548 lines
21 KiB
Python
548 lines
21 KiB
Python
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import base64
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import logging
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import os
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from datetime import datetime
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from typing import Any, Dict
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import aiofiles
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import groq
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import PyPDF2
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from config import settings
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logger = logging.getLogger(__name__)
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class DocumentProcessor:
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def __init__(self):
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self.client = groq.Groq(api_key=settings.GROQ_API_KEY)
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self.model = "meta-llama/llama-4-scout-17b-16e-instruct" # Vision model
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async def process_file(self, file_path: str, file_type: str) -> Dict[str, Any]:
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"""Process uploaded file and extract receipt data"""
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try:
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if file_type.lower() in ["jpg", "jpeg", "png", "gif", "bmp"]:
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return await self._process_image(file_path)
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elif file_type.lower() == "pdf":
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return await self._process_pdf(file_path)
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else:
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raise ValueError(f"Unsupported file type: {file_type}")
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except Exception as e:
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return {"error": str(e)}
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async def _process_image(self, image_path: str) -> Dict[str, Any]:
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"""Extract data from image using Groq vision"""
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try:
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# Encode image to base64
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base64_image = self._encode_image(image_path)
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# Create Groq vision prompt
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prompt = """
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Analyze this receipt image and extract the following information in JSON format:
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{
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"vendor": "Store/company name",
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"description": "Detailed description of items/services purchased",
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"total_amount": 0.00,
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"tax_amount": 0.00,
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"date": "YYYY-MM-DD",
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"category": "Food/Transport/Office/Other",
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"confidence": 0.95,
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"currency": "USD"
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}
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Rules:
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- Extract vendor name as it appears on receipt
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- Extract description of items/services purchased (e.g., "Coffee and sandwich", "Gasoline", "Office supplies")
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- Total amount should be the final total including tax
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- Tax amount is separate tax line if available
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- Date should be the date on the receipt
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- Categorize based on vendor type (Starbucks=Food, Shell=Transport, etc.)
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- Confidence score 0-1 based on how clear the receipt is
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- Currency should be the currency used on the receipt (e.g., "USD", "EUR")
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Return only valid JSON.
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"""
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# Call Groq vision API with correct format
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response = self.client.chat.completions.create(
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messages=[
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{
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"role": "user",
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"content": [
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{"type": "text", "text": prompt},
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{
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"type": "image_url",
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"image_url": {
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"url": f"data:image/jpeg;base64,{base64_image}",
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},
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},
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],
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}
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],
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model=self.model,
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max_tokens=500,
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temperature=0.1,
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)
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# Parse response
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result_text = response.choices[0].message.content.strip()
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return self._parse_extraction_result(result_text)
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except Exception as e:
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return {"error": f"Image processing error: {str(e)}"}
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def _encode_image(self, image_path: str) -> str:
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"""Encode image to base64 string"""
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with open(image_path, "rb") as image_file:
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return base64.b64encode(image_file.read()).decode("utf-8")
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async def _process_pdf(self, pdf_path: str) -> Dict[str, Any]:
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"""Extract data from PDF by converting to image first"""
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try:
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# For now, extract text from PDF and process as text
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text_content = self._extract_text_from_pdf(pdf_path)
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return self._process_text_content(text_content)
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except Exception as e:
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return {"error": f"PDF processing error: {str(e)}"}
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def _extract_text_from_pdf(self, pdf_path: str) -> str:
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"""Extract text from PDF"""
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try:
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with open(pdf_path, "rb") as file:
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pdf_reader = PyPDF2.PdfReader(file)
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text = ""
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for page in pdf_reader.pages:
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text += page.extract_text() + "\n"
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return text
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except Exception:
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return ""
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def _process_text_content(self, text_content: str) -> Dict[str, Any]:
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"""Process text content using Groq (fallback for PDFs)"""
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try:
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prompt = f"""
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Analyze this receipt text and extract the following information in JSON format:
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Receipt Text:
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{text_content}
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Extract:
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{{
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"vendor": "Store/company name",
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"description": "Detailed description of items/services purchased",
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"total_amount": 0.00,
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"tax_amount": 0.00,
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"date": "YYYY-MM-DD",
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"category": "Food/Transport/Office/Other",
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"confidence": 0.95,
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"currency": "USD"
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}}
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Rules:
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- Extract vendor name as it appears on receipt
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- Extract description of items/services purchased (e.g., "Coffee and sandwich", "Gasoline", "Office supplies")
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- Total amount should be the final total including tax
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- Tax amount is separate tax line if available
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- Date should be the date on the receipt
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- Categorize based on vendor type
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- Confidence score 0-1 based on clarity
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- Currency should be the currency used on the receipt (e.g., "USD", "EUR")
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Return only valid JSON.
