Spaces:
Sleeping
Sleeping
File size: 7,050 Bytes
535dd76 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 | """
PDF Parser — Entry point for every paper analysis.
Every analysis we do depends on clean text extraction.
If this is wrong, everything downstream is wrong.
SciPeerAI v2.3.1 — Built by Sameer Nadeem
Table extraction + DOI harvesting added.
"""
import re
import hashlib
import fitz
from dataclasses import dataclass
from pathlib import Path
MAX_FILE_SIZE_MB = 50
MAX_PAGES = 300
ALLOWED_MIME_HEADER = b"%PDF"
@dataclass
class ParsedPaper:
title: str
full_text: str
sections: dict
page_count: int
has_figures: bool
figure_count: int
metadata: dict
tables_text: str = ""
dois_found: list = None
def __post_init__(self):
if self.dois_found is None:
self.dois_found = []
class PDFParser:
"""
Handles PDF ingestion and structured text extraction.
v2.3.1 upgrades:
- Table extraction via PyMuPDF blocks
- Structured number extraction for GRIM/SPRITE/P-Curve
- DOI harvesting from full document
- Section-aware extraction
"""
def __init__(self):
self._section_markers = [
"abstract", "introduction", "methods", "methodology",
"results", "discussion", "conclusion", "references",
"related work", "background", "experiments",
"materials and methods", "statistical analysis",
"data analysis", "findings", "participants",
]
self._doi_pattern = re.compile(
r'10\.\d{4,9}/[^\s\],;"\'<>\|\{\}\\]+',
re.IGNORECASE
)
def parse(self, pdf_path: str) -> ParsedPaper:
pdf_path = Path(pdf_path)
if not pdf_path.exists():
raise FileNotFoundError(f"Paper not found: {pdf_path}")
if pdf_path.suffix.lower() != ".pdf":
raise ValueError(f"Expected PDF file, got: {pdf_path.suffix}")
raw_bytes = pdf_path.read_bytes()
return self.parse_bytes(raw_bytes, filename=pdf_path.name)
def parse_bytes(self, file_bytes: bytes, filename: str = "upload.pdf") -> ParsedPaper:
filename = self._sanitize_filename(filename)
self._validate_bytes(file_bytes, filename)
doc = fitz.open(stream=file_bytes, filetype="pdf")
if len(doc) > MAX_PAGES:
doc.close()
raise ValueError(
f"Paper has {len(doc)} pages. Maximum allowed is {MAX_PAGES}."
)
full_text = self._extract_text(doc)
tables_text = self._extract_tables(doc)
combined = full_text + "\n\n" + tables_text
sections = self._split_into_sections(combined)
figure_count = self._count_figures(doc)
title = self._extract_title(doc, full_text)
page_count = len(doc)
dois_found = self._extract_dois(combined)
doc.close()
return ParsedPaper(
title = title,
full_text = combined,
sections = sections,
page_count = page_count,
has_figures = figure_count > 0,
figure_count = figure_count,
dois_found = dois_found,
tables_text = tables_text,
metadata = {
"filename": filename,
"file_size_kb": round(len(file_bytes) / 1024, 2),
"sha256": hashlib.sha256(file_bytes).hexdigest(),
},
)
def _validate_bytes(self, file_bytes: bytes, filename: str) -> None:
if len(file_bytes) == 0:
raise ValueError("Uploaded file is empty.")
max_bytes = MAX_FILE_SIZE_MB * 1024 * 1024
if len(file_bytes) > max_bytes:
size_mb = round(len(file_bytes) / 1024 / 1024, 1)
raise ValueError(
f"File too large: {size_mb} MB. Maximum: {MAX_FILE_SIZE_MB} MB."
)
if not file_bytes.startswith(ALLOWED_MIME_HEADER):
raise ValueError("Invalid file. Only real PDF files are accepted.")
@staticmethod
def _sanitize_filename(filename: str) -> str:
name = Path(filename).name
if not name.lower().endswith(".pdf"):
raise ValueError(f"Expected a PDF filename, got: {filename}")
return name
def _extract_text(self, doc: fitz.Document) -> str:
pages = []
for page in doc:
pages.append(page.get_text("text"))
return "\n".join(pages)
def _extract_tables(self, doc: fitz.Document) -> str:
"""
Extract structured table content from PDF using block-level parsing.
This is critical for GRIM/SPRITE/P-Curve modules to find
means, SDs, sample sizes, and p-values that live inside tables.
"""
table_parts = []
for page_num, page in enumerate(doc):
blocks = page.get_text("blocks")
rows = []
for block in blocks:
if block[6] != 0:
continue
text = block[4].strip()
if not text:
continue
rows.append((block[1], text))
rows.sort(key=lambda x: x[0])
for _, text in rows:
if any(char.isdigit() for char in text):
clean = " ".join(text.split())
table_parts.append(clean)
if not table_parts:
return ""
return "\nTABLE_DATA:\n" + "\n".join(table_parts)
def _split_into_sections(self, text: str) -> dict:
sections = {}
text_lower = text.lower()
for i, marker in enumerate(self._section_markers):
start_idx = text_lower.find(marker)
if start_idx == -1:
continue
end_idx = len(text)
for next_marker in self._section_markers[i + 1:]:
next_idx = text_lower.find(next_marker, start_idx + 1)
if next_idx != -1:
end_idx = next_idx
break
sections[marker] = text[start_idx:end_idx].strip()
return sections
def _count_figures(self, doc: fitz.Document) -> int:
total = 0
for page in doc:
total += len(page.get_images())
return total
def _extract_title(self, doc: fitz.Document, full_text: str) -> str:
meta = doc.metadata
if meta and meta.get("title"):
return meta["title"].strip()
for line in full_text.split("\n"):
line = line.strip()
if len(line) > 10:
return line
return "Unknown Title"
def _extract_dois(self, text: str) -> list:
"""
Extract all DOIs from full document text including tables and headers.
Uses broad pattern to catch DOIs in any format.
"""
dois = []
for m in self._doi_pattern.finditer(text):
doi = m.group(0).rstrip('.,;)')
doi_clean = doi.lower()
if doi_clean not in dois:
dois.append(doi_clean)
return dois[:50] |