Create backend.py

backend.py

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+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
+
+os.environ["GOOGLE_API_KEY"] = "AIzaSyCYGj5e2eAQbUi9HtuMaW0LDSnDuxLG54U"
+
+
+class InvoicePipeline:
+
+    def __init__(self, paths):
+        self._paths = paths
+        self._llm = GoogleGenerativeAI(model="gemini-1.5-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:
+            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