README.md

metadata

title: ModelSmith AI
emoji: 🤖
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 6.5.1
app_file: app.py
pinned: false
license: apache-2.0
short_description: 'An intelligent ML platform '

ModelSmith AI

An intelligent ML platform that automates tabular classification and regression tasks. It analyzes datasets, recommends optimal strategies, trains models, and provides explanations.

Features

• Dataset Analysis: Automatic detection of data types, missing values, and potential issues
• Strategy Reasoning: Intelligent model selection based on dataset characteristics
• Automated Training: End-to-end model training with preprocessing pipelines
• Explainability: SHAP-based feature importance explanations
• FastAPI Backend: RESTful API for seamless integration

Supported Scope

• Task: Tabular classification and regression
• Input: CSV files with ≥1200 rows
• Target: Binary or multiclass classification, regression
• Features: At least 2 usable features after preprocessing

API Endpoints

• POST /analyze: Analyze dataset and get strategy recommendations
• POST /train: Train a model on the dataset
• POST /explain: Get model explanations and feature importance
• POST /predict: Make predictions with trained model
• GET /health: Health check

Deployment

This project is designed for deployment on Hugging Face Spaces using Docker.

Files for Deployment

• Dockerfile
• requirements.txt
• backend/ (entire directory)

Running Locally

pip install -r requirements.txt
uvicorn backend.api.main:app --host 0.0.0.0 --port 7860

Limitations

• NLP functionality is disabled
• Requires datasets with ≥1200 rows
• CPU-only, no GPU support
• Stateless API (models saved temporarily)

Architecture

• Orchestrator: Main workflow coordinator
• Dataset Analyzer: Data profiling and preprocessing
• Strategy Reasoner: Model selection logic
• Model Factory: Training and evaluation
• Explainability Engine: SHAP explanations

License

MIT License