backend.py

import os
import re
import pandas as pd
from pypdf import PdfReader
from typing import List, Dict
from langchain.prompts import PromptTemplate
from langchain_google_genai import GoogleGenerativeAI


api_key = "AIzaSyCYGj5e2eAQbUi9HtuMaW0LDSnDuxLG54U"


class InvoicePipeline:

    def __init__(self, paths):
        self._paths = paths
        self._llm = GoogleGenerativeAI(model="gemini-1.0-pro", google_api_key=api_key)
        self._prompt_template = self._get_default_prompt_template()
    # This funcition will help in extracting and run the code, and will produce a dataframe for us
    def run(self) -> pd.DataFrame:
        # We have defined the way the data has to be returned
        df = pd.DataFrame({
            "Invoice ID": pd.Series(dtype = "int"),
            "DESCRIPTION": pd.Series(dtype = "str"),
            "Issue Data": pd.Series(dtype = "str"),
            "UNIT PRICE": pd.Series(dtype = "str"),
            "AMOUNT": pd.Series(dtype = "int"),
            "Bill For": pd.Series(dtype = "str"),
            "From": pd.Series(dtype ="str"),
            "Terms": pd.Series(dtype = "str")}
        )

        for path in self._paths:
            raw_text = self._get_raw_text_from_pdf(path) # This function needs to be created
            llm_resp = self._extract_data_from_llm(raw_text) # 
            data = self._parse_response(llm_resp)
            df = pd.concat([df, pd.DataFrame([data])], ignore_index = True)

        return df

    # The default template that the machine will take
    def _get_default_prompt_template(self) -> PromptTemplate:
        template = """Extract all the following values: Invoice ID, DESCRIPTION, Issue Data,UNIT PRICE, AMOUNT, Bill for, From and Terms for: {pages}

        Expected Outcome: remove any dollar symbols {{"Invoice ID":"12341234", "DESCRIPTION": "UNIT PRICE", "AMOUNT": "3", "Date": "2/1/2021", "AMOUNT": "100", "Bill For": "Dev", "From": "Coca Cola", "Terms" : "Net for 30 days"}}
        """

        prompt_template = PromptTemplate(input_variables = ["pages"], template = template)
        return prompt_template


    # We will try to extract the text from the PDF to a normal variable.
    def _get_raw_text_from_pdf(self, path:str) -> str:
        text = ""
        pdf_reader = PdfReader(path)
        for page in pdf_reader.pages:
            text += page.extract_text()
        return text

    def _extract_data_from_llm(self, raw_data:str) -> str:
        resp = self._llm(self._prompt_template.format(pages = raw_data))
        return resp
        
    def _parse_response(self, response: str) -> Dict[str, str]:
        pattern = r'{(.+)}'
        re_match = re.search(pattern, response, re.DOTALL)
        if re_match:
            extracted_text = re_match.group(1)
            data = eval('{' + extracted_text + '}')
            return data
        else:
            raise Exception("No match found.")