import pickle
import re
import string
from nltk.corpus import stopwords
import nltk
import spacy

# Get the list of English stop words from NLTK
nltk_stop_words = stopwords.words('english')

# Load the spaCy model for English
nlp = spacy.load("en_core_web_sm")
def process_text(text):
    """
    Process text by:
    1. Lowercasing
    2. Removing punctuation and non-alphanumeric characters
    3. Removing stop words
    4. Lemmatization
    """
    # Step 1: Tokenization & Processing with spaCy
    doc = nlp(text.lower())  # Process text with spaCy

    # Step 2: Filter out stop words, non-alphanumeric characters, punctuation, and apply lemmatization
    processed_tokens = [
        re.sub(r'[^a-zA-Z0-9]', '', token.lemma_)  # Remove non-alphanumeric characters
        for token in doc 
        if token.text not in nltk_stop_words and token.text not in string.punctuation
    ]
    
    # Optional: Filter out empty strings resulting from the regex replacement
    processed_tokens = " ".join([word for word in processed_tokens if word])
    
    return processed_tokens

    
def predict(input_df: pd.DataFrame, tfidf_path: str, model_path: str, text_column: str = "quote"):
    """
    Predict the output using a saved TF-IDF vectorizer and Random Forest model.

    Parameters:
        input_df (pd.DataFrame): Input dataframe containing the text data.
        tfidf_path (str): Path to the saved TF-IDF vectorizer pickle file.
        model_path (str): Path to the saved Random Forest model pickle file.
        text_column (str): The name of the column in the dataframe containing the text data.

    Returns:
        pd.Series: Predictions for each row in the input dataframe.
    """
    # Load the TF-IDF vectorizer
    with open(tfidf_path, "rb") as tfidf_file:
        tfidf_vectorizer = pickle.load(tfidf_file)

    # Load the Random Forest model
    with open(model_path, "rb") as model_file:
        model = pickle.load(model_file)

    # Transform the input text using the TF-IDF vectorizer
    text_data = input_df.to_pandas()["quote"]
    text_features = tfidf_vectorizer.transform(text_data)

    # Make predictions using the loaded model
    predictions = model.predict(text_features)

    return predictions