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#As Langchain team has been working aggresively on improving the tool, we can see a lot of changes happening every weeek,
#As a part of it, the below import has been depreciated
#from langchain.llms import OpenAI
from langchain_openai import OpenAI
from pypdf import PdfReader
#from langchain.llms.openai import OpenAI
import pandas as pd
import re
# import replicate
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
# filename = r"/Invoice_Extraction_Bot/Invoice/invoice_1001329.pdf"
# raw_data=get_pdf_text(filename)
#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 = OpenAI(temperature=.7)
# full_response=llm(prompt_template.format(pages=pages_data))
#The below code will be used when we want to use LLAMA 2 model, we will use Replicate for hosting our model....
# output = CTransformers(model=r"TheBloke/llama-2-7b-chat.ggmlv3.q8_0.bin", #https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/tree/main
# model_type='llama',
# input={"prompt":prompt_template.format(pages=pages_data) ,
# "temperature":0.1, "top_p":0.9, "max_length":512, "repetition_penalty":1})
llm = AutoModelForCausalLM.from_pretrained("TheBloke/Llama-2-7B-Chat-GGML", model_file="llama-2-7b-chat.ggmlv3.q8_0.bin")
output_text=llm(prompt_template.format(pages=pages_data))
full_response = ''
for item in output_text:
full_response += item
#print(full_response)
return full_response
#print(raw_data)
# print("extracted raw data")
# llm_extracted_data=extracted_data(raw_data)
#print(llm_extracted_data)
# iterate over files in
# that user uploaded PDF files, one by one
def create_docs(filename):
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 filename:
print(filename)
raw_data=get_pdf_text(filename)
print(raw_data)
# print("extracted raw data")
llm_extracted_data=extracted_data(raw_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)
df.head()
return df |