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"""
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response = self.client.chat.completions.create(
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model=self.model,
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messages=[{"role": "user", "content": prompt}],
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max_tokens=500,
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temperature=0.1,
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)
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result_text = response.choices[0].message.content.strip()
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return self._parse_extraction_result(result_text)
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except Exception as e:
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return {"error": f"Text processing error: {str(e)}"}
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def _parse_extraction_result(self, result_text: str) -> Dict[str, Any]:
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"""Parse Groq response and extract JSON data"""
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try:
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# Clean up response and extract JSON
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import json
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import re
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# Find JSON in response - try multiple patterns
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json_match = re.search(r"\{.*\}", result_text, re.DOTALL)
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if json_match:
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json_str = json_match.group()
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# Clean up common JSON issues
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json_str = re.sub(
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r",\s*([}\]])", r"\1", json_str
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) # Remove trailing commas
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json_str = re.sub(
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r"([{,])\s*([a-zA-Z_][a-zA-Z0-9_]*)\s*:", r'\1"\2":', json_str
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) # Quote unquoted keys
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try:
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data = json.loads(json_str)
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except json.JSONDecodeError as e:
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# Try to fix common JSON issues
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logger.warning(f"Initial JSON parsing failed: {e}")
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# Try to extract individual fields using regex
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vendor_match = re.search(r'"vendor"\s*:\s*"([^"]*)"', json_str)
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description_match = re.search(
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r'"description"\s*:\s*"([^"]*)"', json_str
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)
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total_amount_match = re.search(
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r'"total_amount"\s*:\s*([0-9.]+)', json_str
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)
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tax_amount_match = re.search(
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r'"tax_amount"\s*:\s*([0-9.]+)', json_str
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)
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date_match = re.search(r'"date"\s*:\s*"([^"]*)"', json_str)
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category_match = re.search(r'"category"\s*:\s*"([^"]*)"', json_str)
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confidence_match = re.search(
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r'"confidence"\s*:\s*([0-9.]+)', json_str
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)
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currency_match = re.search(
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r'"currency"\s*:\s*"([^"]*)"', json_str
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)
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data = {
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"vendor": vendor_match.group(1) if vendor_match else "",
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"description": description_match.group(1)
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if description_match
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else "",
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"total_amount": float(total_amount_match.group(1))
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if total_amount_match
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else 0.0,
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"tax_amount": float(tax_amount_match.group(1))
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if tax_amount_match
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else 0.0,
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"date": date_match.group(1) if date_match else "",
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"category": category_match.group(1)
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if category_match
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else "Other",
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"confidence": float(confidence_match.group(1))
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if confidence_match
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else 0.5,
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"currency": currency_match.group(1) if currency_match else "CAD"
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}
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# Validate and clean data
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return {
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"vendor": str(data.get("vendor", "")).strip(),
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"description": str(data.get("description", "")).strip(),
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"total_amount": float(data.get("total_amount", 0)),
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"tax_amount": float(data.get("tax_amount", 0)),
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"date": str(data.get("date", "")).strip(),
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"category": str(data.get("category", "Other")).strip(),
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"confidence": float(data.get("confidence", 0.5)),
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"extraction_success": True,
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"currency": data.get("currency", "CAD").strip(),
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}
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else:
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# Try to extract fields from plain text
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logger.warning("No JSON found in response, attempting text extraction")
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return self._extract_from_plain_text(result_text)
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except Exception as e:
