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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 llama_cpp import Llama
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")
# Choose the appropriate model filename below; adjust if needed.
# self.llm = self._initialize_llm("deepseek-llm-7b-base.Q2_K.gguf")
self.llm = self._initialize_llm("llama-2-7b.Q2_K.gguf")
self.output_dir = Path("./output")
self.output_dir.mkdir(exist_ok=True)
def _initialize_emb_model(self, model_name):
try:
# Use SentenceTransformer if available
return SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
except Exception as e:
logger.warning(f"SentenceTransformer failed: {e}. Falling back to transformers model.")
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/" + model_name)
model = AutoModel.from_pretrained("sentence-transformers/" + model_name)
return model
def _initialize_llm(self, model_name):
"""Initialize LLM with automatic download if needed"""
# Here we use from_pretrained so that if the model is missing locally it downloads it.
model_path = os.path.join("models/", model_name)
if os.path.exists(model_path):
return Llama(
model_path=model_path,
n_ctx=1024,
n_gpu_layers=-1,
n_threads=os.cpu_count() - 1,
verbose=False
)
else:
return Llama.from_pretrained(
repo_id="Tien203/llama.cpp",
filename="Llama-2-7b-hf-q4_0.gguf",
)
def process_pdf(self, pdf_path: str) -> Dict:
"""Process PDF using PyMuPDF"""
start_time = time.time()
# Open PDF
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 = []
# Extract text and tables from each page
for page_num, page in enumerate(doc):
# Extract text blocks from page and filter out very short blocks (noise)
blocks = self._extract_text_blocks(page)
filtered = [block for block in blocks if len(block.strip()) >= 10]
logger.debug(f"Page {page_num + 1}: Extracted {len(blocks)} blocks, {len(filtered)} kept after filtering.")
text_blocks.extend(filtered)
# Extract tables (if any)
tables.extend(self._extract_tables(page, page_num))
# Process text blocks with LLM to extract product information
products = []
for idx, block in enumerate(text_blocks):
# Log the text block for debugging
logger.debug(f"Processing text block {idx}: {block[:100]}...")
product = self._process_text_block(block)
if product:
product.tables = tables
# Only add if at least one key (like name) is non-empty
if product.name or product.description or product.price or (
product.attributes and len(product.attributes) > 0):
products.append(product.to_dict())
else:
logger.debug(f"LLM returned empty product for block {idx}.")
else:
logger.debug(f"No product extracted from block {idx}.")
logger.info(f"Processed {len(products)} products in {time.time() - start_time:.2f}s")
return {"products": products, "tables": tables}
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 _process_text_block(self, text: str) -> Optional[ProductSpec]:
"""Process a text block with LLM to extract product specifications."""
prompt = self._generate_query_prompt(text)
logger.debug(f"Generated prompt: {prompt[:200]}...")
try:
response = self.llm.create_chat_completion(
messages=[{"role": "user", "content": prompt}],
temperature=0.1,
max_tokens=512
)
# Debug: log raw response
logger.debug(f"LLM raw response: {response}")
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 a prompt instructing the LLM to extract product information."""
return f"""Extract product specifications from the following text. If no product is found, return an empty JSON object with keys.\n\nText:\n{text}\n\nReturn JSON format exactly as:\n{{\n \"name\": \"product name\",\n \"description\": \"product description\",\n \"price\": numeric_price,\n \"attributes\": {{ \"key\": \"value\" }}\n}}"""
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__":
# Example usage: change this if you call process_pdf_catalog elsewhere
pdf_path = "path/to/your/pdf_file.pdf"
result, message = process_pdf_catalog(pdf_path)
print(result, message)
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