Spaces:
Running
Running
File size: 2,505 Bytes
6f5a0e0 | 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 | import pandas as pd
from .config import all_training_fames, main_fames, minor_fames, fame_groups
def _prompt_fame_group(fames: list, highlight: set) -> dict:
"""Prompt the user for FAME values in a group; Enter accepts 0."""
data = {}
for fame in fames:
marker = " *" if fame in highlight else " "
raw = input(f"{marker}{fame} [0]: ").strip()
try:
data[fame] = float(raw) if raw else 0.0
except ValueError:
print(f" Invalid value — using 0.")
data[fame] = 0.0
return data
def _get_single_sample() -> pd.DataFrame:
print("\n" + "-" * 70)
print("Enter FAME composition (% by weight). Press Enter to use 0.")
print(" * marks FAMEs most commonly used by the model.")
print("-" * 70)
main_set = set(main_fames)
data = {}
for group_name, fames in fame_groups.items():
print(f"\n [{group_name}]")
data.update(_prompt_fame_group(fames, main_set))
total = sum(data.values())
print(f"\n Total entered: {total:.2f}%")
if total == 0:
print(" Warning: all values are 0 — this sample will be rejected as invalid.")
return pd.DataFrame([data])
def _get_csv() -> pd.DataFrame:
print("\n" + "-" * 70)
print("CSV format: one row per sample, columns named by FAME (e.g. C18:1).")
print("Missing FAME columns are filled with 0 automatically.")
print("-" * 70)
while True:
path = input("\nEnter path to CSV file: ").strip()
try:
df = pd.read_csv(path)
print(f" Loaded {len(df)} sample(s) from '{path}'")
return df
except FileNotFoundError:
print(f" File not found: '{path}'. Please try again.")
except Exception as e:
print(f" Error loading file: {e}")
def get_user_config() -> dict:
"""Collect prediction mode and input data from the user."""
print("\n" + "=" * 70)
print("BIODIESEL CN PREDICTOR — Configuration")
print("=" * 70)
mode = input("\nSelect prediction mode (1: Single sample, 2: CSV batch): ").strip()
while mode not in {"1", "2"}:
print("Invalid selection. Please choose 1 or 2.")
mode = input("Select prediction mode (1: Single sample, 2: CSV batch): ").strip()
if mode == "1":
return {"mode": "single", "df": _get_single_sample()}
else:
return {"mode": "csv", "df": _get_csv()}
|