Namtheg: LangChain-Orchestrated AutoML 🛠️

Namtheg is a premium, end-to-end agentic AutoML platform. It automates the entire machine learning pipeline—from raw CSV upload, profiling, target selection, and feature engineering to model training, evaluation plotting, and instant serverless API deployment.

Driven by a LangChain agentic brain (DeepSeek via OpenRouter) and equipped with tailored Python execution tools, Namtheg makes training and deploying custom ML models a seamless, single-click experience.

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🏗️ Architecture & Monorepo Layout

This repository is structured as a modern monorepo separating frontend UI from backend execution:

├── Backend/                 # Python FastAPI Backend
│   ├── app/
│   │   ├── agent/           # LangChain Tool-calling Orchestrator & System Prompts
│   │   ├── pipeline/        # Core pipeline steps (Profiling, Feature Engineering, Training, Visualization)
│   │   ├── deploy/          # Modal serverless deployment logic
│   │   ├── storage.py       # Local file-based run manager
│   │   └── main.py          # FastAPI application routes
│   ├── requirements.txt     # Backend Python dependencies
│   └── .env.example         # Example local backend environment variables
│
├── Frontend/                # Next.js 15 Frontend
│   ├── app/                 # Next.js App Router (Upload, Preview, Running, Result, Inference UI)
│   ├── components/          # Reusable UI component library (TailwindCSS + Framer Motion)
│   ├── lib/                 # Frontend API client library
│   ├── package.json         # Node.js dependencies and scripts
│   └── .env.local.example   # Example local frontend environment variables
│
├── Docs/                    # Product specs and agent rules
│   ├── PRD.md               # Product Requirements Document
│   └── AGENTS.md            # Agent Operational guidelines
│
├── render.yaml              # Render Deployment Blueprint config
└── insurance.csv            # Sample dataset for demonstration

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🧠 LangChain AutoML Pipeline Flow

When you select a target column and click Start AutoML, Namtheg kicks off a stateful LangChain agent that executes the following specialized tools sequentially:

1. profile_dataset: Inspects schemas, missing values, distinct values, and data types.
2. detect_problem_type: Auto-detects whether the task is regression or classification based on the target column cardinality and datatype.
3. run_eda: Computes descriptive statistics, target distribution characteristics, and feature-target correlations.
4. feature_engineer: Dynamically drops high-missing or high-cardinality ID columns, imputes missing values (median/mode), and encodes categorical columns.
5. train_model: Trains a Random Forest (Regressor or Classifier) with a 5-fold Cross-Validation + 80/20 holdout split.
6. generate_visualization: Generates a Predicted-vs-Actual scatter plot (regression) or Confusion Matrix heatmap (classification).
7. Justification: DeepSeek reviews all metrics and writes a highly structured 3-5 sentence analysis explaining model performance.

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🚀 Local Quickstart

1. Prerequisites

• Python 3.10+ installed
• Node.js 18+ installed

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2. Backend Setup

1. Navigate to the backend directory:

   cd Backend

2. Create and activate a virtual environment:

   python -m venv .venv
   # Windows:
   .venv\Scripts\Activate.ps1
   # macOS/Linux:
   source .venv/bin/activate

3. Install dependencies:

   pip install -r requirements.txt

4. Configure your environment variables:

   copy .env.example .env

   Open .env and fill in:

   • OPENROUTER_API_KEY: Get one from OpenRouter.
   • MODAL_WORKSPACE: Your Modal username.

5. Start the backend:

   uvicorn app.main:app --reload --port 8000

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3. Frontend Setup

1. Navigate to the frontend directory:

   cd Frontend

2. Install npm packages:

   npm install

3. Set up local environment variables:

   copy .env.local.example .env.local

4. Start the Next.js development server:

   npm run dev

   Open http://localhost:3000 to interact with the UI.

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🌐 Deploying to Production

Namtheg is fully ready for multi-tier production deployment:

1. FastAPI & Next.js on Render

A pre-configured Render Blueprint (render.yaml) is located in the root. When pushed to GitHub and connected to Render:

• It automatically provisions the FastAPI backend at namtheg-b.
• It provisions the Next.js frontend at namtheg, automatically wiring it to communicate internally with the backend.
• Environment variables (OPENROUTER_API_KEY, MODAL_TOKEN_ID, etc.) are securely requested during setup.

2. Serverless Predictors on Modal

When a user clicks "Deploy to Modal" on their successfully trained model:

• The backend leverages Modal serverless volumes (namtheg-models) and the shared app (namtheg-inference).
• Zero-cold-start uploads: The model is saved directly to a mounted persistent volume rather than redeploying containers.
• Interactive serverless prediction endpoints are served dynamically!

One-Time Modal Setup:

Ensure you deploy the core serverless inference wrapper once to your Modal space:

cd Backend
pip install modal
modal token new
modal deploy app/deploy/inference_app.py

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📄 License

Copyright (c) 2026 Khalid. All Rights Reserved. See LICENSE.

This project is source-available for viewing only. No use, copying, modification, or distribution is permitted without prior written permission from the copyright holder.