| import pdfplumber |
| import os |
|
|
| INPUT_PDF = r"Data\English_CORE2000.pdf" |
| OUTPUT_TXT = r"Data\source_text.txt" |
|
|
| def extract_sentences_visually(): |
| all_sentences = [] |
| print(f"Opening {INPUT_PDF}...") |
| |
| with pdfplumber.open(INPUT_PDF) as pdf: |
| for i, page in enumerate(pdf.pages): |
| words = page.extract_words() |
| header_x = None |
| header_bottom = None |
| |
| for j, word in enumerate(words): |
| if word['text'] == 'Sample' and j+1 < len(words): |
| next_word = words[j+1] |
| if next_word['text'] == 'Sentence': |
| header_x = word['x0'] |
| header_bottom = word['bottom'] |
| break |
| |
| if header_x is not None: |
| crop_box = ( |
| header_x - 5, |
| header_bottom + 5, |
| page.width, |
| page.height |
| ) |
| |
| try: |
| cropped_page = page.crop(crop_box) |
| text_block = cropped_page.extract_text() |
| |
| if text_block: |
| lines = text_block.split('\n') |
| for line in lines: |
| clean_line = line.strip() |
| if len(clean_line) > 10: |
| all_sentences.append(clean_line) |
| except ValueError: |
| pass |
|
|
| if not all_sentences: |
| print("ERROR: Still found nothing.") |
| else: |
| with open(OUTPUT_TXT, "w", encoding="utf-8") as f: |
| f.write("\n".join(all_sentences)) |
| print(f"Success! Extracted {len(all_sentences)} sentences to {OUTPUT_TXT}") |
|
|
| def clean_source_text(file_path): |
| if not os.path.exists(file_path): |
| print(f"Error: {file_path} not found.") |
| return |
|
|
| with open(file_path, 'r', encoding='utf-8') as f: |
| lines = f.readlines() |
|
|
| filtered_lines = [] |
| removed_count = 0 |
| |
| for line in lines: |
| cleaned = line.replace('EnglishClass101.com', '') |
| stripped = cleaned.strip() |
| |
| if not stripped: |
| continue |
|
|
| word_count = len(stripped.split()) |
| |
| if word_count < 4: |
| removed_count += 1 |
| continue |
| |
| filtered_lines.append(stripped) |
|
|
| with open(file_path, 'w', encoding='utf-8') as f: |
| f.write('\n'.join(filtered_lines)) |
| |
| print("-" * 30) |
| print(f"CLEANING COMPLETE") |
| print(f"Original count: {len(lines)}") |
| print(f"Removed: {removed_count} sentences (2-3 words)") |
| print(f"Remaining: {len(filtered_lines)}") |
| print("-" * 30) |
|
|
| def analyze_lengths(file_path): |
| if not os.path.exists(file_path): |
| print(f"Error: {file_path} not found.") |
| return |
|
|
| counts = { |
| 2: 0, |
| 3: 0, |
| 4: 0, |
| 5: 0, |
| "over_5": 0 |
| } |
| |
| total_sentences = 0 |
|
|
| with open(file_path, 'r', encoding='utf-8') as f: |
| for line in f: |
| line = line.strip() |
| if not line: |
| continue |
| |
| word_count = len(line.split()) |
| total_sentences += 1 |
| |
| if word_count == 2: |
| counts[2] += 1 |
| elif word_count == 3: |
| counts[3] += 1 |
| elif word_count == 4: |
| counts[4] += 1 |
| elif word_count == 5: |
| counts[5] += 1 |
| elif word_count > 5: |
| counts["over_5"] += 1 |
|
|
| print(f"ANALYSIS RESULT") |
| print(f"Total Sentences: {total_sentences}") |
| print(f"2 words: {counts[2]}") |
| print(f"3 words: {counts[3]}") |
| print(f"4 words: {counts[4]}") |
| print(f"5 words: {counts[5]}") |
| print(f"> 5 words: {counts['over_5']}") |
| print("-" * 30) |
|
|
| if __name__ == "__main__": |
| extract_sentences_visually() |
| clean_source_text(OUTPUT_TXT) |
| analyze_lengths(OUTPUT_TXT) |
