Spaces:
Sleeping
Sleeping
| 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 = 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) | |