| """ | |
| PhenoSeq — minimal inference example. | |
| Downloads the pretrained model from HuggingFace and generates | |
| scGPT RNA-seq embeddings from synthetic ViT-L imaging features. | |
| Usage: | |
| pip install torch numpy huggingface_hub | |
| python example.py | |
| """ | |
| import numpy as np | |
| from pipeline import PhenoSeqPipeline | |
| # ── Load model from the Hub ──────────────────────────────────────────────────── | |
| pipe = PhenoSeqPipeline.from_pretrained("Sentinal4D/PhenoSeq") | |
| print(pipe) | |
| # ── Prepare imaging features ─────────────────────────────────────────────────── | |
| # Real use: extract ViT-L/14 embeddings from 5-channel fluorescence microscopy. | |
| # Shape: (n_cells, n_imaging_cells, 5120) | |
| # n_cells — number of single cells to predict RNA for | |
| # n_imaging_cells — imaging cells sampled per well (16 during training) | |
| # 5120 — 5 fluorescence channels × 1024 ViT-L dims | |
| n_cells = 8 | |
| n_imaging_cells = 16 | |
| img_features = np.random.randn(n_cells, n_imaging_cells, 5120).astype(np.float32) | |
| # ── Run inference ────────────────────────────────────────────────────────────── | |
| # Returns scGPT-space embeddings in the original (denormalized) scale. | |
| # DDIM with 50 steps by default; pass ddim_steps=0 for full 1000-step DDPM. | |
| rna_predictions = pipe(img_features) | |
| print(f"\nInput imaging features : {img_features.shape}") # (8, 16, 5120) | |
| print(f"Output RNA embeddings : {rna_predictions.shape}") # (8, 512) | |
| print(f"Output range : [{rna_predictions.min():.3f}, {rna_predictions.max():.3f}]") | |