import code

import pandas as pd
from sklearn.metrics import confusion_matrix, f1_score, precision_score, recall_score


def explore(df):
    print("The dataset is available as `df`. Press Ctrl-D or type exit() to quit.\n")
    code.interact(local={"df": df, "pd": pd}, banner="")


def dataset_summary(df, n_train, n_test):
    print("=" * 60)
    print("DATASET")
    print("=" * 60)
    counts = df["label"].value_counts()
    total = n_train + n_test
    print(f"\n  Total messages: {len(df)}")
    for label, n in counts.items():
        print(f"  {label:4s}: {n:5d}  ({100 * n / len(df):.1f}%)")
    print(f"\n  train: {n_train}  ({100 * n_train / total:.0f}%)   test: {n_test}  ({100 * n_test / total:.0f}%)")
    print()


def evaluation(y_true, y_pred, clf_name):
    print("=" * 60)
    print(f"RESULTS: {clf_name}")
    print("=" * 60)
    print()
    print(f"{'':12s} {'precision':>10} {'recall':>10} {'f1':>10}")
    for label in ["ham", "spam"]:
        p = precision_score(y_true, y_pred, pos_label=label, zero_division=0)
        r = recall_score(y_true, y_pred, pos_label=label, zero_division=0)
        f = f1_score(y_true, y_pred, pos_label=label, zero_division=0)
        print(f"  {label:<10} {p:>10.3f} {r:>10.3f} {f:>10.3f}")
    avg_f1 = f1_score(y_true, y_pred, average="macro", zero_division=0)
    print(f"\n  {'average f1':10} {'':>10} {'':>10} {avg_f1:>10.3f}")
    print()

    cm = confusion_matrix(y_true, y_pred, labels=["ham", "spam"])
    print("Confusion matrix:")
    print(f"{'':18s} {'pred ham':>10} {'pred spam':>10}")
    print(f"{'actual ham':18s} {cm[0][0]:>10} {cm[0][1]:>10}")
    print(f"{'actual spam':18s} {cm[1][0]:>10} {cm[1][1]:>10}")
    print()


def feature_weights(clf, top_n=10):
    if not hasattr(clf, "feature_weights"):
        return
    weights = clf.feature_weights(top_n=top_n)
    if not weights:
        return
    print("=" * 60)
    print(f"TOP {len(weights)} FEATURES BY WEIGHT")
    print("=" * 60)
    for name, w in weights:
        direction = "spam" if w > 0 else "ham "
        bar_len = min(int(abs(w) * 5), 25)
        bar = ("+" if w > 0 else "-") * bar_len
        print(f"  {name:<28} {w:+.3f}  → {direction}  {bar}")
    print()


def error_analysis(X, y_true, y_pred, n):
    errors = [
        (x, t, p)
        for x, t, p in zip(X, y_true, y_pred)
        if t != p
    ]
    shown = errors[:n]
    print("=" * 60)
    print(f"ERROR ANALYSIS ({len(shown)} of {len(errors)} misclassified)")
    print("=" * 60)
    for msg, true_label, pred_label in shown:
        display = msg if len(msg) <= 80 else msg[:77] + "..."
        print(f"\n  true={true_label:<4}  pred={pred_label}")
        print(f"  {display}")
    print()