import os import json import time import logging from pathlib import Path from typing import List, Dict, Optional from dataclasses import dataclass from fastapi.encoders import jsonable_encoder import fitz # PyMuPDF from sentence_transformers import SentenceTransformer from mlc_llm import MLCEngine logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) @dataclass class ProductSpec: name: str description: Optional[str] = None price: Optional[float] = None attributes: Dict[str, str] = None tables: List[Dict] = None def to_dict(self): return jsonable_encoder(self) class PDFProcessor: def __init__(self): self.emb_model = self._initialize_emb_model("all-MiniLM-L6-v2") self.llm = self._initialize_llm() self.output_dir = Path("./output") self.output_dir.mkdir(exist_ok=True) def _initialize_emb_model(self, model_name): try: return SentenceTransformer(f'sentence-transformers/{model_name}') except Exception as e: logger.warning(f"SentenceTransformer failed: {e}") from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained(f"sentence-transformers/{model_name}") model = AutoModel.from_pretrained(f"sentence-transformers/{model_name}") return model def _initialize_llm(self): """Initialize MLC LLM engine with optimized settings""" try: # return MLCEngine(model="HF://mlc-ai/Llama-3-8B-Instruct-q4f16_1-MLC") return MLCEngine(model="HF://mlc-ai/Llama-2-7B-q4f16_1-MLC") except Exception as e: logger.error(f"Failed to initialize MLC Engine: {e}") raise def process_pdf(self, pdf_path: str) -> Dict: """Main PDF processing pipeline""" start_time = time.time() try: doc = fitz.open(pdf_path) except Exception as e: logger.error(f"Failed to open PDF: {e}") raise RuntimeError("Cannot open PDF file.") from e text_blocks = [] tables = [] for page_num, page in enumerate(doc): blocks = self._extract_text_blocks(page) text_blocks.extend([b for b in blocks if len(b.strip()) >= 10]) tables.extend(self._extract_tables(page, page_num)) products = [] for idx, block in enumerate(text_blocks): product = self._process_text_block(block) if product and self._is_valid_product(product): product.tables = tables products.append(product.to_dict()) logger.info(f"Processed {len(products)} products in {time.time() - start_time:.2f}s") return {"products": products, "tables": tables} def _process_text_block(self, text: str) -> Optional[ProductSpec]: """Process text with MLC LLM using optimized prompt""" try: prompt = self._generate_query_prompt(text) response = self.llm.chat.completions.create( messages=[{"role": "user", "content": prompt}], stream=False ) return self._parse_response(response.choices[0].message.content) except Exception as e: logger.warning(f"Error processing text block: {e}") return None def _generate_query_prompt(self, text: str) -> str: """Generate structured prompt for better JSON response""" return f"""Extract product specifications as JSON from this text: Text: {text} Return valid JSON with exactly these keys: - name (string) - description (string, optional) - price (number, optional) - attributes (object with key-value pairs, optional) Example: {{ "name": "Example Product", "description": "High-quality example item", "price": 99.99, "attributes": {{"color": "red", "size": "XL"}} }}""" def _is_valid_product(self, product: ProductSpec) -> bool: """Validate extracted product data""" return any([ product.name, product.description, product.price, product.attributes ]) def _extract_text_blocks(self, page) -> List[str]: """Extract text blocks from a PDF page using PyMuPDF's blocks method.""" blocks = [] for block in page.get_text("blocks"): # block[4] contains the text content text = block[4].strip() if text: blocks.append(text) return blocks def _extract_tables(self, page, page_num: int) -> List[Dict]: """Extract tables from a PDF page using PyMuPDF's table extraction (if available).""" tables = [] try: tab = page.find_tables() if tab and hasattr(tab, 'tables') and tab.tables: for table in tab.tables: table_data = table.extract() if table_data: tables.append({ "page": page_num + 1, "cells": table_data, "header": table.header.names if table.header else [], "content": table_data }) except Exception as e: logger.warning(f"Error extracting tables from page {page_num + 1}: {e}") return tables def _parse_response(self, response: str) -> Optional[ProductSpec]: """Parse the LLM's response to extract a product specification.""" try: json_start = response.find('{') json_end = response.rfind('}') + 1 json_str = response[json_start:json_end].strip() if not json_str: raise ValueError("No JSON content found in response.") data = json.loads(json_str) # If the returned JSON is essentially empty, return None if all(not data.get(key) for key in ['name', 'description', 'price', 'attributes']): return None return ProductSpec( name=data.get('name', ''), description=data.get('description'), price=data.get('price'), attributes=data.get('attributes', {}) ) except (json.JSONDecodeError, KeyError, ValueError) as e: logger.warning(f"Parse error: {e} in response: {response}") return None def process_pdf_catalog(pdf_path: str): processor = PDFProcessor() try: result = processor.process_pdf(pdf_path) return result, "Processing completed successfully!" except Exception as e: logger.error(f"Processing failed: {e}") return {}, "Error processing PDF" if __name__ == "__main__": pdf_path = "path/to/your/pdf_file.pdf" result, message = process_pdf_catalog(pdf_path) print(json.dumps(result, indent=2), message)