Text summarizer produces second half of exampe text rather than complete text summary in 2 sentences plus keywords. Where does the summary fail?Python

App.py runs and produces the spinner feature. However, it outputs the second half of the example wiki text file instead of producing a simple summary generated by the model.py script. App.py and model.py are two separate files. The intention was to produce a language model that uses statistics to produce a results summary. Where is the error?

# -------------------- App.py --------------------

import argparse # Input analysis (parsing) processed with grammar.
import sys # System exit.
import os # File path.
import time # Timestamping.

# Import (AI text summarization and load text file) functions from called upon modules.
from .model import summarize_text
from .utils import load_text

# Spinner animation for longer operations. Heighten user experience, (UX).
def spinner():
  # Function to cycle through characters.
  while True:
    for frame in "|/-\\":
      yield frame

# Define main CLI function as script runs. 
def main():
  # Initialize parser (analysis) with description.
  parser = argparse.ArgumentParser(
    description="AI-powered text summarizer CLI tool"
  )
  # Input the text file we want to consolidate.
  parser.add_argument(
    "input_file",
    type=str,
    help="Path to the text file you want to summarize"
  )
  # Output text file naming.
  parser.add_argument(
    "-o", "--output",
    type=str,
    help="Optional output file to save the summary"
  )
  # Allow for plenty of, (verbose), documentation of each step of process (ideal for teaching and auditing).
  parser.add_argument(
    "-v", "--verbose",
    action="store_true",
    help="Enable verbose mode for detailed logs"
  )

  # Analyse the user argument.
  args = parser.parse_args()

  # Qualify input file.
  if not os.path.exists(args.input_file):
    print(f"Error: File not found: {args.input_file}")
    sys.exit(1)

  # Load and read text file.
  if args.verbose:
    print(f"[INFO] Loading file: {args.input_file}")
  text = load_text(args.input_file)
  
  # Prevent empty file summarization.
  if not text.strip():
    print("Error: File is empty. Cannot summarize an empty text file.")
    sys.exit(1)

  # Show summarization is rendering
  if args.verbose:
    print("Generating summary...")
  
  # Display spinner.
  spin = spinner()
  for _ in range(10):
    sys.stdout.write(next(spin))
    sys.stdout.flush()
    time.sleep(0.05)
    sys.stdout.write("\b")

  # Executive AI summarization.
  summary = summarize_text(text)
  
  # Print summary to console.
  print("\n=== SUMMARY RESULT ===\n")
  print(summary)

    # Save output to file if requested.
  if args.output:
    with open(args.output, "w", encoding="utf-8") as f:
      f.write(summary)

    print(f"\n Summary saved to: {args.output}")


# Direct execution only.
if __name__ == "__main__":
    main()

# ------------------- Model.py -------------------

import re
from collections import Counter

def summarize_text(text):
    """
    Lightweight extractive summarizer:
    - Extracts sentences
    - Computes keyword frequency
    - Selects best 1–2 sentences
    - Produces a clean, grammatical summary
    """

    # --- 1. Split text into sentences ---
    sentences = [s.strip() for s in re.split(r'(?<=[.!?])\s+', text) if s.strip()]

    if not sentences:
        return "No usable sentences found in the text."

    # --- 2. Count word frequencies ---
    words = re.findall(r'\b\w+\b', text.lower())
    word_counts = Counter(words)

    stopwords = {
        "the", "and", "of", "in", "to", "a", "is", "for", "on", "with",
        "as", "by", "an", "at", "that", "this", "it", "from", "be", "are"
    }

    keywords = [w for w, _ in word_counts.most_common(20) if w not in stopwords]

    if not keywords:
        keywords = list(word_counts.keys())[:10]

    # --- 3. Score each sentence ---
    def sentence_score(sentence):
        s_words = re.findall(r'\b\w+\b', sentence.lower())
        return sum(word_counts.get(w, 0) for w in s_words if w in keywords)

    ranked = sorted(sentences, key=sentence_score, reverse=True)

    # --- 4. Take top 1–2 sentences ---
    best_sentences = ranked[:2]
    summary = " ".join(best_sentences)

    # --- 5. Fix spacing, ensure full sentences ---
    summary = summary.replace("  ", " ").strip()
    if not summary.endswith((".", "!", "?")):
        summary += "."

    # --- 6. Include missing core keywords in English form ---
    top_keywords = keywords[:5]
    missing = [k for k in top_keywords if k not in summary.lower()]

    if missing:
        if len(missing) == 1:
            summary += f" Key concept emphasized: {missing[0]}."
        else:
            summary += (
                " Key concepts emphasized: "
                + ", ".join(missing[:-1])
                + f", and {missing[-1]}."
            )

    return summary

Thank you for your answers.