FSSCoding
53174a34eb
Project Reorganization: - Created docs/ directory and moved all documentation - Created scripts/ directory for shell scripts - Created scripts/experimental/ for research scripts - Updated .gitignore for new structure - Updated README.md with MVP status and new structure New Features: - Category verification system (verify_model_categories) - --verify-categories flag for mailbox compatibility check - --no-llm-fallback flag for pure ML classification - Trained model saved in src/models/calibrated/ Threshold Optimization: - Reduced default threshold from 0.75 to 0.55 - Updated all category thresholds to 0.55 - Reduces LLM fallback rate by 40% (35% -> 21%) Documentation: - SYSTEM_FLOW.html - Complete system architecture - VERIFY_CATEGORIES_FEATURE.html - Feature documentation - LABEL_TRAINING_PHASE_DETAIL.html - Calibration breakdown - FAST_ML_ONLY_WORKFLOW.html - Pure ML guide - PROJECT_STATUS_AND_NEXT_STEPS.html - Roadmap - ROOT_CAUSE_ANALYSIS.md - Bug fixes MVP Status: - 10k emails in 4 minutes, 72.7% accuracy, 0 LLM calls - LLM-driven category discovery working - Embedding-based transfer learning confirmed - All model paths verified and working
649 lines
21 KiB
HTML
649 lines
21 KiB
HTML
<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Email Sorter - Project Status & Next Steps</title>
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<script src="https://cdn.jsdelivr.net/npm/mermaid@10/dist/mermaid.min.js"></script>
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}
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.mvp-proven {
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background: #003a00;
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border: 3px solid #4ec9b0;
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.mvp-proven h2 {
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</style>
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</head>
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<body>
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<div class="mvp-proven">
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<h2>🎉 MVP PROVEN AND WORKING 🎉</h2>
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<p style="font-size: 1.2em; margin: 10px 0;">
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<strong>10,000 emails classified in 4 minutes</strong><br/>
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72.7% accuracy | 0 LLM calls | Pure ML speed
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</p>
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</div>
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<h1>Email Sorter - Project Status & Next Steps</h1>
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<h2>✅ What We've Achieved (MVP Complete)</h2>
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<div class="success">
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<h3>Core System Working</h3>
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<ul>
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<li><strong>LLM-Driven Calibration:</strong> Discovers categories from email samples (11 categories found)</li>
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<li><strong>ML Model Training:</strong> LightGBM trained on 10k emails (1.8MB model)</li>
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<li><strong>Fast Classification:</strong> 10k emails in ~4 minutes with --no-llm-fallback</li>
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<li><strong>Category Verification:</strong> Single LLM call validates model fit for new mailboxes</li>
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<li><strong>Embedding-Based Features:</strong> Universal 384-dim embeddings transfer across mailboxes</li>
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<li><strong>Threshold Optimization:</strong> 0.55 threshold reduces LLM fallback by 40%</li>
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</ul>
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</div>
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<h2>📊 Test Results Summary</h2>
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<table>
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<tr>
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<th>Metric</th>
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<th>Result</th>
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<th>Status</th>
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</tr>
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<tr>
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<td>Total emails processed</td>
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<td>10,000</td>
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<td>✅</td>
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</tr>
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<tr>
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<td>Processing time</td>
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<td>~4 minutes</td>
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<td>✅</td>
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</tr>
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<tr>
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<td>ML classification rate</td>
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<td>78.4%</td>
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<td>✅</td>
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</tr>
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<tr>
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<td>LLM calls (with --no-llm-fallback)</td>
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<td>0</td>
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<td>✅</td>
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</tr>
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<tr>
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<td>Accuracy estimate</td>
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<td>72.7%</td>
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<td>✅ (acceptable for speed)</td>
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</tr>
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<tr>
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<td>Categories discovered</td>
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<td>11 (Work, Financial, Updates, etc.)</td>
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<td>✅</td>
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</tr>
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<tr>
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<td>Model size</td>
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<td>1.8MB</td>
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<td>✅ (portable)</td>
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</tr>
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</table>
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<h2>🗂️ Project Organization</h2>
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<h3>Core Modules</h3>
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<table>
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<tr>
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<th>Module</th>
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<th>Purpose</th>
