import pandas as pd
import joblib

from evidently.report import Report
from evidently.metrics import ColumnDriftMetric

# Chargement des données

pipe = joblib.load("./BestModel/pipeline_complet.joblib")
df_example = joblib.load("./data/app_test_clean_v2.joblib")

expected_cols = pipe.feature_names_in_
df_reference = df_example[expected_cols].copy()
df_production = pd.read_parquet("logs/predictions_log.parquet")

# Colonnes communes
df_reference.columns = df_reference.columns.astype(str)
df_production.columns = df_production.columns.astype(str)

common_cols = sorted(set(df_reference.columns).intersection(df_production.columns))
df_reference = df_reference[common_cols].copy()
df_production = df_production[common_cols].copy()

# Harmonisation des types
for col in common_cols:
    # bool -> int
    if df_reference[col].dtype == bool:
        df_reference[col] = df_reference[col].astype(int)

    if df_production[col].dtype == bool:
        df_production[col] = df_production[col].astype(int)

    ref_num = pd.api.types.is_numeric_dtype(df_reference[col])
    prod_num = pd.api.types.is_numeric_dtype(df_production[col])

    # Les deux numériques
    if ref_num and prod_num:
        df_reference[col] = (pd.to_numeric(df_reference[col], errors="coerce").astype("float64"))
        df_production[col] = (pd.to_numeric(df_production[col], errors="coerce").astype("float64"))

    # Sinon tout en string
    else:
        df_reference[col] = (df_reference[col].fillna("MISSING").astype(str))
        df_production[col] = (df_production[col].fillna("MISSING").astype(str))

# Suppression colonnes vides
valid_cols = []
for col in common_cols:
    if (df_reference[col].notna().sum() > 0 and df_production[col].notna().sum() > 0):
        valid_cols.append(col)
df_reference = df_reference[valid_cols]
df_production = df_production[valid_cols]

# Suppression colonnes constantes
non_constant_cols = []
for col in valid_cols:
    if (df_reference[col].nunique(dropna=True) > 1 and df_production[col].nunique(dropna=True) > 1):
        non_constant_cols.append(col)
df_reference = df_reference[non_constant_cols]
df_production = df_production[non_constant_cols]

print(f"\nNombre de colonnes testées : {len(non_constant_cols)}")

# Test colonne par colonne
for col in non_constant_cols:
    print(f"\nTest : {col}")
    try:
        report = Report(metrics = [ColumnDriftMetric(column_name = col)])
        report.run(reference_data = df_reference[[col]], current_data = df_production[[col]])
        print("OK")
    except Exception as e:
        print("\n-------------------------------------")
        print(f"COLONNE EN ERREUR : {col}")
        print("-------------------------------------")

        print("\nType référence :")
        print(df_reference[col].dtype)

        print("\nType production :")
        print(df_production[col].dtype)

        print("\nTypes Python REF :")
        print(set(df_reference[col].dropna().map(type)))
        print("\nTypes Python PROD :")
        print(set(df_production[col].dropna().map(type)))

        print("\nException :")
        print(e)

        break

print(df_reference.shape)
print(df_production.shape)

print(set(df_production.columns) - set(df_reference.columns))
print(set(df_reference.columns) - set(df_production.columns))

print("\nFin du diagnostic.")