update electricity.py

electricity_prices.py

@@ -1,129 +1,28 @@
 # electricity_prices.py
+"""
+Simplified electricity price module for Hugging Face demo.
 
-import os
-from datetime import date as Date
-from typing import Dict, Optional
+We don't load CSVs or use HF_DATASET_TOKEN anymore.
+The real-time app takes electricity_rate directly from the user.
 
-import pandas as pd
-from huggingface_hub import hf_hub_download
+This file only exists:
+- to provide a simple default ELECTRICITY_RATES dict (if needed)
+- to keep compatibility with older imports (get_electricity_rate)
+"""
 
-# ---------------------------------------------------------------------------
-# Configuration
-# ---------------------------------------------------------------------------
-
-# Private dataset repo on Hugging Face containing the CSV files
-HF_DATASET_REPO = "sithuWiki/electricity"
-HF_DATASET_TOKEN_ENV = "HF_DATASET_TOKEN"  # set this in your Space secrets
-
-# Fallback / base rates used when a date is outside the CSV range
-BASE_ELECTRICITY_RATES: Dict[str, float] = {
-    "texas": 0.1549,
+# Simple default average rates for reference / fallback
+ELECTRICITY_RATES = {
+    "texas": 0.15,    # average residential USD/kWh (example)
     "china": 0.08,
     "ethiopia": 0.01,
 }
 
-# Mapping from region name -> CSV filename in the private dataset
-REGION_FILES: Dict[str, str] = {
-    "texas": "texas_residential_daily_df.csv",
-    "china": "china_electricity_prices_daily.csv",
-    "ethiopia": "ethiopia_electricity_prices_daily.csv",
-}
-
-# In-memory cache: region -> pandas.Series indexed by python date with float prices
-_ELECTRICITY_SERIES: Dict[str, Optional[pd.Series]] = {}
-
-
-def _get_token() -> str:
-    token = os.environ.get(HF_DATASET_TOKEN_ENV)
-    if not token:
-        raise RuntimeError(
-            f"Environment variable {HF_DATASET_TOKEN_ENV} is not set. "
-            "Add a read token for the private dataset to your Space secrets."
-        )
-    return token
-
-
-def _load_region_series(region: str, filename: str) -> Optional[pd.Series]:
-    """
-    Load a single region's CSV from the private HF dataset as a Series.
-
-    Expected columns in CSV:
-      - 'date'  (any format parsable by pandas.to_datetime, e.g. '10/1/15')
-      - 'price' (electricity price per kWh)
-    """
-    try:
-        token = _get_token()
-        file_path = hf_hub_download(
-            repo_id=HF_DATASET_REPO,
-            filename=filename,
-            repo_type="dataset",
-            token=token,
-        )
-        df = pd.read_csv(file_path)
-
-        if "date" not in df.columns or "price" not in df.columns:
-            raise ValueError(f"{filename} must contain 'date' and 'price' columns.")
-
-        # Normalize date to python date objects
-        df["date"] = pd.to_datetime(df["date"]).dt.date
-        df = df[["date", "price"]].copy()
-        df = df.sort_values("date")
-        series = df.set_index("date")["price"].astype(float)
-        return series
-    except Exception as e:
-        print(f"⚠️ Could not load electricity data for {region} from {filename}: {e}")
-        return None
-
-
-# Load all regions at import time (one-time cost)
-for _region, _fname in REGION_FILES.items():
-    _ELECTRICITY_SERIES[_region] = _load_region_series(_region, _fname)
-
-
-def get_electricity_rate(region: str, d) -> float:
+def get_electricity_rate(region: str, day=None) -> float:
     """
-    Return the electricity rate (USD/kWh) for a given region and date.
+    For the real-time demo, we don't actually use this in feature construction
+    (we rely on user input). But some legacy code may still call it.
 
-    - If d is inside the CSV range, we use that day's price (or last available
-      before d, to handle gaps).
-    - If d is outside the CSV range or data is missing, we fall back to
-      BASE_ELECTRICITY_RATES[region].
+    We simply return a region-average from ELECTRICITY_RATES,
+    without reading any CSV or using HF_DATASET_TOKEN.
     """
-    if region not in BASE_ELECTRICITY_RATES:
-        raise ValueError(
-            f"Unknown region '{region}'. Expected one of {list(BASE_ELECTRICITY_RATES.keys())}"
-        )
-
-    # Normalise input date
-    if isinstance(d, pd.Timestamp):
-        d = d.date()
-    elif isinstance(d, str):
-        d = pd.to_datetime(d).date()
-    elif isinstance(d, Date):
-        pass  # already ok
-    else:
-        raise TypeError(
-            f"Unsupported date type {type(d)}; expected datetime.date, pandas.Timestamp, or str"
-        )
-
-    base_rate = BASE_ELECTRICITY_RATES[region]
-    series = _ELECTRICITY_SERIES.get(region)
-
-    if series is None or series.empty:
-        return base_rate
-
-    idx = series.index
-
-    # Outside known range → use base constant rate
-    if d < idx[0] or d > idx[-1]:
-        return base_rate
-
-    # Exact match
-    if d in series.index:
-        return float(series.loc[d])
-
-    # Otherwise, use the last available price before this date
-    prev = series.loc[:d]
-    if prev.empty:
-        return base_rate
-    return float(prev.iloc[-1])
+    return ELECTRICITY_RATES.get(region, 0.10)