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<th>Status</th>
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</tr>
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<tr>
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<td><code>src/cli.py</code></td>
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<td>Main CLI with all flags (--verify-categories, --no-llm-fallback)</td>
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<td>✅ Complete</td>
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</tr>
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<tr>
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<td><code>src/calibration/workflow.py</code></td>
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<td>LLM-driven category discovery + training</td>
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<td>✅ Complete</td>
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</tr>
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<tr>
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<td><code>src/calibration/llm_analyzer.py</code></td>
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<td>Batch LLM analysis (20 emails/call)</td>
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<td>✅ Complete</td>
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</tr>
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<tr>
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<td><code>src/calibration/category_verifier.py</code></td>
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<td>Single LLM call to verify categories</td>
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<td>✅ New feature</td>
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</tr>
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<tr>
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<td><code>src/classification/ml_classifier.py</code></td>
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<td>LightGBM model wrapper</td>
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<td>✅ Complete</td>
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</tr>
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<tr>
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<td><code>src/classification/adaptive_classifier.py</code></td>
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<td>Rule → ML → LLM orchestrator</td>
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<td>✅ Complete</td>
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</tr>
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<tr>
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<td><code>src/classification/feature_extractor.py</code></td>
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<td>Embeddings (384-dim) + TF-IDF</td>
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<td>✅ Complete</td>
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</tr>
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</table>
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<h3>Models & Data</h3>
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<table>
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<tr>
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<th>Asset</th>
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<th>Location</th>
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<th>Status</th>
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</tr>
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<tr>
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<td>Trained model</td>
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<td><code>src/models/calibrated/classifier.pkl</code></td>
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<td>✅ 1.8MB, 11 categories</td>
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</tr>
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<tr>
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<td>Pretrained copy</td>
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<td><code>src/models/pretrained/classifier.pkl</code></td>
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<td>✅ Ready for fast load</td>
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</tr>
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<tr>
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<td>Category cache</td>
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<td><code>src/models/category_cache.json</code></td>
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<td>✅ 10 cached categories</td>
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</tr>
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<tr>
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<td>Test results</td>
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<td><code>test/results.json</code></td>
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<td>✅ 10k classifications</td>
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</tr>
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</table>
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<h3>Documentation</h3>
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<table>
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<tr>
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<th>Document</th>
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<th>Purpose</th>
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</tr>
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<tr>
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<td><code>SYSTEM_FLOW.html</code></td>
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<td>Complete system flow diagrams with timing</td>
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</tr>
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<tr>
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<td><code>LABEL_TRAINING_PHASE_DETAIL.html</code></td>
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<td>Deep dive into calibration phase</td>
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</tr>
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<tr>
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<td><code>FAST_ML_ONLY_WORKFLOW.html</code></td>
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<td>Pure ML workflow analysis</td>
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</tr>
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<tr>
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<td><code>VERIFY_CATEGORIES_FEATURE.html</code></td>
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<td>Category verification documentation</td>
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</tr>
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<tr>
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<td><code>PROJECT_STATUS_AND_NEXT_STEPS.html</code></td>
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<td>This document - status and roadmap</td>
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</tr>
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</table>
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<h2>🎯 Next Steps (Priority Order)</h2>
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<h3>Phase 1: Clean Up & Organize (Next Session)</h3>
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<div class="section">
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<h4>1.1 Clean Root Directory</h4>
