app.py

import re
import fitz  # PyMuPDF
from pdfminer.high_level import extract_text
from pdfminer.layout import LAParams
import language_tool_python
from typing import List, Dict, Any, Tuple
from collections import Counter
import json
import traceback
import io
import tempfile
import os
import gradio as gr

# Set JAVA_HOME environment variable
os.environ['JAVA_HOME'] = '/usr/lib/jvm/java-11-openjdk-amd64'

# ------------------------------
# Analysis Functions
# ------------------------------

def extract_pdf_text_by_page(file) -> List[str]:
    """Extracts text from a PDF file, page by page, using PyMuPDF."""
    if isinstance(file, str):
        with fitz.open(file) as doc:
            return [page.get_text("text") for page in doc]
    else:
        with fitz.open(stream=file.read(), filetype="pdf") as doc:
            return [page.get_text("text") for page in doc]

def extract_pdf_text(file) -> str:
    """Extracts full text from a PDF file using PyMuPDF."""
    print("me llamo samyak")
    try:
        # Open the PDF file
        print("me llamo samyak")
        doc = fitz.open(stream=file.read(), filetype="pdf") if not isinstance(file, str) else fitz.open(file)
        full_text = ""
        print(doc)
        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.")
        print(full_text)
        return full_text
    except Exception as e:
        print(f"Error extracting text from PDF: {e}")
        return ""

def check_text_presence(full_text: str, search_terms: List[str]) -> Dict[str, bool]:
    """Checks for the presence of required terms in the text."""
    return {term: term.lower() in full_text.lower() for term in search_terms}

def label_authors(full_text: str) -> str:
    """Label authors in the text with 'Authors:' if not already labeled."""
    author_line_regex = r"^(?:.*\n)(.*?)(?:\n\n)"
    match = re.search(author_line_regex, full_text, re.MULTILINE)
    if match:
        authors = match.group(1).strip()
        return full_text.replace(authors, f"Authors: {authors}")
    return full_text

def check_metadata(full_text: str) -> Dict[str, Any]:
    """Check for metadata elements."""
    return {
        "author_email": bool(re.search(r'\b[\w.-]+?@\w+?\.\w+?\b', full_text)),
        "list_of_authors": bool(re.search(r'Authors?:', full_text, re.IGNORECASE)),
        "keywords_list": bool(re.search(r'Keywords?:', full_text, re.IGNORECASE)),
        "word_count": len(full_text.split()) or "Missing"
    }

def check_disclosures(full_text: str) -> Dict[str, bool]:
    """Check for disclosure statements."""
    search_terms = [
        "author contributions statement",
        "conflict of interest statement",
        "ethics statement",
        "funding statement",
        "data access statement"
    ]
    return check_text_presence(full_text, search_terms)

def check_figures_and_tables(full_text: str) -> Dict[str, bool]:
    """Check for figures and tables."""
    return {
        "figures_with_citations": bool(re.search(r'Figure \d+.*?citation', full_text, re.IGNORECASE)),
        "figures_legends": bool(re.search(r'Figure \d+.*?legend', full_text, re.IGNORECASE)),
        "tables_legends": bool(re.search(r'Table \d+.*?legend', full_text, re.IGNORECASE))
    }

def check_references(full_text: str) -> Dict[str, Any]:
    """Check for references."""
    return {
        "old_references": bool(re.search(r'\b19[0-9]{2}\b', full_text)),
        "citations_in_abstract": bool(re.search(r'\b(citation|reference)\b', full_text[:1000], re.IGNORECASE)),
        "reference_count": len(re.findall(r'\[.*?\]', full_text)),
        "self_citations": bool(re.search(r'Self-citation', full_text, re.IGNORECASE))
    }

def check_structure(full_text: str) -> Dict[str, bool]:
    """Check document structure."""
    return {
        "imrad_structure": all(section in full_text for section in ["Introduction", "Methods", "Results", "Discussion"]),
        "abstract_structure": "structured abstract" in full_text.lower()
    }

