Spaces:
Runtime error
Runtime error
File size: 2,713 Bytes
e2d5c54 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 |
import os
import joblib
import shutil
from huggingface_hub import hf_hub_download, HfApi
from typing import List
def download_model_from_hf(model_repo: str, model_filename: str = None, token: str = None, local_dir: str = "/app/model"):
"""
Try to download the model file from HF model repo.
If model_filename is None, attempt fallback names (best_overall_XGBoost, RandomForest, Bagging, DecisionTree).
Returns local filepath.
"""
os.makedirs(local_dir, exist_ok=True)
api = HfApi(token=token)
candidates = []
if model_filename:
candidates.append(model_filename)
# fallback candidates (order of preference)
candidates.extend([
"best_overall_XGBoost.joblib",
"best_overall_RandomForest.joblib",
"best_overall_Bagging.joblib",
"best_overall_DecisionTree.joblib",
"best_XGBoost.joblib",
"best_RandomForest.joblib",
"best_Bagging.joblib",
"best_DecisionTree.joblib",
])
last_exception = None
for fn in candidates:
try:
print(f"Trying to download '{fn}' from '{model_repo}' ...")
remote = hf_hub_download(repo_id=model_repo, filename=fn, repo_type="model", use_auth_token=token)
# hf_hub_download returns a cache path; copy into local_dir with same filename
dest = os.path.join(local_dir, os.path.basename(remote))
if remote != dest:
shutil.copy(remote, dest)
print("Downloaded model to:", dest)
return dest
except Exception as e:
last_exception = e
print(f"Could not download {fn}: {e}")
# If we got here no candidate succeeded
raise FileNotFoundError(f"Model not found in repo '{model_repo}'. Tried: {candidates}. Last error: {last_exception}")
def load_model(local_model_path: str):
"""Load joblib model/pipeline from given local path."""
return joblib.load(local_model_path)
def inputs_to_dataframe(payload: dict, feature_order: List[str]):
"""
Convert one record (dict) to dataframe with fixed column order.
"""
import pandas as pd
if isinstance(payload, dict):
rows = [payload]
elif isinstance(payload, list):
rows = payload
else:
raise ValueError("Payload must be dict or list of dicts")
df = pd.DataFrame(rows)
# ensure columns exist
for c in feature_order:
if c not in df.columns:
df[c] = pd.NA
df = df[feature_order].copy()
# try cast numeric columns where possible
for col in df.columns:
try:
df[col] = pd.to_numeric(df[col], errors="ignore")
except Exception:
pass
return df
|