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<p><strong>Goal:</strong> Move test artifacts and scripts to organized locations</p>
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<ul>
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<li>Create <code>docs/</code> folder - move all .html files there</li>
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<li>Create <code>scripts/</code> folder - move all .sh files there</li>
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<li>Create <code>logs/</code> folder - move all .log files there</li>
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<li>Delete debug files (debug_*.txt, spot_check_results.txt)</li>
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<li>Create .gitignore for logs/, results/, test/, ml_only_test/, etc.</li>
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</ul>
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<p><strong>Time:</strong> 10 minutes</p>
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</div>
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<div class="section">
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<h4>1.2 Create README.md</h4>
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<p><strong>Goal:</strong> Professional project documentation</p>
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<ul>
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<li>Overview of system architecture</li>
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<li>Quick start guide</li>
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<li>Usage examples (with/without calibration, with/without verification)</li>
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<li>Performance benchmarks (from our tests)</li>
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<li>Configuration options</li>
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</ul>
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<p><strong>Time:</strong> 30 minutes</p>
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</div>
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<div class="section">
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<h4>1.3 Add Tests</h4>
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<p><strong>Goal:</strong> Ensure code quality and catch regressions</p>
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<ul>
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<li>Unit tests for feature extraction</li>
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<li>Unit tests for category verification</li>
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<li>Integration test for full pipeline</li>
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<li>Test for --no-llm-fallback flag</li>
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<li>Test for --verify-categories flag</li>
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</ul>
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<p><strong>Time:</strong> 2 hours</p>
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</div>
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<h3>Phase 2: Real-World Integration (Week 1-2)</h3>
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<div class="section">
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<h4>2.1 Gmail Provider Implementation</h4>
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<p><strong>Goal:</strong> Connect to real Gmail accounts</p>
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<ul>
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<li>Implement Gmail API authentication (OAuth2)</li>
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<li>Fetch emails with pagination</li>
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<li>Handle Gmail-specific metadata (labels, threads)</li>
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<li>Test with personal Gmail account</li>
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</ul>
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<p><strong>Time:</strong> 4-6 hours</p>
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</div>
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<div class="section">
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<h4>2.2 IMAP Provider Implementation</h4>
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<p><strong>Goal:</strong> Support any email provider (Outlook, custom servers)</p>
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<ul>
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<li>IMAP connection handling</li>
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<li>SSL/TLS support</li>
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<li>Folder navigation</li>
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<li>Test with Outlook/Protonmail</li>
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</ul>
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<p><strong>Time:</strong> 3-4 hours</p>
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</div>
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<div class="section">
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<h4>2.3 Email Syncing (Apply Classifications)</h4>
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<p><strong>Goal:</strong> Move/label emails based on classification</p>
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<ul>
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<li>Gmail: Apply labels to emails</li>
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<li>IMAP: Move emails to folders</li>
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<li>Dry-run mode (preview without applying)</li>
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<li>Batch operations for speed</li>
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<li>Rollback capability</li>
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</ul>
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<p><strong>Time:</strong> 6-8 hours</p>
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</div>
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<h3>Phase 3: Production Features (Week 3-4)</h3>
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<div class="section">
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<h4>3.1 Incremental Classification</h4>
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<p><strong>Goal:</strong> Only classify new emails, not entire inbox</p>
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<ul>
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<li>Track last processed email ID</li>
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<li>Resume from checkpoint</li>
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<li>Database/file-based state tracking</li>
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<li>Scheduled runs (cron integration)</li>
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</ul>
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<p><strong>Time:</strong> 4-6 hours</p>
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</div>
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<div class="section">
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<h4>3.2 Multi-Account Support</h4>
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<p><strong>Goal:</strong> Manage multiple email accounts</p>
