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\n\n## πΈ What It Does\n\nStyle Agent acts as your personal AI stylist. You fill in a few details on the left panel, click **Generate My Outfits**, and within seconds receive:\n\n- **3 complete outfit looks** β clothing, footwear, bag, and matching jewellery\n- Outfits correct for your **gender** (Women / Men), **vibe** (Ethnic, Modern, Bohoβ¦), and **occasion** (Wedding, Office, Brunchβ¦)\n- **HSL-based colour palette** β three harmonious suggestions (complementary, triadic, split-complementary)\n- **Real shopping links** β 3 clickable pill buttons per item opening Google Shopping, Myntra, and Ajio\n- **Formality scoring** β no sneakers with a bridal lehenga\n- **AI Chat Stylist** β a floating ChatGPT-style dialog powered by Ollama llama3\n\n
\n\n## ποΈ Architecture\n\n```\nrun.py\n ββ LangGraph workflow (workflow/langgraph_state.py)\n ββ Node 1: PersonaAgent β builds user style profile from DB history\n ββ Node 2: ColourEngineAgent β HSL palette math, hexβcolour family mapping\n ββ Node 3: TrendScoutAgent β occasion & vibe trend analysis\n ββ Node 4: WardrobeArchitectAgent β 5-tier DB query, outfit assembly\n ββ Node 5: JewelleryAgent β matches jewellery to skin tone & outfit\n```\n\nAll agents run in a **LangGraph state machine**. The CrewAI crew (`workflow/crewai_crew.py`) wraps them in a multi-agent task collaboration layer.\n\n
\n\n## β¨ Key Features\n\n| Feature | Detail |\n|---|---|\n| **5-Tier Colour Matching** | `colour_family` (warm/cool/neutral/earth/pastel/jewel) fallback so the DB always yields results |\n| **Dense Inventory** | 1,185 auto-generated items β Women, Men, Unisex Γ 17 colours Γ all vibes |\n| **Formality Scoring** | Every occasion scored 1-5; footwear & bag picked to match |\n| **Gender Filter** | π© Women / π¨ Men toggle routes to correct Indian garment categories |\n| **Indian Ethnic Garments** | Lehenga, Sharara, Gharara, Anarkali, Saree, Sherwani, Bandhgala, Kurta, Mojari |\n| **Outfit Coherence Validator** | Flags mismatches (sneakers with lehenga, missing dupatta) |\n| **Google Shopping Links** | 3 pill buttons per item (Google Shopping Β· Myntra Β· Ajio) β always work |\n| **Match Transparency** | Pale yellow notice if colour was approximated (explains tier used) |\n| **AI Chat Stylist** | Floating ChatGPT-style dialog driven by Ollama llama3 |\n| **Hex Colour Picker** | 24-swatch grid + OS colour wheel + manual hex input |\n| **Vibe Tiles** | 8 vibes with accent colours and emoji β click to select |\n\n
\n\n## π Project Structure\n\n```\nStyleAgentRetailAnalyst/\nβ\nβββ run.py # π Entry point β python run.py\nβ\nβββ agents/\nβ βββ persona_agent.py # Agent 1: user style profile\nβ βββ colour_engine_agent.py # Agent 2: palette math + hexβfamily\nβ βββ trend_scout_agent.py # Agent 3: vibe & occasion trends\nβ βββ wardrobe_architect_agent.py # Agent 4: 5-tier DB query + outfit assembly\nβ βββ jewellery_agent.py # Agent 5: jewellery matching\nβ\nβββ workflow/\nβ βββ langgraph_state.py # LangGraph state machine (agent pipeline)\nβ βββ crewai_crew.py # CrewAI multi-agent task crew\nβ\nβββ database/\nβ βββ setup_database.py # Creates + seeds all 6 SQLite tables\nβ βββ inventory.db # The live SQLite database (auto-created)\nβ βββ sql_queries.py # Query helpers\nβ\nβββ gui/\nβ βββ tkinter_app.py # Full Tkinter GUI (1,400+ lines)\nβ\nβββ scraper/\nβ βββ live_link_scraper.py # Builds Google Shopping / Myntra / Ajio URLs\nβ\nβββ requirements.txt\nβββ setup_guide.txt\n```\n\n
\n\n## π Quick Start\n\n### Prerequisites\n- Python 3.10+\n- [Ollama](https://ollama.ai) installed and running locally (for AI chat only)\n\n### 1. Install dependencies\n```bash\npip install -r requirements.txt\n```\n\n### 2. Pull the Ollama model (for AI chat)\n```bash\nollama pull llama3\n```\n\n### 3. Run the app\n```bash\npython run.py\n```\n\n> The database is created automatically on first run β no manual setup needed.\n\n
