Fix flagged page styling by adding CSS/JS blocks to base template
- Add {% block extra_css %} to base.html head section
- Add {% block extra_js %} to base.html before closing body tag
- Add encode_uri template filter for URL encoding
Flagged page CSS and JavaScript now load correctly, fixing:
- Filter bar styling
- Table formatting
- Review button styles and functionality
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
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Pieter
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# Toxicity Analysis System
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This document describes the toxicity analysis system for the Mastodon collector, adapted from the Bluesky collector implementation.
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## Overview
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The toxicity analysis system uses OpenAI's GPT-4o-mini to classify Mastodon posts across 12 toxicity categories:
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- **toxic**: rude, disrespectful, or aggressive language
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- **threat**: threats of violence, harm, or intimidation
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- **hate_speech**: targeting based on protected characteristics
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- **racism**: race/ethnicity-based targeting
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- **antisemitism**: anti-Jewish content
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- **islamophobia**: anti-Muslim content
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- **sexism**: gender-based discrimination
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- **homophobia**: anti-LGBTQ+ content
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- **insult**: personal attacks and name-calling
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- **dehumanization**: comparing people to animals/vermin
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- **extremism**: far-right/left extremist rhetoric
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- **ableism**: targeting people with disabilities
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## Architecture
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The system consists of:
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1. **Analyzer Module** (`app/analyzer/`) - Async batch processor for classification
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2. **Database Schema** (`scripts/02-toxicity.sql`) - Toxicity scores and analysis runs
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3. **Web Interface** - Dashboard and flagged content review
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4. **API Endpoints** - For manual review of flagged content
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## Setup
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### 1. Environment Variables
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Add to your `.env` file:
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```bash
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# OpenAI API key for toxicity analysis
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OPENAI_API_KEY=sk-...
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# Analyzer configuration (optional)
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ANALYZER_MODEL=gpt-4o-mini
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ANALYZER_BATCH_SIZE=10
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ANALYZER_CONCURRENCY=5
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ANALYZER_FLAG_THRESHOLD=0.5
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ANALYZER_LIMIT=0 # 0 = no limit, or set to test on limited number
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```
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### 2. Database Migration
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The toxicity schema is applied automatically when the analyzer runs for the first time. It creates:
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- `toxicity_scores` table - stores scores for each status
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- `analysis_runs` table - audit trail of analysis runs
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To manually apply the migration:
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```bash
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docker exec -i mastodon-collector-db-1 psql -U collector -d mastodon_collector < scripts/02-toxicity.sql
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```
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### 3. Install Dependencies
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Dependencies are already added to `requirements.txt`:
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- `openai==1.58.1` - OpenAI API client
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- `asyncpg==0.30.0` - Async PostgreSQL driver
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Rebuild the Docker containers to install:
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```bash
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docker-compose build
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docker-compose up -d
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```
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## Running the Analyzer
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### One-Time Analysis
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Run the analyzer manually to score all unscored statuses:
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```bash
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docker exec mastodon-collector-collector-1 python -m app.analyzer
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```
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### Test on Limited Sample
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To test on 100 statuses first:
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```bash
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docker exec mastodon-collector-collector-1 bash -c "ANALYZER_LIMIT=100 python -m app.analyzer"
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```
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### Automated Analysis (Future)
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You can schedule the analyzer to run periodically using cron or a scheduler service. For example, add to your `docker-compose.yml`:
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```yaml
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analyzer:
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build: .
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command: python -m app.analyzer
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environment:
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- DATABASE_URL=postgresql://collector:${POSTGRES_PASSWORD}@db:5432/mastodon_collector
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- OPENAI_API_KEY=${OPENAI_API_KEY}
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- ANALYZER_LIMIT=${ANALYZER_LIMIT:-0}
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depends_on:
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- db
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restart: "no" # Run once, don't restart
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```
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Then trigger manually:
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```bash
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docker-compose run --rm analyzer
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```
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## Web Interface
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### Analysis Dashboard
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Visit http://localhost:8585/analysis to see:
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- Overall statistics (total scored, flagged count, averages)
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- Toxicity trends over time
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- Category breakdown chart
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- Recent analysis runs
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### Flagged Content Review
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Visit http://localhost:8585/analysis/flagged to:
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- Browse flagged content (threshold >= 0.5 by default)
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- Filter by category, account, date range, review status
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- Sort by overall toxicity or specific categories
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- Manually review and mark items as:
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- ✓ Correct (correctly flagged)
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- ✗ Incorrect (false positive)
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- ? Unsure
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### Review Workflow
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1. Click on flagged items to review
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2. Use the review buttons (✓, ✗, ?) to mark your assessment
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3. Filter by `review_status=unreviewed` to focus on items needing review
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4. Use reviewed data to improve the classifier or adjust thresholds
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## Cost Estimation
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Based on GPT-4o-mini pricing (as of Jan 2025):
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- Input: $0.150 per 1M tokens
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- Output: $0.600 per 1M tokens
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Typical costs:
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- ~1,000 statuses = $0.05-0.15
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- ~10,000 statuses = $0.50-1.50
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The analyzer logs estimated costs after each run.
