import streamlit as st
from langchain.schema.messages import HumanMessage, SystemMessage, AIMessage
from streamlit_chat import message
import PyPDF2
from langchain.prompts import PromptTemplate
from dotenv import load_dotenv
import os
from langchain_community.chat_models import ChatOpenAI
import json

load_dotenv()
os.getenv('OPENAI_API_KEY')

chat_bot = ChatOpenAI(temperature=0.5)


def streamlit_init():
    st.set_page_config(
        page_title = "Resume GPT",
        page_icon= '🤖'
    )

    st.header("Your Resume GPT 🤖")

    if 'flowmessages' not in st.session_state:
        st.session_state['flowmessages'] = [SystemMessage(
            content='''You are a resume consultant, good at pointing out keywords and other
            points how users can update their resume for a particualr job profile.'''
            )]
        
    description = st.text_input('Add a job profile here (LinkedIn)...')
    resume = st.file_uploader('Add your resume here', type='pdf')

    return description,resume

def getTextFromPDF(resume):
    pdf_reader = PyPDF2.PdfReader(resume)
    extracted_text = ""

    for page_num in range(len(pdf_reader.pages)):
        page = pdf_reader.pages[page_num]
        extracted_text += page.extract_text()

    return extracted_text

def chatBot(resume, description):
    answer = chat_bot.invoke(f'''
    Hey Act Like a skilled or very experience ATS(Application Tracking System)
    with a deep understanding of tech field,software engineering,data science ,data analyst
    and big data engineer. Your task is to evaluate the resume based on the given job description.
    You must consider the job market is very competitive and you should provide 
    best assistance for improving thr resumes. Assign the percentage Matching based 
    on Jd and
    the missing keywords with high accuracy
    resume:{resume}
    description:{description}

    I want the response in one single string having the structure
    {{"JD Match":"%","MissingKeywords:[]", "ResumeSuggestions": [], Profile Summary":""}}
    ''')
    return answer.content

description, resume = streamlit_init()
submit = st.button("Analyze")

if submit: 
    if resume is not None and description != "":
        extract_resume = getTextFromPDF(resume)
        answer = chatBot(extract_resume, description)

        json_answer = json.loads(answer)

        st.subheader('Job Description Match - ')
        st.write(json_answer['JD Match'])

        st.subheader('Missing Key Words - ')
        formatted_keywords = "\n".join([f"{i+1}. {keyword}" for i, keyword in enumerate(json_answer['MissingKeywords'])])
        st.write(formatted_keywords)

        st.subheader('Resume Suggestions - ')
        formatted_keywords = "\n".join([f"{i+1}. {keyword}" for i, keyword in enumerate(json_answer['ResumeSuggestions'])])
        st.write(formatted_keywords)

        st.subheader('Profile Summary - ')
        st.write(json_answer['Profile Summary'])