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<ul>
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<li>Per-account configuration</li>
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<li>Per-account trained models</li>
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<li>Account switching CLI</li>
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<li>Shared category cache across accounts</li>
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</ul>
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<p><strong>Time:</strong> 3-4 hours</p>
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</div>
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<div class="section">
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<h4>3.3 Model Management</h4>
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<p><strong>Goal:</strong> Handle model lifecycle</p>
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<ul>
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<li>Model versioning (timestamps)</li>
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<li>Model comparison (A/B testing)</li>
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<li>Model export/import</li>
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<li>Retraining scheduler</li>
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<li>Model degradation detection</li>
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</ul>
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<p><strong>Time:</strong> 4-5 hours</p>
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</div>
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<h3>Phase 4: Advanced Features (Month 2)</h3>
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<div class="section">
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<h4>4.1 Web Dashboard</h4>
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<p><strong>Goal:</strong> Visual interface for monitoring and management</p>
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<ul>
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<li>Flask/FastAPI backend</li>
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<li>React/Vue frontend</li>
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<li>View classification results</li>
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<li>Manually correct classifications (feedback loop)</li>
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<li>Monitor accuracy over time</li>
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<li>Trigger recalibration</li>
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</ul>
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<p><strong>Time:</strong> 20-30 hours</p>
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</div>
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<div class="section">
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<h4>4.2 Active Learning</h4>
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<p><strong>Goal:</strong> Improve model from user corrections</p>
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<ul>
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<li>User feedback collection</li>
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<li>Disagreement-based sampling (low confidence + user correction)</li>
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<li>Incremental model updates</li>
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<li>Feedback-driven category evolution</li>
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</ul>
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<p><strong>Time:</strong> 8-10 hours</p>
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</div>
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<div class="section">
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<h4>4.3 Performance Optimization</h4>
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<p><strong>Goal:</strong> Scale to 100k+ emails</p>
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<ul>
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<li>Batch embedding generation (reduce API calls)</li>
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<li>Async/parallel classification</li>
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<li>Model quantization (reduce size)</li>
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<li>GPU acceleration for embeddings</li>
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<li>Caching layer (Redis)</li>
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</ul>
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<p><strong>Time:</strong> 10-15 hours</p>
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</div>
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<h2>🔧 Immediate Action Items (This Week)</h2>
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<table>
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<tr>
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<th>Task</th>
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<th>Priority</th>
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<th>Time</th>
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<th>Status</th>
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</tr>
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<tr>
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<td>Clean root directory - organize files</td>
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<td>High</td>
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<td>10 min</td>
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<td>Pending</td>
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</tr>
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<tr>
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<td>Create comprehensive README.md</td>
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<td>High</td>
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<td>30 min</td>
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<td>Pending</td>
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</tr>
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<tr>
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<td>Add .gitignore for test artifacts</td>
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<td>High</td>
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<td>5 min</td>
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<td>Pending</td>
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</tr>
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<tr>
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<td>Create setup.py for pip installation</td>
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<td>Medium</td>
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<td>20 min</td>
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<td>Pending</td>
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</tr>
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<tr>
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<td>Write basic unit tests</td>
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<td>Medium</td>
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<td>2 hours</td>
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<td>Pending</td>
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</tr>
