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| from langchain_openai import OpenAI | |
| from pypdf import PdfReader | |
| import pandas as pd | |
| import re | |
| from langchain.prompts import PromptTemplate | |
| from langchain_community.llms import CTransformers | |
| from ctransformers import AutoModelForCausalLM | |
| #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 = """Please Extract all the following values : invoice no., Description, Quantity, date, | |
| Unit price , Amount, Total, email, phone number and address from this data: {pages} | |
| 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': '[email protected]','Phone number': '9999999999','Address': 'Mumbai, India'}} | |
| """ | |
| # prompt_template = PromptTemplate(input_variables=["pages"], template=template) | |
| # llm = CTransformers(model="llama-2-7b-chat.ggmlv3.q8_0.bin",model_type='llama') | |
| llm = AutoModelForCausalLM.from_pretrained("TheBloke/Llama-2-7B-Chat-GGML", model_file="llama-2-7b-chat.ggmlv3.q8_0.bin") | |
| #Creating the final PROMPT | |
| prompt = PromptTemplate( | |
| input_variables=["pages"], | |
| template=template,) | |
| #Generating the response using LLM | |
| #Last week langchain has recommended to use 'invoke' function for the below please :) | |
| response=llm(prompt.format(email_topic=form_input,sender=email_sender,recipient=email_recipient,style=email_style)) | |
| output_text=llm(prompt_template.format(pages=pages_data)) | |
| full_response = '' | |
| for item in output_text: | |
| full_response += item | |
| 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 no.': pd.Series(dtype='str'), | |
| 'Description': pd.Series(dtype='str'), | |
| 'Quantity': pd.Series(dtype='str'), | |
| 'Date': 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'), | |
| 'Address': pd.Series(dtype='str') | |
| }) | |
| for filename in user_pdf_list: | |
| # print(filename) | |
| raw_data=get_pdf_text(filename) | |
| print("pdf_Data",raw_data) | |
| # print("extracted raw data") | |
| llm_extracted_data=extracted_data(raw_data) | |
| print("llm_extracted_data",llm_extracted_data) | |
| #print(llm_extracted_data) | |
| #print("llm extracted data") | |
| #Adding items to our list - Adding data & its metadata | |
| pattern = r'{(.+)}' | |
| match = re.search(pattern, llm_extracted_data, re.DOTALL) | |
| if match: | |
| extracted_text = match.group(1) | |
| # Converting the extracted text to a dictionary | |
| data_dict = eval('{' + extracted_text + '}') | |
| print(data_dict) | |
| else: | |
| print("No match found.") | |
| # Initialize data_dict | |
| data_dict = {} | |
| df=df._append([data_dict], ignore_index=True) | |
| print("********************DONE***************") | |
| # df=df.append(save_to_dataframe(llm_extracted_data), ignore_index=True) | |
| llm_extracted_data | |
| return llm_extracted_data |