\n\n## π§° Tech Stack\n\n| Layer | Technology |\n|---|---|\n| **Language** | Python 3.10+ |\n| **GUI** | Tkinter (built-in) |\n| **Database** | SQLite via `sqlite3` (built-in) |\n| **Agent Orchestration** | [LangGraph](https://github.com/langchain-ai/langgraph) + [CrewAI](https://github.com/joaomdmoura/crewai) |\n| **Local LLM** | [Ollama](https://ollama.ai) β llama3 |\n| **Colour Math** | `colorsys` (built-in) β HSL/HSV analysis |\n| **HTTP** | `requests` + `beautifulsoup4` (trend data) |\n| **Shopping Links** | Google Shopping URL construction (no scraping) |\n\n
\n\n## ποΈ Database Schema\n\nThe SQLite database (`database/inventory.db`) contains 6 tables:\n\n| Table | Purpose | Rows |\n|---|---|---|\n| `current_inventory` | Full fashion catalogue β programmatically generated | **1,185** |\n| `jewellery_inventory` | Jewellery pieces matched to skin tone | 30 |\n| `user_profile` | Stored style preferences | 1 (sample) |\n| `purchase_history` | Past purchases for persona analysis | seeded |\n| `browsing_logs` | Viewed items for personalisation | seeded |\n| `outfit_history` | Generated outfits (saved by app) | starts empty |\n\n### `current_inventory` key columns\n| Column | Type | Description |\n|---|---|---|\n| `colour_family` | TEXT | `warm / cool / neutral / earth / pastel / jewel` |\n| `gender` | TEXT | `Women / Men / Unisex` |\n| `formality_score` | INTEGER | 1 (casual) β 5 (black tie) |\n| `vibe_tags` | TEXT | comma-separated e.g. `\"ethnic,classic\"` |\n| `occasion_tags` | TEXT | comma-separated e.g. `\"wedding,sangeet,festive\"` |\n| `category` | TEXT | `lehenga / saree / sharara / kurta_pyjama / top / bottom / footwear / bag β¦` |\n\n
\n\n## π€ Agent Pipeline Detail\n\n### Agent 1 β PersonaAgent\nReads `user_profile`, `purchase_history`, and `browsing_logs` to build a style persona. Outputs comfort level, brand tier affinity, and risk score.\n\n### Agent 2 β ColourEngineAgent\nUses `colorsys.rgb_to_hsv()` to map any hex code to one of **6 colour families**, then generates 3 harmonious palettes (complementary, triadic, split-complementary). New in v5: `get_search_colours_for_hex()` for DB-compatible colour lookups.\n\n### Agent 3 β TrendScoutAgent\nAnalyses the occasion and vibe to output relevant trend keywords, silhouette guidance, and fabric suggestions. Checks live trend data where available.\n\n### Agent 4 β WardrobeArchitectAgent *(core)*\nBuilds 3 complete outfits using a **5-tier SQL fallback system**:\n1. `colour_family + vibe + occasion + gender + formality Β±1`\n2. `colour_family + vibe + gender`\n3. `colour_family + gender`\n4. `gender + formality`\n5. `gender + price` *(guaranteed result)*\n\nAlso runs the `OutfitCoherenceValidator` to catch formality mismatches.\n\n### Agent 5 β JewelleryAgent\nPicks earrings, necklace, bangles, and optional maang tikka based on the outfit's metal tone (gold for warm skin, silver for cool) and occasion formality.\n\n
\n\n## π v5 Upgrades (7-Fix Series)\n\n| # | Fix | Impact |\n|---|---|---|\n| 6 | Dense inventory generator | 1,185 items from 70 templates Γ 17 colours |\n| 1 | Colour family matching | No more \"0 results\" β hex β family β 5-tier fallback |\n| 7 | Formality scoring | Correct footwear/bags for every occasion type |\n| 4 | Outfit coherence validator | Flags sneakers-with-lehenga, missing dupattas |\n| 3 | Gender filter | Correct Indian garments for Women vs Men |\n| 2 | Google Shopping links | 3 pill buttons per item β always clickable |\n| 5 | Match transparency messages | Explains any colour approximation to user |\n\n
\n\n## π License\n\nMIT β free to use, modify, and distribute.\n\n---\n\n*Built with β€οΈ by Chitra Kulkarni β [GitHub](https://github.com/chitrakulkarni2830/StyleAgent-Retail-Analyst)*\n","readmeExcerpt":"π Style Agent β Hyper-Personalised AI Fashion Stylist **An end-to-end AI agent pipeline** that analyses your body type, skin undertone, occasion, vibe, and budget to generate three complete, coherent outfits β each with live Google Shopping links, colour-matched jewellery, and stylist-quality notes.
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