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Upload requirements.txt
Browse files- requirements.txt +7 -64
requirements.txt
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/data/output signal CSVs, holdings.txt
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/data/reports auto-generated research reports (markdown)
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/data/traces agent traces (JSON) — shareable on the Hub (📡 badge)
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/data/hf_cache GGUF model files (downloaded once, kept forever)
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/data/pylibs llama-cpp-python installed once at runtime, persisted
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Without a bucket the app still works — it just falls back to the container
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filesystem (wiped on restart).
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"""
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from __future__ import annotations
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import os
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import sys
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def _writable(p: str) -> bool:
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try:
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return os.path.isdir(p) and os.access(p, os.W_OK)
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except OSError:
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return False
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DATA_ROOT = "/data" if _writable("/data") else "."
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PERSISTENT = DATA_ROOT == "/data"
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CACHE_DIR = os.path.join(DATA_ROOT, "cache_us")
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OUTPUT_DIR = os.path.join(DATA_ROOT, "output") if PERSISTENT else "./_app_output"
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REPORTS_DIR = os.path.join(DATA_ROOT, "reports")
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TRACES_DIR = os.path.join(DATA_ROOT, "traces")
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DATASET_DIR = os.path.join(DATA_ROOT, "dataset") # SFT training pairs (JSONL)
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HF_CACHE_DIR = os.path.join(DATA_ROOT, "hf_cache")
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PYLIBS_DIR = os.path.join(DATA_ROOT, "pylibs")
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for _d in (CACHE_DIR, OUTPUT_DIR, REPORTS_DIR, TRACES_DIR, DATASET_DIR, HF_CACHE_DIR, PYLIBS_DIR):
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try:
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os.makedirs(_d, exist_ok=True)
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except OSError:
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pass
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# GGUF downloads (hf_hub_download) go to the bucket so models persist
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if PERSISTENT:
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os.environ.setdefault("HF_HOME", HF_CACHE_DIR)
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# llama-cpp-python installed into the bucket must be importable.
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# APPEND (not insert) so bucket-installed copies of common deps (numpy etc.)
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# can never shadow the system site-packages the app was built against.
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if PERSISTENT and PYLIBS_DIR not in sys.path:
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sys.path.append(PYLIBS_DIR)
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def storage_status() -> str:
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if PERSISTENT:
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return ("✅ Persistent storage bucket mounted at `/data` — data cache, "
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"model files, reports, traces and the llama.cpp runtime all "
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"survive restarts.")
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return ("⚠️ No `/data` bucket detected — running on ephemeral container "
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"storage (everything re-downloads after a restart).")
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gradio>=5.49
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pandas>=2.0
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numpy>=1.24
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pyarrow>=14
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yfinance>=0.2.40
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apscheduler>=3.10
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huggingface_hub>=0.23
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