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## Architecture Details
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### Batch Processing
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The analyzer processes statuses in batches (default: 10 per API call) with concurrency control (default: 5 simultaneous batches). This optimizes for:
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- Cost efficiency (batch API calls)
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- Rate limit compliance
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- Parallel processing speed
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### Scoring Logic
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Each status receives:
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- 12 category scores (0.0 - 1.0)
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- Overall score = max of all categories
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- Flagged if overall >= threshold (default 0.5)
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### Human Review
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Manual reviews help:
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- Validate AI classifications
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- Identify patterns of false positives
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- Build training data for future improvements
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- Adjust thresholds per category if needed
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## Dutch Language Support
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The classifier is specifically trained to handle Dutch political content, including:
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- Dutch slang and coded terms ("gelukszoekers", "omvolking", "wappie", etc.)
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- Political context and satire
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- Zwarte Piet debates
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- Dutch far-right rhetoric
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## Templates
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The Bluesky collector templates can be adapted for Mastodon. Key files to create:
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1. `app/templates/analysis.html` - Main dashboard
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2. `app/templates/flagged.html` - Flagged content browser
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These templates should include:
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- Chart.js for visualizations
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- Filter forms for exploration
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- Review buttons for manual validation
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## Troubleshooting
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### No statuses being scored
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- Check that statuses exist: `SELECT COUNT(*) FROM statuses WHERE content IS NOT NULL AND reblog_of_id IS NULL;`
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- Check migration applied: `\dt toxicity_scores` in psql
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- Check OPENAI_API_KEY is set
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### Rate limit errors
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- Reduce `ANALYZER_CONCURRENCY` (try 2-3)
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- Reduce `ANALYZER_BATCH_SIZE` (try 5)
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- The analyzer retries with exponential backoff automatically
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### High false positive rate
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- Increase `ANALYZER_FLAG_THRESHOLD` (try 0.6 or 0.7)
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- Review flagged items and look for patterns
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- Dutch political content can be intense but not necessarily toxic
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### Template errors
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- Ensure templates exist in `app/templates/`
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- Check that analysis helper functions are imported correctly
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- Verify template filters are defined (`format_number`, `time_ago`, etc.)
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## Next Steps
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1. Copy analysis templates from Bluesky collector to `app/templates/`
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2. Add navigation links to analysis dashboard in base template
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3. Run initial analysis on sample data
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4. Review flagged content and adjust thresholds
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5. Set up automated analysis runs (cron/scheduler)
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6. Monitor costs and performance
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## References
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- Bluesky collector: https://forgejo.postxsociety.cloud/pieter/bluesky-collector
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- OpenAI API: https://platform.openai.com/docs
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- asyncpg: https://magicstack.github.io/asyncpg/
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@ -234,6 +234,7 @@
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.justify-between { justify-content: space-between; }
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.truncate { white-space: nowrap; overflow: hidden; text-overflow: ellipsis; max-width: 400px; }
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</style>
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{% block extra_css %}{% endblock %}
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</head>
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<body>
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<nav>
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{% block content %}{% endblock %}
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</div>
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</main>
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{% block extra_js %}{% endblock %}
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</body>
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</html>
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@ -75,6 +75,13 @@ def truncate_text(text, length=200):
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return text[:length] + "..."
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@app.template_filter('encode_uri')
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def encode_uri(uri):
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"""URL encode a URI for use in query parameters."""
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from urllib.parse import quote
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return quote(str(uri), safe='')
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# Initialize database on startup
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with app.app_context():
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init_db()
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