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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 |