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logger.error(f"JSON parsing error: {str(e)}")
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return {
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"error": f"JSON parsing error: {str(e)}",
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"extraction_success": False,
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}
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def _extract_from_plain_text(self, text: str) -> Dict[str, Any]:
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"""Extract receipt data from plain text when JSON parsing fails"""
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try:
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import re
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# Extract vendor (look for common patterns)
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vendor_patterns = [
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r"(?:vendor|store|merchant|company)\s*[:\-]?\s*([A-Za-z0-9\s&.,]+)",
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r"([A-Z][A-Za-z0-9\s&.,]{3,30})", # Capitalized words
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]
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vendor = ""
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for pattern in vendor_patterns:
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match = re.search(pattern, text, re.IGNORECASE)
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if match:
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vendor = match.group(1).strip()
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break
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# Extract amount (look for currency patterns)
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amount_patterns = [
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r"\$?\s*([0-9,]+\.?[0-9]*)",
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r"(?:total|amount|sum)\s*[:\-]?\s*\$?\s*([0-9,]+\.?[0-9]*)",
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]
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total_amount = 0.0
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for pattern in amount_patterns:
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match = re.search(pattern, text, re.IGNORECASE)
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if match:
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try:
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total_amount = float(match.group(1).replace(",", ""))
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break
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except ValueError:
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continue
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# Extract date
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date_patterns = [
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r"(\d{4}-\d{2}-\d{2})",
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r"(\d{1,2}/\d{1,2}/\d{2,4})",
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r"(Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)\s+\d{1,2},?\s+\d{4}",
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]
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date = ""
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for pattern in date_patterns:
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match = re.search(pattern, text, re.IGNORECASE)
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if match:
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date = match.group(0)
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break
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return {
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"vendor": vendor or "Unknown",
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"total_amount": total_amount,
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"tax_amount": 0.0,
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"date": date or "",
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"category": "Other",
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"confidence": 0.3, # Low confidence for text extraction
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"extraction_success": True,
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}
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except Exception as e:
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logger.error(f"Text extraction error: {str(e)}")
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return {
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"vendor": "Unknown",
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"total_amount": 0.0,
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"tax_amount": 0.0,
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"date": "",
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"category": "Other",
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"confidence": 0.1,
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"extraction_success": False,
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"error": f"Text extraction failed: {str(e)}",
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}
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async def save_uploaded_file(self, file_content: bytes, filename: str) -> str:
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"""Save uploaded file to temporary storage"""
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try:
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# Create uploads directory if it doesn't exist
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upload_dir = "uploads"
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os.makedirs(upload_dir, exist_ok=True)
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# Generate unique filename
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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safe_filename = f"{timestamp}_{filename.replace(' ', '_')}"
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file_path = os.path.join(upload_dir, safe_filename)
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# Save file
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async with aiofiles.open(file_path, "wb") as f:
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await f.write(file_content)
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return file_path
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except Exception as e:
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raise Exception(f"Failed to save file: {str(e)}")
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async def extract_transactions_from_image(self, image_path: str) -> Dict[str, Any]:
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"""Extract multiple transactions from an image (bank statement, credit card statement, etc.)"""
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try:
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# Encode image to base64
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base64_image = self._encode_image(image_path)
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# Create Groq vision prompt for transaction extraction
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prompt = """
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Analyze this financial document image (bank statement, credit card statement, etc.) and extract ALL transactions in JSON format.
