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# annotations.py
import fitz # PyMuPDF
from typing import List, Dict, Any, Tuple
import language_tool_python
import io
def extract_pdf_text(file) -> str:
"""Extracts full text from a PDF file using PyMuPDF."""
try:
doc = fitz.open(stream=file.read(), filetype="pdf") if not isinstance(file, str) else fitz.open(file)
full_text = ""
for page in doc:
text = page.get_text("text")
full_text += text + "\n"
doc.close()
return full_text
except Exception as e:
print(f"Error extracting text from PDF: {e}")
return ""
def check_language_issues(full_text: str) -> Dict[str, Any]:
"""Check for language issues using LanguageTool."""
try:
language_tool = language_tool_python.LanguageTool('en-US')
matches = language_tool.check(full_text)
issues = []
for match in matches:
issues.append({
"message": match.message,
"context": match.context,
"suggestions": match.replacements[:3] if match.replacements else [],
"category": match.category,
"rule_id": match.ruleId,
"offset": match.offset,
"length": match.errorLength
})
return {
"total_issues": len(issues),
"issues": issues
}
except Exception as e:
print(f"Error checking language issues: {e}")
return {"error": str(e)}
def highlight_issues_in_pdf(file, language_matches: List[Dict[str, Any]]) -> bytes:
"""
Highlights language issues in the PDF and returns the annotated PDF as bytes.
This function maps LanguageTool matches to specific words in the PDF
and highlights those words.
"""
try:
# Open the PDF
doc = fitz.open(stream=file.read(), filetype="pdf") if not isinstance(file, str) else fitz.open(file)
# Extract words with positions from each page
word_list = [] # List of tuples: (page_number, word, x0, y0, x1, y1)
for page_number in range(len(doc)):
page = doc[page_number]
words = page.get_text("words") # List of tuples: (x0, y0, x1, y1, "word", block_no, line_no, word_no)
for w in words:
word_text = w[4]
word_list.append((page_number, word_text, w[0], w[1], w[2], w[3]))
# Concatenate all words to form the full text
concatenated_text = " ".join([w[1] for w in word_list])
# Iterate over each language issue
for issue in language_matches:
offset = issue["offset"]
length = issue["length"]
error_text = concatenated_text[offset:offset+length]
# Find the words that fall within the error span
current_pos = 0
target_words = []
for word in word_list:
word_text = word[1]
word_length = len(word_text) + 1 # +1 for the space
if current_pos + word_length > offset and current_pos < offset + length:
target_words.append(word)
current_pos += word_length
# Add highlight annotations to the target words
for target in target_words:
page_num, word_text, x0, y0, x1, y1 = target
page = doc[page_num]
# Define a rectangle around the word with some padding
rect = fitz.Rect(x0 - 1, y0 - 1, x1 + 1, y1 + 1)
# Add a highlight annotation
highlight = page.add_highlight_annot(rect)
highlight.set_colors(stroke=(1, 1, 0)) # Yellow color
highlight.update()
# Save annotated PDF to bytes
byte_stream = io.BytesIO()
doc.save(byte_stream)
annotated_pdf_bytes = byte_stream.getvalue()
doc.close()
# Save annotated PDF locally for verification
with open("annotated_temp.pdf", "wb") as f:
f.write(annotated_pdf_bytes)
print("Annotated PDF saved as 'annotated_temp.pdf' for manual verification.")
return annotated_pdf_bytes
except Exception as e:
print(f"Error in highlighting PDF: {e}")
return b""
def analyze_pdf(file) -> Tuple[Dict[str, Any], bytes]:
"""Analyzes the PDF for language issues and returns results and annotated PDF."""
try:
full_text = extract_pdf_text(file)
if not full_text:
return {"error": "Failed to extract text from PDF."}, None
language_issues = check_language_issues(full_text)
if "error" in language_issues:
return language_issues, None
issues = language_issues.get("issues", [])
annotated_pdf = highlight_issues_in_pdf(file, issues) if issues else None
return language_issues, annotated_pdf
except Exception as e:
return {"error": str(e)}, None |