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': 'Santoshvarma0988@gmail.com','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