Merge pull request #55 from DaoyuanLi2816/docs/hero-banner

Add SVG hero banner and switch README images to absolute URLs

Author: DaoyuanLi2816

README.md

@@ -1,17 +1,23 @@
-# Molecule-Generator
+<p align="center">
+  <img src="https://raw.githubusercontent.com/DaoyuanLi2816/Molecule-Generator/main/docs/banner.svg" alt="molgen — lightweight de novo molecular generation: SMILES and SELFIES tokenizers, Transformer β-TC-VAE / CharRNN / MolGPT generators, MOSES-style evaluation." width="880">
+</p>
+
+<div align="center">
 
 [![CI](https://github.com/DaoyuanLi2816/Molecule-Generator/actions/workflows/ci.yml/badge.svg)](https://github.com/DaoyuanLi2816/Molecule-Generator/actions/workflows/ci.yml)
 [![Python](https://img.shields.io/badge/python-3.10%2B-blue.svg)](https://www.python.org/)
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)
 [![Code style: ruff](https://img.shields.io/badge/code%20style-ruff-000000.svg)](https://github.com/astral-sh/ruff)
 
+</div>
+
 A lightweight, modern toolkit for **de novo molecular generation** with deep
 sequence models. It provides atom-level SMILES and SELFIES tokenizers, several
 generator architectures, a mixed-precision training loop, configurable
 sampling, and a MOSES-style evaluation suite — small enough to train on a single
 GPU in minutes, but reflecting current practice.
 
-![molgen — a molecular generation pipeline: input, tokenize, model, sample, evaluate](./assets/pipeline.png)
+![molgen — a molecular generation pipeline: input, tokenize, model, sample, evaluate](https://raw.githubusercontent.com/DaoyuanLi2816/Molecule-Generator/main/assets/pipeline.png)
 
 ## Features
 
@@ -90,14 +96,14 @@ Both figures come from **real model output** and are reproducible with
 
 **Generated molecules** — structures sampled directly from the trained model:
 
-![Molecules generated by the model](./assets/generated_molecules.png)
+![Molecules generated by the model](https://raw.githubusercontent.com/DaoyuanLi2816/Molecule-Generator/main/assets/generated_molecules.png)
 
 **Goal-directed generation** — from a *single* base model, fine-tuning toward
 the most (or least) drug-like molecules steers the generated QED distribution
 in **both** directions (a ~0.15 QED span) and moves the samples through QED-vs-SA
 property space. Generation can be steered toward a target, not just imitated:
 
-![Bidirectional QED steering and movement through QED–SA property space](./assets/controlled_generation.png)
+![Bidirectional QED steering and movement through QED–SA property space](https://raw.githubusercontent.com/DaoyuanLi2816/Molecule-Generator/main/assets/controlled_generation.png)
 
 ## Models
 
@@ -114,7 +120,7 @@ Both `CharRNN` and `MolGPT` train and sample through the same trainer/sampler.
 The original VAE workflow is still available for generating molecules near a
 seed or interpolating between two molecules in latent space:
 
-![VAE encoder, latent space, and decoder](./molecule.png)
+![VAE encoder, latent space, and decoder](https://raw.githubusercontent.com/DaoyuanLi2816/Molecule-Generator/main/molecule.png)
 
 ```bash
 python -m molgen.synthetic     # build a synthetic dataset (molecules.csv)