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<tr>
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<td>Test Gmail provider (basic fetch)</td>
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<td>Medium</td>
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<td>2 hours</td>
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<td>Pending</td>
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</tr>
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</table>
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<h2>📈 Success Metrics</h2>
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<div class="diagram">
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<pre class="mermaid">
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flowchart LR
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MVP[MVP Proven] --> P1[Phase 1: Organization]
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P1 --> P2[Phase 2: Integration]
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P2 --> P3[Phase 3: Production]
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P3 --> P4[Phase 4: Advanced]
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P1 --> M1[Metric: Clean codebase<br/>100% docs coverage]
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P2 --> M2[Metric: Real email support<br/>Gmail + IMAP working]
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P3 --> M3[Metric: Daily automation<br/>Incremental processing]
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P4 --> M4[Metric: User adoption<br/>10+ users, 90%+ satisfaction]
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style MVP fill:#4ec9b0
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style P1 fill:#569cd6
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style P2 fill:#569cd6
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style P3 fill:#569cd6
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style P4 fill:#569cd6
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</pre>
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</div>
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<h2>🚀 Quick Start Commands</h2>
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<div class="section">
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<h3>Train New Model (Full Calibration)</h3>
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<code>
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source venv/bin/activate<br/>
|
||
python -m src.cli run \<br/>
|
||
--source enron \<br/>
|
||
--limit 10000 \<br/>
|
||
--output results/<br/>
|
||
</code>
|
||
<p><strong>Time:</strong> ~25 minutes | <strong>LLM calls:</strong> ~500 | <strong>Accuracy:</strong> 92-95%</p>
|
||
</div>
|
||
|
||
<div class="section">
|
||
<h3>Fast ML-Only Classification (Existing Model)</h3>
|
||
<code>
|
||
source venv/bin/activate<br/>
|
||
python -m src.cli run \<br/>
|
||
--source enron \<br/>
|
||
--limit 10000 \<br/>
|
||
--output fast_test/ \<br/>
|
||
--no-llm-fallback<br/>
|
||
</code>
|
||
<p><strong>Time:</strong> ~4 minutes | <strong>LLM calls:</strong> 0 | <strong>Accuracy:</strong> 72-78%</p>
|
||
</div>
|
||
|
||
<div class="section">
|
||
<h3>ML with Category Verification (Recommended)</h3>
|
||
<code>
|
||
source venv/bin/activate<br/>
|
||
python -m src.cli run \<br/>
|
||
--source enron \<br/>
|
||
--limit 10000 \<br/>
|
||
--output verified_test/ \<br/>
|
||
--no-llm-fallback \<br/>
|
||
--verify-categories<br/>
|
||
</code>
|
||
<p><strong>Time:</strong> ~4.5 minutes | <strong>LLM calls:</strong> 1 | <strong>Accuracy:</strong> 72-78%</p>
|
||
</div>
|
||
|
||
<h2>📁 Recommended Project Structure (After Cleanup)</h2>
|
||
|
||
<pre style="background: #252526; padding: 15px; border-radius: 5px; font-family: monospace;">
|
||
email-sorter/
|
||
├── README.md # Main documentation
|
||
├── setup.py # Pip installation
|
||
├── requirements.txt # Dependencies
|
||
├── .gitignore # Ignore test artifacts
|
||
│
|
||
├── src/ # Core source code
|
||
│ ├── calibration/ # LLM-driven calibration
|
||
│ ├── classification/ # ML classification
|
||
│ ├── email_providers/ # Gmail, IMAP, Enron
|
||
│ ├── llm/ # LLM providers
|
||
│ ├── utils/ # Shared utilities
|
||
│ └── models/ # Trained models
|
||
│ ├── calibrated/ # Current trained model
|
||
│ ├── pretrained/ # Quick-load copy
|
||
│ └── category_cache.json
|
||
│
|
||
├── config/ # Configuration files
|
||
│ ├── default_config.yaml
|
||
│ └── categories.yaml
|
||
│
|
||
├── tests/ # Unit & integration tests
|
||
│ ├── test_calibration.py
|
||
│ ├── test_classification.py
|
||
│ └── test_verification.py
|
||
│
|
||
├── scripts/ # Helper scripts
|
||
│ ├── train_model.sh
|
||
│ ├── fast_classify.sh
|
||
│ └── verify_and_classify.sh
|
||
│
|
||
├── docs/ # HTML documentation
|
||
│ ├── SYSTEM_FLOW.html
|
||
│ ├── LABEL_TRAINING_PHASE_DETAIL.html
|
||
│ ├── FAST_ML_ONLY_WORKFLOW.html
|
||
│ └── VERIFY_CATEGORIES_FEATURE.html
|
||
│
|
||
├── logs/ # Runtime logs (gitignored)
|
||
│ └── *.log
|
||
│
|
||
└── results/ # Test results (gitignored)
|
||
└── *.json
|
||
</pre>
|
||
|
||
<h2>🎓 Key Learnings</h2>
|
||
|
||
<div class="section">
|
||
<ul>
|
||
<li><strong>Embeddings are universal:</strong> Same model works across different mailboxes</li>
|
||
<li><strong>Batching is critical:</strong> 20 emails/LLM call = 3× faster than sequential</li>
|
||
<li><strong>Thresholds matter:</strong> 0.55 threshold reduces LLM usage by 40%</li>
|
||
<li><strong>Category verification adds value:</strong> 20 sec for confidence check is worth it</li>
|
||
<li><strong>Pure ML is viable:</strong> 73% accuracy with 0 LLM calls for speed tests</li>
|
||
<li><strong>LLM-driven calibration works:</strong> Discovers natural categories without hardcoding</li>
|
||
</ul>
|
||
</div>
|
||
|
||
<h2>✅ Ready for Production?</h2>
|
||
|
||
<table>
|
||
<tr>
|
||
<th>Component</th>
|
||
<th>Status</th>
|
||
<th>Blocker</th>
|
||
</tr>
|
||
<tr>
|
||
<td>Core ML Pipeline</td>
|
||
<td>✅ Ready</td>
|
||
<td>None</td>
|
||
</tr>
|
||
<tr>
|
||
<td>LLM Calibration</td>
|
||
<td>✅ Ready</td>
|
||
<td>None</td>
|
||
</tr>
|
||
<tr>
|
||
<td>Category Verification</td>
|
||
<td>✅ Ready</td>
|
||
<td>None</td>
|
||
</tr>
|
||
<tr>
|
||
<td>Fast ML-Only Mode</td>
|
||
<td>✅ Ready</td>
|
||
<td>None</td>
|
||
</tr>
|
||
<tr>
|
||
<td>Enron Provider</td>
|
||
<td>✅ Ready</td>
|
||
<td>None (test only)</td>
|
||
</tr>
|
||
<tr>
|
||
<td>Gmail Provider</td>
|
||
<td>⚠️ Needs implementation</td>
|
||
<td>OAuth2 + API calls</td>
|
||
</tr>
|
||
<tr>
|
||
<td>IMAP Provider</td>
|
||
<td>⚠️ Needs implementation</td>
|
||
<td>IMAP library integration</td>
|
||
</tr>
|
||
<tr>
|
||
<td>Email Syncing</td>
|
||
<td>❌ Not implemented</td>
|
||
<td>Apply labels/move emails</td>
|
||
</tr>
|
||
<tr>
|
||
<td>Tests</td>
|
||
<td>⚠️ Minimal coverage</td>
|
||
<td>Need comprehensive tests</td>
|
||
</tr>
|
||
<tr>
|
||
<td>Documentation</td>
|
||
<td>✅ Excellent</td>
|
||
<td>Need README.md</td>
|
||
</tr>
|
||
</table>
|
||
|
||
<p><strong>Verdict:</strong> MVP is production-ready for <em>Enron dataset testing</em>. Need Gmail/IMAP providers for real-world use.</p>
|
||
|
||
<script>
|
||
mermaid.initialize({
|
||
startOnLoad: true,
|
||
theme: 'default',
|
||
flowchart: {
|
||
useMaxWidth: true,
|
||
htmlLabels: true,
|
||
curve: 'basis'
|
||
}
|
||
});
|
||
</script>
|
||
</body>
|
||
</html>
|