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from langchain.llms 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
#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 = """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': 'Santoshvarma0988@gmail.com','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 = replicate.run('replicate/llama-2-70b-chat:2c1608e18606fad2812020dc541930f2d0495ce32eee50074220b87300bc16e1',
#input={"prompt":prompt_template.format(pages=pages_data) ,
#"temperature":0.1, "top_p":0.9, "max_length":512, "repetition_penalty":1})
#full_response = ''
#for item in output:
#full_response += item
#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 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(raw_data)
#print("extracted raw data")
llm_extracted_data=extracted_data(raw_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.")
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 |