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| from os import write | |
| from langchain.llms import OpenAI | |
| from pypdf import PdfReader | |
| from langchain.llms.openai import OpenAI | |
| import pandas as pd | |
| import re | |
| import streamlit as st | |
| from langchain.prompts import PromptTemplate | |
| #Extract Information from PDF file | |
| def get_pdf_text(pdf_doc): | |
| text = "" | |
| pdf_reader = PdfReader(pdf_doc) | |
| for page in pdf_reader.pages: | |
| text += page.extract_text() | |
| return text | |
| #Function to extract data from text | |
| def extracted_data(pages_data): | |
| template = """ | |
| Remove any $ symbols. | |
| Extract the following values in a JSON format : Invoice,Date, Description, Quantity, | |
| Unit Price , Amount, Total, Email and Phone Number from this data: {pages}. | |
| """ | |
| prompt_template = PromptTemplate(input_variables=["pages"], template=template) | |
| llm = OpenAI(temperature=.7) | |
| full_response=llm(prompt_template.format(pages=pages_data)) | |
| #print("Full Response") | |
| #print(full_response) | |
| return full_response | |
| # iterate over files in | |
| # that user uploaded PDF files, one by one | |
| def create_docs(user_pdf_list): | |
| df = pd.DataFrame({'Invoice': pd.Series(dtype='str'), | |
| 'Description': pd.Series(dtype='str'), | |
| 'Date': pd.Series(dtype='str'), | |
| 'Quantity': pd.Series(dtype='str'), | |
| 'Unit Price': pd.Series(dtype='str'), | |
| 'Amount': pd.Series(dtype='int'), | |
| 'Total': pd.Series(dtype='str'), | |
| 'Email': pd.Series(dtype='str'), | |
| 'Phone Number': pd.Series(dtype='str') | |
| }) | |
| for filename in user_pdf_list: | |
| print("filename") | |
| print(filename) | |
| raw_data=get_pdf_text(filename) | |
| #st.write("RAWDATA:..."+raw_data) | |
| #print("extracted raw data") | |
| #print(raw_data) | |
| llm_extracted_data=extracted_data(raw_data) | |
| #st.write("LLMDATA..."+llm_extracted_data) | |
| # Write code to append the extracted data to the dataframe | |
| # df = df.append(llm_extracted_data, ignore_index=True) | |
| #print("DF start") | |
| # print(df) | |
| print("llm extracted data") | |
| #Adding items to our list - Adding data & its metadata | |
| print(llm_extracted_data) | |
| pattern = r'{(.+)}' | |
| match = re.search(pattern, llm_extracted_data, re.DOTALL) | |
| if match: | |
| extracted_text = match.group(1) | |
| # Escape quotes in the extracted text | |
| #extracted_text = extracted_text.replace('"', '\\"').replace("'", "\\'") | |
| # Converting the extracted text to a dictionary | |
| # Remove line continuation characters | |
| #extracted_text = extracted_text.replace('\\', '') | |
| #print("********************Extracted Text***************") | |
| #print(extracted_text) | |
| #print("********************Data Dict***************") | |
| data_dict = eval('{' + extracted_text + '}') | |
| #print(data_dict) | |
| #Convert data_dict to a dataframe | |
| # df = pd.DataFrame.from_dict(data_dict, orient='index').T | |
| #print("********************Data Dict End***************") | |
| else: | |
| print("No match found.") | |
| data_dict = {} | |
| #print("********************DF Final start***************") | |
| df = pd.concat([df, pd.DataFrame([data_dict])], ignore_index=True) | |
| #print(df) | |
| #print("********************DF Final End***************") | |
| #df = pd.concat([df, pd.DataFrame.from_dict(data_dict)]) | |
| #df= pd.concat([df, pd.DataFrame(data_dict, index=[0])], ignore_index=True) | |
| #df = pd.concat([df, pd.DataFrame.from_dict(data_dict, orient='columns', index=[0])]) | |
| #df = pd.concat([df, pd.DataFrame({k: [v] for k, v in data_dict.items()})]) | |
| # Create a DataFrame from the dictionary | |
| # If the dictionary values are not lists, convert them to lists | |
| #df=df.append(save_to_dataframe(llm_extracted_data), ignore_index=True) | |
| df.head() | |
| return df |