#!/usr/bin/env python3
"""
Example usage of screenplay salience features from Hugging Face.
"""

from datasets import load_dataset
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report

# ============================================================================
# Method 1: Load with datasets library
# ============================================================================

print("Loading training data...")
ds = load_dataset(
    "YOUR_USERNAME/screenplay-features",
    data_files={
        "train": ["train/base.parquet", "train/gc_polarity.parquet"],
        "test": ["test/base.parquet", "test/gc_polarity.parquet"]
    }
)

train_df = ds['train'].to_pandas()
test_df = ds['test'].to_pandas()

# Separate features and labels
feature_cols = [c for c in train_df.columns if c not in ["movie_id", "scene_index", "label"]]

X_train = train_df[feature_cols]
y_train = train_df["label"]

X_test = test_df[feature_cols]
y_test = test_df["label"]

print(f"Train: {len(X_train)} samples, {len(feature_cols)} features")
print(f"Test: {len(X_test)} samples")

# ============================================================================
# Method 2: Use custom loader (easier)
# ============================================================================

# If you have the feature_cache module:
# from feature_cache.load_hf import load_groups
#
# X_train, y_train = load_groups(
#     groups=["base", "gc_polarity", "emotional"],
#     split="train",
#     hf_repo="YOUR_USERNAME/screenplay-features"
# )
#
# X_test, y_test = load_groups(
#     groups=["base", "gc_polarity", "emotional"],
#     split="test",
#     hf_repo="YOUR_USERNAME/screenplay-features"
# )

# ============================================================================
# Train a simple model
# ============================================================================

print("\nTraining logistic regression...")
clf = LogisticRegression(max_iter=1000, random_state=42)
clf.fit(X_train.fillna(0), y_train)

# Evaluate
y_pred = clf.predict(X_test.fillna(0))
print("\nTest Results:")
print(classification_report(y_test, y_pred, target_names=["Non-salient", "Salient"]))

# ============================================================================
# Explore features
# ============================================================================

print("\nTop 10 features by coefficient:")
feature_importance = pd.DataFrame({
    'feature': feature_cols,
    'coefficient': clf.coef_[0]
}).sort_values('coefficient', key=abs, ascending=False)

print(feature_importance.head(10))