# annotations.py
# this is beta-1
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:
        # Open the PDF file
        doc = fitz.open(stream=file.read(), filetype="pdf") if not isinstance(file, str) else fitz.open(file)
        full_text = ""
        for page_num, page in enumerate(doc, start=1):
            text = page.get_text("text")
            full_text += text + "\n"
            print(f"Extracted text from page {page_num}: {len(text)} characters.")
        doc.close()
        print(f"Total extracted text length: {len(full_text)} characters.")
        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.strip(),
                "suggestions": match.replacements[:3] if match.replacements else [],
                "category": match.category,
                "rule_id": match.ruleId,
                "offset": match.offset,
                "length": match.errorLength
            })
        print(f"Total language issues found: {len(issues)}")
        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)
        print(f"Opened PDF with {len(doc)} pages.")

        # 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]
                # **Fix:** Insert a space before '[' to ensure "globally [2]" instead of "globally[2]"
                if '[' in word_text:
                    word_text = word_text.replace('[', ' [')
                word_list.append((page_number, word_text, w[0], w[1], w[2], w[3]))
        print(f"Total words extracted: {len(word_list)}")

        # Concatenate all words to form the full text
        concatenated_text = " ".join([w[1] for w in word_list])
        print(f"Concatenated text length: {len(concatenated_text)} characters.")

        # Iterate over each language issue
        for idx, issue in enumerate(language_matches, start=1):
            offset = issue["offset"]
            length = issue["length"]
            error_text = concatenated_text[offset:offset+length]
            print(f"\nIssue {idx}: '{error_text}' at offset {offset} with length {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

            if not target_words:
                print("No matching words found for this issue.")
                continue

            # 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()
                print(f"Highlighted '{word_text}' on page {page_num + 1} at position ({x0}, {y0}, {x1}, {y1})")

        # 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:
        # Reset file pointer before reading
        file.seek(0)
        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", [])
        # Reset file pointer before highlighting
        file.seek(0)
        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