content_analyzer.py

import os
from typing import Tuple

# Potential libraries for text extraction and classification
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import make_pipeline
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report

class ContentAnalyzer:
    def __init__(self):
        # Initialize the model pipeline
        self.model = make_pipeline(TfidfVectorizer(), MultinomialNB())
        
    def train_model(self, data, target):
        # Split data into training and testing sets
        X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.2, random_state=42)
        # Train the model
        self.model.fit(X_train, y_train)
        # Evaluate the model
        predictions = self.model.predict(X_test)
        print(classification_report(y_test, predictions))
        
    def classify_content(self, text: str) -> str:
        # Predict the category of the given text
        return self.model.predict([text])[0]
        
    def extract_text(self, file_path: str) -> str:
        # Placeholder method for extracting text from various file types
        # This will need to be expanded to handle different file formats
        return "Extracted text from the file."
        
def main():
    # Example usage of the ContentAnalyzer class
    analyzer = ContentAnalyzer()
    # Example: Training the model with dummy data
    # This section will need to be replaced with actual data loading and preprocessing
    data = ["Text from document 1", "Text from document 2"]
    target = ["Category1", "Category2"]
    analyzer.train_model(data, target)
    
    # Example classification
    example_text = "An example text that needs classification."
    category = analyzer.classify_content(example_text)
    print(f"The category of the text: {category}")
    
if __name__ == "__main__":
    main()