('size_mb', 0):.2f} MB")
|
|
except:
|
|
st.metric("DB Size", "N/A")
|
|
|
|
# Show backup files
|
|
backup_dir = Path("data/backups")
|
|
if backup_dir.exists():
|
|
backups = sorted(backup_dir.glob("*.db"), reverse=True)[:5]
|
|
if backups:
|
|
st.write("**Recent Backups:**")
|
|
for b in backups:
|
|
size_mb = b.stat().st_size / (1024 * 1024)
|
|
st.text(f" • {b.name} ({size_mb:.2f} MB)")
|
|
|
|
st.divider()
|
|
|
|
# Plugin Configuration
|
|
st.subheader("🔌 Plugins")
|
|
|
|
try:
|
|
from plugins import load_plugins
|
|
plugins = load_plugins()
|
|
|
|
if plugins:
|
|
st.write("**Available Plugins:**")
|
|
for plugin in plugins:
|
|
status = "✅" if plugin.enabled else "❌"
|
|
st.markdown(f"{status} **{plugin.name}** - {plugin.description}")
|
|
|
|
st.info("💡 Enable plugins when scraping: `python main.py <target> --plugins`")
|
|
else:
|
|
st.warning("No plugins found in plugins/ directory")
|
|
except Exception as e:
|
|
st.error(f"Plugin loading error: {e}")
|
|
|
|
st.divider()
|
|
|
|
# Quick Commands Reference
|
|
st.subheader("📋 Quick Commands")
|
|
st.code("""
|
|
# Start REST API
|
|
python main.py --api
|
|
|
|
# Export to Parquet
|
|
python main.py --export-parquet <subreddit>
|
|
|
|
# Backup database
|
|
python main.py --backup
|
|
|
|
# Scrape with plugins
|
|
python main.py <target> --plugins
|
|
|
|
# Dry run (test without saving)
|
|
python main.py <target> --dry-run
|
|
""", language="bash")
|
|
|
|
if __name__ == "__main__":
|
|
main()
|