interview.py

import streamlit as st
from streamlit_option_menu import option_menu
from app_utils import switch_page
from PIL import Image
from streamlit_lottie import st_lottie
from typing import Literal
from dataclasses import dataclass
import json
import base64
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationChain, RetrievalQA
from langchain.prompts.prompt import PromptTemplate
from langchain.text_splitter import NLTKTextSplitter
from langchain.vectorstores import FAISS
import nltk
from prompts.prompts import templates
from langchain_google_genai import ChatGoogleGenerativeAI
import getpass
import os
from langchain_google_genai import GoogleGenerativeAIEmbeddings


if "GOOGLE_API_KEY" not in os.environ:
    os.environ["GOOGLE_API_KEY"] = "AIzaSyCA4__JMC_ZIQ9xQegIj5LOMLhSSrn3pMw"

im = Image.open("icon.png")

def app():

        home_title = "AI Interviewer"

        st.markdown(
            "<style>#MainMenu{visibility:hidden;}</style>",
            unsafe_allow_html=True
        )
        st.image(im, width=100)
        st.markdown(f"""# {home_title}""", unsafe_allow_html=True)
        st.markdown("""\n""")
        # st.markdown("#### Greetings")
        st.markdown("Welcome to AI Interviewer! 👏 AI Interviewer is your personal interviewer powered by generative AI that conducts mock interviews."
                    "You can upload your resume and enter job descriptions, and AI Interviewer will ask you customized questions. Additionally, you can configure your own Interviewer!")
        st.markdown("""\n""")
        jd = st.text_input("Enter your role")
        certification_name = st.text_input("Certification name", "")
        certification_link = st.text_input("Certification link", "")

        if certification_name:
            with open("certification_data.json", "w") as f:
                json.dump(certification_name, f)
            st.success("Certification data saved successfully!")


        if jd:
                with open("job_description.json", "w") as f:
                        json.dump(jd, f)
                        st.success("Job description saved successfully!")