Iyeoluwa Akinrinola
377 lines
15 KiB
Python
377 lines
15 KiB
Python
import groq
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import base64
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import io
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from PIL import Image
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import PyPDF2
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from typing import Dict, Any, List, Optional
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import config
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import os
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import aiofiles
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from datetime import datetime
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class DocumentProcessor:
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def __init__(self):
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self.client = groq.Groq(api_key=config.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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"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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}
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Rules:
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- Extract vendor name as it appears on receipt
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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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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 as e:
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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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"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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}}
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Rules:
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- Extract vendor name as it appears on receipt
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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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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
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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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data = json.loads(json_str)
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# Validate and clean data
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return {
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"vendor": data.get("vendor", "").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": data.get("date", ""),
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"category": data.get("category", "Other"),
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"confidence": float(data.get("confidence", 0.5)),
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"extraction_success": True
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}
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else:
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return {"error": "Could not parse JSON from AI response"}
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except Exception as e:
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return {"error": f"JSON parsing error: {str(e)}"}
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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": [
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{
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"date": "YYYY-MM-DD",
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"amount": 0.00,
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"vendor": "Vendor name",
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"memo": "Additional notes"
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},
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{
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"date": "YYYY-MM-DD",
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"amount": -0.00,
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"vendor": "Another vendor",
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"memo": "Payment or charge description"
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}
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]
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}
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Rules:
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- Extract ALL visible transactions
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- Include both positive (credits) and negative (debits) amounts
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- Use the actual date format from the document
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- Vendor should be the merchant/payee name
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- Memo can include transaction type, reference numbers, etc.
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- If no transactions found, return empty array but set extraction_success to true
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Return only valid JSON.
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"""
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# Call Groq vision API
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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=2000, # Higher token limit for multiple transactions
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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_transaction_extraction_result(result_text)
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except Exception as e:
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return {
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"extraction_success": False,
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"error": f"Transaction extraction error: {str(e)}",
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"transactions": []
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}
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def _parse_transaction_extraction_result(self, result_text: str) -> Dict[str, Any]:
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"""Parse Groq response for transaction extraction"""
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try:
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import json
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import re
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# Find JSON in response
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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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data = json.loads(json_str)
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# Validate and clean data
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transactions = data.get("transactions", [])
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cleaned_transactions = []
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for txn in transactions:
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try:
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# Clean and validate each transaction
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cleaned_txn = {
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"date": str(txn.get("date", "")).strip(),
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"amount": float(str(txn.get("amount", 0)).replace('$', '').replace(',', '')),
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"vendor": str(txn.get("vendor", "")).strip(),
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"memo": str(txn.get("memo", "")).strip()
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}
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cleaned_transactions.append(cleaned_txn)
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except Exception as e:
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# Skip invalid transactions
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continue
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return {
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"extraction_success": data.get("extraction_success", True),
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"transactions": cleaned_transactions,
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"total_transactions": len(cleaned_transactions)
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}
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else:
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return {
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"extraction_success": False,
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"error": "Could not parse JSON from AI response",
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"transactions": []
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}
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except Exception as e:
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return {
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"extraction_success": False,
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"error": f"JSON parsing error: {str(e)}",
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"transactions": []
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}
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def _parse_date_to_iso(self, date_str: str) -> str:
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"""Parse various date formats and convert to YYYY-MM-DD"""
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try:
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import re
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from datetime import datetime
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date_str = date_str.strip().upper()
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# Handle formats like "MAY 22", "JUN 01", "MAY 22, 2024"
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month_pattern = r'(JAN|FEB|MAR|APR|MAY|JUN|JUL|AUG|SEP|OCT|NOV|DEC)\s+(\d{1,2})(?:,\s*(\d{4}))?'
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match = re.match(month_pattern, date_str)
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if match:
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month_abbr, day, year = match.groups()
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month_map = {
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'JAN': 1, 'FEB': 2, 'MAR': 3, 'APR': 4, 'MAY': 5, 'JUN': 6,
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'JUL': 7, 'AUG': 8, 'SEP': 9, 'OCT': 10, 'NOV': 11, 'DEC': 12
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}
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month = month_map[month_abbr]
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day = int(day)
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year = int(year) if year else datetime.now().year
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# Handle 2-digit years
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if year < 100:
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year += 2000
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return f"{year:04d}-{month:02d}-{day:02d}"
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# Handle YYYY-MM-DD format
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if re.match(r'\d{4}-\d{2}-\d{2}', date_str):
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return date_str
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# Handle MM/DD/YYYY format
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if re.match(r'\d{1,2}/\d{1,2}/\d{4}', date_str):
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return datetime.strptime(date_str, '%m/%d/%Y').strftime('%Y-%m-%d')
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# Handle MM/DD/YY format
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if re.match(r'\d{1,2}/\d{1,2}/\d{2}', date_str):
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return datetime.strptime(date_str, '%m/%d/%y').strftime('%Y-%m-%d')
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return None
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except Exception:
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return None |