import streamlit as st
#from secret_key import openapi_key, groq_api_key
import os
from groq import Groq
from langchain_groq import ChatGroq

from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains.question_answering import load_qa_chain


st.set_page_config(page_icon='rex.png', layout='wide', page_title='Interview Preparation : Getting Started')

st.sidebar.markdown("Navigate using the options above")
#key = st.sidebar.text_input("Groq API Key ", type="password")

if "groq_key" not in st.session_state:
    st.session_state.groq_key = os.getenv('GROQ_API_KEY')

#if not key and not st.session_state.groq_key:
    #st.sidebar.info("Please add your API key to continue")
    #st.stop()

#if key:
    #st.session_state.groq_key = key

#os.environ['GROQ_API_KEY'] = groq_api_key
llm = ChatGroq(
        groq_api_key=groq_api_key,
        model_name="mixtral-8x7b-32768"
    )

st.title("Interview AI Tool : Getting Started")
st.header("Recommended Steps : ")

st.markdown("""\n1. Please upload your **resume** in the sidebar on your **left**.
               \n\n2. If you are applying for a specific job , please add **job description** in the text box **below**.
               \n\n3. For starters we recommend navigating to the **Introduction Round** , here your AI assistant will debrief you
                on the interview and answer your queries related to the interview.
               \n\n4. Next, we recommend having a go with a low stakes **Warmup Round** to get you in the right flow for the 
               actual interview round.
               \n\n5. Navigate to the **Interview Round** to get started with your practice interviews.\n\n""")

st.sidebar.header("Resume")
resume = st.sidebar.file_uploader(label="**Upload your Resume/CV PDF file**", type='pdf')

if resume:
    pdf = PdfReader(resume)

    text = ""
    for page in pdf.pages:
        text += page.extract_text()

    text_splitter = CharacterTextSplitter(
        separator="\n",
        chunk_size=1000,
        chunk_overlap=200,
        length_function=len
    )
    chunks = text_splitter.split_text(text)
    embeddings = HuggingFaceEmbeddings()
    doc = FAISS.from_texts(chunks, embeddings)

    chain = load_qa_chain(llm, chain_type="stuff")

    name = chain.run(input_documents=doc.similarity_search("What is the person's name?"), question="What is the person's name")
    #exp = chain.run(input_documents=doc.similarity_search("What is the professional experience?"), question="What is the professional experience?")
    skills = chain.run(input_documents=doc.similarity_search("What are the person's skills?"), question="What are the person's skills?")
    #certs = chain.run(input_documents=doc.similarity_search("What is the person's courses/certifications?"), question="What is the person's courses/certifications?")
    #projects = chain.run(input_documents=doc.similarity_search("What are the person's projects"), question="What are the person's projects")

    resume_info = {"Name": name,
                   #"Experience": exp,
                   "Skills": skills,
                   #"Certifications": certs,
                   #"Projects": projects
                   }


    st.session_state["Resume Info"] = resume_info
    st.sidebar.info("PDF Read Successfully!")


st.header("Job Details")

st.session_state["Job Description"] = st.text_area(label="**Write your job description here**", height=300)