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Instead of a human manually checking spreadsheets for low stock, an **Autonomous AI Agent** wakes up, scans the database, and proactively drafts orders when inventory hits a critical threshold.\n\nThe system features a **Human-in-the-Loop** workflow: the AI *proposes* the order with reasoning (e.g., \"Stock is 5, minimum is 10\"), but a human manager must click \"Approve\" on the dashboard to finalize it.\n\n---\n\n## 🏗️ Architecture\n\nThe system is built as a monorepo with a clear separation of concerns:\n\n\n
\n
\n \"System\n
\n
\n\n\n### 1. The Brain (Backend)\n* **Framework:** Python FastAPI\n* **AI Orchestration:** CrewAI & LangChain\n* **LLM:** OpenAI GPT-4\n* **Database:** Supabase (PostgreSQL)\n\n### 2. The Interface (Frontend)\n* **Framework:** Next.js 15 (App Router)\n* **Styling:** Tailwind CSS & Shadcn UI\n* **State:** Server Actions & React Hooks\n\n---\n\n## ⚡ Key Features\n\n* **Autonomous Monitoring:** The Agent connects directly to the SQL database to check real-time stock levels.\n* **Intelligent Reasoning:** It doesn't just match numbers; it generates a natural language explanation for *why* an order is needed.\n* **Human-in-the-Loop UI:** A dedicated \"Approval Queue\" in the dashboard allows managers to review AI drafts before execution.\n* **Real-Time Updates:** The frontend refreshes instantly upon approval, providing immediate feedback (Optimistic UI).\n\n---\n\n## 🚀 Getting Started\n\n### Prerequisites\n* **Docker Desktop** (Recommended method)\n* **OR:** Node.js 18+ & Python 3.10+ (Manual method)\n* OpenAI API Key & Supabase Credentials\n\n### Option A: Run with Docker 🐳 (Recommended)\nThe fastest way to test the system. No need to install Python or Node locally.\n\n1. **Clone the repository:**\n\n```bash\ngit clone [https://github.com/yourusername/auto-procure.git](https://github.com/yourusername/auto-procure.git)\ncd auto-procure\n```\n\n2. **Configure Environment:** Create a ```.env``` file in the ```backend/``` folder:\n\n```bash\nOPENAI_API_KEY=sk-...\nSUPABASE_URL=http://host.docker.internal:54321 # Use this for local Docker (Windows/Mac)\n # OR use your cloud URL: https://xyz.supabase.co\nSUPABASE_KEY=...\n```\n\n3. **Launch the System:** Run this command in the root folder:\n\n```bash\ndocker-compose up --build\n```\n\nThe Dashboard will be available at ```http://localhost:3000``` and the API at ```http://localhost:8000```.\n\n---\n\n### Option B: Manual Installation 🛠️\nUse this if you want to modify the code or debug locally.\n\n1. **Backend Setup**\n\n```bash\ncd backend\npython -m venv venv\n# Windows: venv\\Scripts\\activate | Mac/Linux: source venv/bin/activate\n\npip install -r requirements.txt\n```\n\n2. **Configure Keys** Create a ```.env``` file in ```backend/``` as shown in Option A.\n\n3. **Run Backend**\n\n```bash\npython run.py\n# Server starts at [http://127.0.0.1:8000](http://127.0.0.1:8000)\n```\n\n4. **Frontend Setup** Open a new terminal:\n\n```bash\ncd frontend\nnpm install\nnpm run dev\n# Dashboard runs at http://localhost:3000\n```\n\n---\n\n## ▶️ Usage / Demo\n\nOnce both servers are running, you can manually trigger the AI Agent to perform a supply chain check cycle.\n\n**Option 1: Via Terminal (API)**\nOpen a new terminal and run:\n\n```bash\n# Windows (PowerShell) - Note the .exe usage\ncurl.exe -X POST http://localhost:8000/run-agent\n\n# Mac / Linux / Git Bash\ncurl -X POST http://localhost:8000/run-agent\n```\n\n**Option 2: Via Dashboard**\n\n1. Navigate to ```http://localhost:3000```.\n\n2. View the **Live Inventory Feed**.\n\n3. If an order is needed, the **\"AI Drafted Orders\"** section will appear automatically.\n\n4. Click **\"Approve\"** to finalize the order.\n\n---\n\n## 🧠 How the Agent Works\n\nThe Supply Chain Analyst agent follows a strict logic loop:\n\n* **OBSERVE:** Calls the ```check_low_stock``` tool to scan the ```products``` table in Supabase.\n\n* **REASON:** Identifies items where ```current_stock < min_threshold```.\n\n* **ACT:** Groups items by vendor and calls the ```create_draft_order``` tool.\n\n* **RECORD:** Writes a new row to the ```purchase_orders``` table with status ```pending_approval```.\n\n**Example AI Output:**\n***\"I have detected low stock for RTX 3080 (Count: 2, Min: 10). 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