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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=os.getenv('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)
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