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Look for transaction lists, payment records, or any financial entries that show:
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- Date
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- Amount (positive or negative)
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- Vendor/Description/Payee name
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- Any additional notes or memo
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Return the transactions as a JSON array:
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{
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"extraction_success": true,
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"transactions": [
|
||
|
|
{
|
||
|
|
"date": "YYYY-MM-DD",
|
||
|
|
"amount": 0.00,
|
||
|
|
"vendor": "Vendor name",
|
||
|
|
"memo": "Additional notes"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"date": "YYYY-MM-DD",
|
||
|
|
"amount": -0.00,
|
||
|
|
"vendor": "Another vendor",
|
||
|
|
"memo": "Payment or charge description"
|
||
|
|
}
|
||
|
|
]
|
||
|
|
}
|
||
|
|
|
||
|
|
Rules:
|
||
|
|
- Extract ALL visible transactions
|
||
|
|
- Include both positive (credits) and negative (debits) amounts
|
||
|
|
- Use the actual date format from the document
|
||
|
|
- Vendor should be the merchant/payee name
|
||
|
|
- Memo can include transaction type, reference numbers, etc.
|
||
|
|
- If no transactions found, return empty array but set extraction_success to true
|
||
|
|
|
||
|
|
Return only valid JSON.
|
||
|
|
"""
|
||
|
|
|
||
|
|
# Call Groq vision API
|
||
|
|
response = self.client.chat.completions.create(
|
||
|
|
messages=[
|
||
|
|
{
|
||
|
|
"role": "user",
|
||
|
|
"content": [
|
||
|
|
{"type": "text", "text": prompt},
|
||
|
|
{
|
||
|
|
"type": "image_url",
|
||
|
|
"image_url": {
|
||
|
|
"url": f"data:image/jpeg;base64,{base64_image}",
|
||
|
|
},
|
||
|
|
},
|
||
|
|
],
|
||
|
|
}
|
||
|
|
],
|
||
|
|
model=self.model,
|
||
|
|
max_tokens=2000, # Higher token limit for multiple transactions
|
||
|
|
temperature=0.1,
|
||
|
|
)
|
||
|
|
|
||
|
|
# Parse response
|
||
|
|
result_text = response.choices[0].message.content.strip()
|
||
|
|
return self._parse_transaction_extraction_result(result_text)
|
||
|
|
|
||
|
|
except Exception as e:
|
||
|
|
return {
|
||
|
|
"extraction_success": False,
|
||
|
|
"error": f"Transaction extraction error: {str(e)}",
|
||
|
|
"transactions": [],
|
||
|
|
}
|
||
|
|
|
||
|
|
def _parse_transaction_extraction_result(self, result_text: str) -> Dict[str, Any]:
|
||
|
|
"""Parse Groq response for transaction extraction"""
|
||
|
|
try:
|
||
|
|
import json
|
||
|
|
import re
|
||
|
|
|
||
|
|
# Find the first '{' and last '}'
|
||
|
|
start = result_text.find("{")
|
||
|
|
end = result_text.rfind("}")
|
||
|
|
if start == -1 or end == -1 or end <= start:
|
||
|
|
return {
|
||
|
|
"extraction_success": False,
|
||
|
|
"error": "Could not find JSON object in AI response",
|
||
|
|
"transactions": [],
|
||
|
|
}
|
||
|
|
json_str = result_text[start : end + 1]
|
||
|
|
|
||
|
|
# Remove trailing commas before } or ]
|
||
|
|