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 check_language(full_text: str) -> Dict[str, Any]:
    """Check language quality."""
    return {
        "plain_language": bool(re.search(r'plain language summary', full_text, re.IGNORECASE)),
        "readability_issues": False,  # Placeholder for future implementation
        "language_issues": check_language_issues(full_text)
    }

def check_figure_order(full_text: str) -> Dict[str, Any]:
    """Check if figures are referred to in sequential order."""
    figure_pattern = r'(?:Fig(?:ure)?\.?|Figure)\s*(\d+)'
    figure_references = re.findall(figure_pattern, full_text, re.IGNORECASE)
    figure_numbers = sorted(set(int(num) for num in figure_references))
    
    is_sequential = all(a + 1 == b for a, b in zip(figure_numbers, figure_numbers[1:]))
    
    if figure_numbers:
        expected_figures = set(range(1, max(figure_numbers) + 1))
        missing_figures = list(expected_figures - set(figure_numbers))
    else:
        missing_figures = None

    duplicates = [num for num, count in Counter(figure_references).items() if count > 1]
    duplicate_numbers = [int(num) for num in duplicates]
    not_mentioned = list(set(figure_references) - set(duplicates))
    
    return {
        "sequential_order": is_sequential,
        "figure_count": len(figure_numbers),
        "missing_figures": missing_figures,
        "figure_order": figure_numbers,
        "duplicate_references": duplicates,
        "not_mentioned": not_mentioned
    }

def check_reference_order(full_text: str) -> Dict[str, Any]:
    """Check if references in the main body text are in order."""
    reference_pattern = r'\[(\d+)\]'
    references = re.findall(reference_pattern, full_text)
    ref_numbers = [int(ref) for ref in references]
    
    max_ref = 0
    out_of_order = []
    for i, ref in enumerate(ref_numbers):
        if ref > max_ref + 1:
            out_of_order.append((i+1, ref))
        max_ref = max(max_ref, ref)
    
    all_refs = set(range(1, max_ref + 1))
    used_refs = set(ref_numbers)
    missing_refs = list(all_refs - used_refs)
    
    return {
        "max_reference": max_ref,
        "out_of_order": out_of_order,
        "missing_references": missing_refs,
        "is_ordered": len(out_of_order) == 0 and len(missing_refs) == 0
    }

def check_reference_style(full_text: str) -> Dict[str, Any]:
    """Check the reference style used in the paper and identify inconsistencies."""
    reference_section_match = re.search(r'References\b([\s\S]*?)(?:\n\S|\Z)', full_text, re.IGNORECASE)
    if not reference_section_match:
        return {"style": "Unknown", "reason": "References section not found", "inconsistent_refs": []}

    references_text = reference_section_match.group(1)
    reference_list = re.split(r'\n(?=\[\d+\]|\d+\.\s|\(\w+,\s*\d{4}\))', references_text)
    references = [ref.strip() for ref in reference_list if ref.strip()]

    styles = []
    inconsistent_refs = []
    patterns = {
        "IEEE": r'^\[\d+\]',
        "Harvard": r'^[A-Z][a-z]+,?\s[A-Z]\.\s\(?\d{4}\)?',
        "APA": r'^[A-Z][a-z]+,?\s[A-Z]\.\s\(?\d{4}\)?',
        "MLA": r'^[A-Z][a-z]+,\s[A-Z][a-z]+\.',
        "Vancouver": r'^\d+\.\s',
        "Chicago": r'^\d+\s[A-Z][a-z]+\s[A-Z]',
    }

    for i, ref in enumerate(references, 1):
        matched = False
        for style, pattern in patterns.items():
            if re.match(pattern, ref):
                styles.append(style)
                matched = True
                break
        if not matched:
            styles.append("Unknown")
            inconsistent_refs.append((i, ref, "Unknown"))

    if not styles:
        return {"style": "Unknown", "reason": "No references found", "inconsistent_refs": []}

    style_counts = Counter(styles)
    majority_style, majority_count = style_counts.most_common(1)[0]

    for i, style in enumerate(styles, 1):
        if style != majority_style and style != "Unknown":
            inconsistent_refs.append((i, references[i-1], style))

    consistency = majority_count / len(styles)

    return {
        "majority_style": majority_style,
        "inconsistent_refs": inconsistent_refs,
        "consistency": consistency
    }

