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| 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 | |
| ```bash | |
| 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 |