json_str = re.sub(r",\s*([}\]])", r"\1", json_str)
|
||
|
|
|
||
|
|
try:
|
||
|
|
data = json.loads(json_str)
|
||
|
|
except Exception as e:
|
||
|
|
import logging
|
||
|
|
|
||
|
|
logging.error(f"JSON parsing error: {str(e)}")
|
||
|
|
logging.error(f"Offending JSON string:\n{json_str}")
|
||
|
|
return {
|
||
|
|
"extraction_success": False,
|
||
|
|
"error": f"JSON parsing error: {str(e)}",
|
||
|
|
"transactions": [],
|
||
|
|
}
|
||
|
|
|
||
|
|
# Validate and clean data
|
||
|
|
transactions = data.get("transactions", [])
|
||
|
|
cleaned_transactions = []
|
||
|
|
for txn in transactions:
|
||
|
|
try:
|
||
|
|
cleaned_txn = {
|
||
|
|
"date": str(txn.get("date", "")).strip(),
|
||
|
|
"amount": float(
|
||
|
|
str(txn.get("amount", 0)).replace("$", "").replace(",", "")
|
||
|
|
),
|
||
|
|
"vendor": str(txn.get("vendor", "")).strip(),
|
||
|
|
"memo": str(txn.get("memo", "")).strip(),
|
||
|
|
}
|
||
|
|
cleaned_transactions.append(cleaned_txn)
|
||
|
|
except Exception:
|
||
|
|
continue
|
||
|
|
return {
|
||
|
|
"extraction_success": data.get("extraction_success", True),
|
||
|
|
"transactions": cleaned_transactions,
|
||
|
|
"total_transactions": len(cleaned_transactions),
|
||
|
|
}
|
||
|
|
except Exception as e:
|
||
|
|
import logging
|
||
|
|
|
||
|
|
logging.error(f"JSON parsing error (outer): {str(e)}")
|
||
|
|
return {
|
||
|
|
"extraction_success": False,
|
||
|
|
"error": f"JSON parsing error: {str(e)}",
|
||
|
|
"transactions": [],
|
||
|
|
}
|
||
|
|
|
||
|
|
def _parse_date_to_iso(self, date_str: str) -> str:
|
||
|
|
"""Parse various date formats and convert to YYYY-MM-DD"""
|
||
|
|
try:
|
||
|
|
import re
|
||
|
|
from datetime import datetime
|
||
|
|
|
||
|
|
date_str = date_str.strip().upper()
|
||
|
|
|
||
|
|
# Handle formats like "MAY 22", "JUN 01", "MAY 22, 2024"
|
||
|
|
month_pattern = r"(JAN|FEB|MAR|APR|MAY|JUN|JUL|AUG|SEP|OCT|NOV|DEC)\s+(\d{1,2})(?:,\s*(\d{4}))?"
|
||
|
|
match = re.match(month_pattern, date_str)
|
||
|
|
|
||
|
|
if match:
|
||
|
|
month_abbr, day, year = match.groups()
|
||
|
|
month_map = {
|
||
|
|
"JAN": 1,
|
||
|
|
"FEB": 2,
|
||
|
|
"MAR": 3,
|
||
|
|
"APR": 4,
|
||
|
|
"MAY": 5,
|
||
|
|
"JUN": 6,
|
||
|
|
"JUL": 7,
|
||
|
|
"AUG": 8,
|
||
|
|
"SEP": 9,
|
||
|
|
"OCT": 10,
|
||
|
|
"NOV": 11,
|
||
|
|
"DEC": 12,
|
||
|
|
}
|
||
|
|
|
||
|
|
month = month_map[month_abbr]
|
||
|
|
day = int(day)
|
||
|
|
year = int(year) if year else datetime.now().year
|
||
|
|
|
||
|
|
# Handle 2-digit years
|
||
|
|
if year < 100:
|
||
|
|
year += 2000
|
||
|
|
|
||
|
|
return f"{year:04d}-{month:02d}-{day:02d}"
|
||
|
|
|
||
|
|
# Handle YYYY-MM-DD format
|
||
|
|
if re.match(r"\d{4}-\d{2}-\d{2}", date_str):
|
||
|
|
return date_str
|
||
|
|
|
||
|
|
# Handle MM/DD/YYYY format
|
||
|
|
if re.match(r"\d{1,2}/\d{1,2}/\d{4}", date_str):
|
||
|
|
return datetime.strptime(date_str, "%m/%d/%Y").strftime("%Y-%m-%d")
|
||
|
|
|
||
|
|
# Handle MM/DD/YY format
|
||
|
|
if re.match(r"\d{1,2}/\d{1,2}/\d{2}", date_str):
|
||
|
|
return datetime.strptime(date_str, "%m/%d/%y").strftime("%Y-%m-%d")
|
||
|
|
|
||
|
|
return None
|
||
|
|
|
||
|
|
except Exception:
|
||
|
|
return None
|