# ------------------------------
# Annotation Functions
# ------------------------------

def highlight_text(page, words, text, annotation):
    """Highlight text and add annotation."""
    text_instances = find_text_instances(words, text)
    highlighted = False
    for inst in text_instances:
        highlight = page.add_highlight_annot(inst)
        highlight.update()
        comment = page.add_text_annot(inst[:2], annotation)
        comment.update()
        highlighted = True
    return highlighted

def find_text_instances(words, text):
    """Find all instances of text in words."""
    text_lower = text.lower()
    text_words = text_lower.split()
    instances = []
    for i in range(len(words) - len(text_words) + 1):
        if all(words[i+j][4].lower() == text_words[j] for j in range(len(text_words))):
            inst = fitz.Rect(words[i][:4])
            for j in range(1, len(text_words)):
                inst = inst | fitz.Rect(words[i+j][:4])
            instances.append(inst)
    return instances

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""
# ------------------------------
# Main Analysis Function
# ------------------------------

def analyze_pdf(filepath: str) -> Tuple[Dict[str, Any], bytes]:
    """Analyzes the PDF for language issues and returns results and annotated PDF."""
    try:
        full_text = extract_pdf_text(filepath)
        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(filepath, issues) if issues else None
        return language_issues, annotated_pdf
    except Exception as e:
        return {"error": str(e)}, None

# ------------------------------
# Gradio Interface
# ------------------------------

def process_upload(file):
    """
    Process the uploaded PDF file and return analysis results and annotated PDF.
    """
    # print(file.name)
    if file is None:
        return json.dumps({"error": "No file uploaded"}, indent=2), None

    # # Create a temporary file to work with
    
    # with tempfile.NamedTemporaryFile(delete=False, suffix='.pdf') as temp_input:
    #     temp_input.write(file)
    #     temp_input_path = temp_input.name
    #     print(temp_input_path)
    
    temp_input = tempfile.NamedTemporaryFile(delete=False, suffix='.pdf')
    temp_input.write(file)
    temp_input_path = temp_input.name
    print(temp_input_path)
    # Analyze -inputthe PDF
    
    results, annotated_pdf = analyze_pdf(temp_input_path)
    
    print(results)
    results_json = json.dumps(results, indent=2)

    # Clean up the temporary input file
    os.unlink(temp_input_path)

    # If we have an annotated PDF, save it temporarily
    if annotated_pdf:
        with tempfile.NamedTemporaryFile(delete=False, suffix='.pdf') as tmp_file:
            tmp_file.write(annotated_pdf)
            return results_json, tmp_file.name

    return results_json, None
        
    # except Exception as e:
    #     error_message = json.dumps({
    #         "error": str(e),
    #         "traceback": traceback.format_exc()
    #     }, indent=2)
    #     return error_message, None
    
    
def create_interface():
    with gr.Blocks(title="PDF Analyzer") as interface:
        gr.Markdown("# PDF Analyzer")
        gr.Markdown("Upload a PDF document to analyze its structure, references, language, and more.")
        
        with gr.Row():
            file_input = gr.File(
                label="Upload PDF",
                file_types=[".pdf"],
                type="binary"
            )
        
        with gr.Row():
            analyze_btn = gr.Button("Analyze PDF")
        
        with gr.Row():
            results_output = gr.JSON(
                label="Analysis Results",
                show_label=True
            )
        
        with gr.Row():
            pdf_output = gr.File(
                label="Annotated PDF",
                show_label=True
            )
        
        analyze_btn.click(
            fn=process_upload,
            inputs=[file_input],
            outputs=[results_output, pdf_output]
        )
    
    return interface

if __name__ == "__main__":
    interface = create_interface()
    interface.launch(
        share=True,  # Set to False in production
        # server_name="0.0.0.0",
        server_port=None
    )