from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, mean_squared_error, mean_absolute_error
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
from sklearn.linear_model import LogisticRegression, LinearRegression
from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor
from sklearn.svm import SVC, SVR
from xgboost import XGBClassifier, XGBRegressor
from lightgbm import LGBMClassifier, LGBMRegressor
from utils import preprocess_data
import numpy as np
import pandas as pd

def run_automl(df, target_col):
    try:
        X, y, summary, problem_type = preprocess_data(df, target_col)
    except Exception as e:
        return {"error": f"Preprocessing failed: {str(e)}"}

    # ✅ Fix: Convert y to Pandas Series for .nunique()
    if pd.Series(y).nunique() <= 1:
        return {"error": "Target must have more than one unique value."}

    try:
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    except Exception as e:
        return {"error": f"Data splitting failed: {str(e)}"}

    if problem_type == "classification":
        models = {
            "Logistic Regression": LogisticRegression(max_iter=1000),
            "Random Forest": RandomForestClassifier(),
            "SVM": SVC(),
            "KNN": KNeighborsClassifier(),
            "XGBoost": XGBClassifier(use_label_encoder=False, eval_metric='mlogloss'),
            "LightGBM": LGBMClassifier()
        }
    else:
        models = {
            "Linear Regression": LinearRegression(),
            "Random Forest Regressor": RandomForestRegressor(),
            "SVR": SVR(),
            "KNN Regressor": KNeighborsRegressor(),
            "XGBoost Regressor": XGBRegressor(),
            "LightGBM Regressor": LGBMRegressor()
        }

    scores = {}
    best_model = None
    best_score = -999999
    best_name = ""
    predictions = pd.DataFrame()

    for name, model in models.items():
        try:
            model.fit(X_train, y_train)
            preds = model.predict(X_test)

            if problem_type == "classification":
                score = accuracy_score(y_test, preds) * 100
            else:
                score = -mean_squared_error(y_test, preds)  # Lower is better

            scores[name] = round(score, 2)

            if score > best_score:
                best_score = score
                best_model = model
                best_name = name
                predictions = pd.DataFrame({"Actual": y_test, "Predicted": preds})
        except Exception as e:
            scores[name] = 0.0
            print(f"⚠️ {name} failed: {e}")

    if best_model is None:
        return {"error": "No model trained successfully."}

    result = {
        "type": problem_type,
        "preprocessing": summary,
        "model_scores": scores,
        "best_model": best_name,
        "best_accuracy": round(-best_score if problem_type == "regression" else best_score, 2),
        "model_object": best_model,
        "predictions": predictions
    }

    if problem_type == "classification":
        preds = best_model.predict(X_test)
        result["confusion_matrix"] = confusion_matrix(y_test, preds).tolist()
        result["classification_report"] = classification_report(y_test, preds, output_dict=True)
    else:
        preds = best_model.predict(X_test)
        result["regression_report"] = {
            "RMSE": round(np.sqrt(mean_squared_error(y_test, preds)), 2),
            "MAE": round(mean_absolute_error(y_test, preds), 2)
        }

    return result