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from langchain.llms import OpenAI |
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from pypdf import PdfReader |
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from langchain.llms.openai import OpenAI |
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import pandas as pd |
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import re |
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import replicate |
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from langchain.prompts import PromptTemplate |
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def get_pdf_text(pdf_doc): |
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text = "" |
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pdf_reader = PdfReader(pdf_doc) |
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for page in pdf_reader.pages: |
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text += page.extract_text() |
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return text |
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def extracted_data(pages_data): |
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template = """Extract all the following values : invoice no., Description, Quantity, date, |
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Unit price , Amount, Total, email, phone number and address from this data: {pages} |
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Expected output: remove any dollar symbols {{'Invoice no.': '1001329','Description': 'Office Chair','Quantity': '2','Date': '5/4/2023','Unit price': '1100.00','Amount': '2200.00','Total': '2200.00','Email': 'Santoshvarma0988@gmail.com','Phone number': '9999999999','Address': 'Mumbai, India'}} |
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""" |
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prompt_template = PromptTemplate(input_variables=["pages"], template=template) |
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llm = OpenAI(temperature=.7) |
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full_response=llm(prompt_template.format(pages=pages_data)) |
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return full_response |
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def create_docs(user_pdf_list): |
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df = pd.DataFrame({'Invoice no.': pd.Series(dtype='str'), |
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'Description': pd.Series(dtype='str'), |
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'Quantity': pd.Series(dtype='str'), |
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'Date': pd.Series(dtype='str'), |
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'Unit price': pd.Series(dtype='str'), |
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'Amount': pd.Series(dtype='int'), |
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'Total': pd.Series(dtype='str'), |
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'Email': pd.Series(dtype='str'), |
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'Phone number': pd.Series(dtype='str'), |
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'Address': pd.Series(dtype='str') |
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}) |
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for filename in user_pdf_list: |
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print(filename) |
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raw_data=get_pdf_text(filename) |
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llm_extracted_data=extracted_data(raw_data) |
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pattern = r'{(.+)}' |
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match = re.search(pattern, llm_extracted_data, re.DOTALL) |
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if match: |
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extracted_text = match.group(1) |
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data_dict = eval('{' + extracted_text + '}') |
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print(data_dict) |
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else: |
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print("No match found.") |
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df=df._append([data_dict], ignore_index=True) |
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print("********************DONE***************") |
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df.head() |
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return df |