# Riprap runtime dependencies (deployment).
# Offline-only deps (py3dep, terratorch, whitebox-workflows) are NOT here
# — they only run for the one-time fixture pre-compute in scripts/.

# Web + streaming
fastapi>=0.115
uvicorn>=0.32
httpx>=0.27
pydantic>=2.9

# Geo
geopandas>=1.0
shapely>=2.0
pyproj>=3.6
fiona>=1.10
rasterio>=1.4

# Data
pandas>=2.2
pyarrow>=18.0
numpy>=1.26

# pyarrow.PyExtensionType was removed in pyarrow 17 and older `datasets`
# (transitive dep of sentence-transformers / gliner / terratorch) crashes
# on import against pyarrow 18+. datasets >= 3.0 uses pa.ExtensionType
# instead. Without this pin, HF Spaces' resolver picks an older datasets
# and FastAPI startup dies before the first request.
datasets>=3.0

# RAG: Granite Embedding 278M (CPU torch is sufficient on HF Spaces).
# sentence-transformers 3.x's model_card.py does
# `from transformers.integrations import CodeCarbonCallback` at import
# time. transformers's lazy-import surface raises if `codecarbon` isn't
# present; on HF Space (Python 3.10) the lazy-import resolution fails
# even on import-only paths. Two options:
#   (1) pin sentence-transformers to a version that didn't have model_card
#   (2) install codecarbon so the lazy-import resolves
# We tried (1) at <3.4 and <4 — both failed because 3.3.x also imports
# CodeCarbonCallback. Going with (2): codecarbon is a small ~7MB pure-
# Python package; we don't enable its tracking, just satisfy the import.
sentence-transformers>=3.3,<4
codecarbon>=2.5,<4
pypdf>=5.0

# Tight coexistence pins: granite-tsfm 0.3.x calls transformers.utils
# .download_url which was removed in transformers 5.x; mellea 0.3.x is
# happy with the older hf_hub. Keep BOTH older to avoid the conflict
# (transformers >=4.55,<5 + huggingface_hub >=0.34,<1).
transformers>=4.55,<5
huggingface_hub>=0.34,<1

# Granite 4.1 reconciliation via Ollama (local fallback)
ollama>=0.4

# LiteLLM Router: unifies vLLM (AMD GPU, OpenAI-compatible) and Ollama
# behind one chat() call surface, with automatic primary->fallback
# routing when RIPRAP_LLM_PRIMARY=vllm is unreachable. See app/llm.py.
litellm>=1.52

# GLiNER specialist (Phase 2): typed entity extraction over RAG output.
# Apache-2.0 model is `urchade/gliner_medium-v2.1` — NOT the gliner_base
# variant which is CC-BY-NC-4.0. See app/context/gliner_extract.py.
gliner>=0.2.13

# Phase 1 (Prithvi live) + Phase 4 (TerraMind) + earth-observation deps.
#
# These deps live in `requirements-experiments.txt` (local + AMD), NOT
# in production. Two attempts at bringing them into the HF image (the
# floor at 1.0.x then the pin at 1.1rc6) both failed pip resolution
# against our Py3.10 constraints (transformers<5, hf_hub<1,
# granite-tsfm<0.3.4, mellea<0.4). `terratorch>=1.2` pins numpy>=2.2
# which breaks the rest of the stack; `1.1rc6` and earlier had
# transitive cone conflicts the resolver couldn't satisfy in the
# 30-second pip budget.
#
# On HF Spaces the lazy-import path returns clean `skipped: deps
# unavailable on this deployment` for terramind_synthesis (which has
# no remote-inference path); the other EO specialists (prithvi_live,
# terramind_lulc, terramind_buildings) work via app/inference.py
# routing to the AMD MI300X droplet, provided we have the chip-fetch
# deps below — they're small (pure-Python or thin wrappers around
# numpy/rasterio which we already have) and don't pull terratorch or
# torchvision binaries.
#   - planetary-computer / pystac-client: STAC search at Microsoft PC
#   - rioxarray / xarray: COG band reads
#   - einops: tensor reshape used by prithvi_live._build_chip
planetary-computer>=1.0
pystac-client>=0.7
rioxarray>=0.15
xarray>=2024.1
einops>=0.7

# Burr FSM
burr>=0.40

# Granite TimeSeries TTM r2 — short-horizon residual nowcast (Ekambaram et al.
# 2024, NeurIPS). The PyPI package name is granite-tsfm; importable as
# tsfm_public. Brings in transformers + accelerate; torch is already in the
# CUDA base image.
# Pinned to 0.3.3 because >=0.3.4 dropped Python 3.10 support and the
# CUDA-runtime base image ships Ubuntu 22.04 / Python 3.10. The
# tsfm_public.toolkit.get_model API is stable across this minor range.
granite-tsfm>=0.3.0,<0.3.4

# IBM Research's Mellea — instruct/validate/repair framework. Powers the
# default reconciler: Granite output + programmatic post-conditions
# + rejection sampling.
# Pinned to <0.4 because 0.4+ requires Python>=3.11 and the
# nvidia/cuda:12.4.1-cudnn-runtime-ubuntu22.04 base image ships Python 3.10.
# 0.3.2 has the same instruct/req/RejectionSamplingStrategy API surface
# we use; if it doesn't, the validator falls through to the standard
# reconciler (graceful degradation).
mellea>=0.3.0,<0.4

# Misc
tqdm>=4.66