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import streamlit as st |
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from secret_key import openapi_key, groq_api_key |
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import os |
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from groq import Groq |
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from langchain_groq import ChatGroq |
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from PyPDF2 import PdfReader |
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from langchain.text_splitter import CharacterTextSplitter |
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from langchain.embeddings import HuggingFaceEmbeddings |
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from langchain.vectorstores import FAISS |
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from langchain.chains.question_answering import load_qa_chain |
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st.set_page_config(page_icon='rex.png', layout='wide', page_title='Interview Preparation : Getting Started') |
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st.sidebar.markdown("Navigate using the options above") |
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if "groq_key" not in st.session_state: |
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st.session_state.groq_key = groq_api_key |
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os.environ['GROQ_API_KEY'] = groq_api_key |
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llm = ChatGroq( |
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groq_api_key=groq_api_key, |
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model_name="mixtral-8x7b-32768" |
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) |
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st.title("Interview AI Tool : Getting Started") |
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st.header("Recommended Steps : ") |
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st.markdown("""\n1. Please upload your **resume** in the sidebar on your **left**. |
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\n\n2. If you are applying for a specific job , please add **job description** in the text box **below**. |
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\n\n3. For starters we recommend navigating to the **Introduction Round** , here your AI assistant will debrief you |
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on the interview and answer your queries related to the interview. |
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\n\n4. Next, we recommend having a go with a low stakes **Warmup Round** to get you in the right flow for the |
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actual interview round. |
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\n\n5. Navigate to the **Interview Round** to get started with your practice interviews.\n\n""") |
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st.sidebar.header("Resume") |
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resume = st.sidebar.file_uploader(label="**Upload your Resume/CV PDF file**", type='pdf') |
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if resume: |
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pdf = PdfReader(resume) |
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text = "" |
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for page in pdf.pages: |
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text += page.extract_text() |
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text_splitter = CharacterTextSplitter( |
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separator="\n", |
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chunk_size=1000, |
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chunk_overlap=200, |
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length_function=len |
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) |
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chunks = text_splitter.split_text(text) |
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embeddings = HuggingFaceEmbeddings() |
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doc = FAISS.from_texts(chunks, embeddings) |
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chain = load_qa_chain(llm, chain_type="stuff") |
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name = chain.run(input_documents=doc.similarity_search("What is the person's name?"), question="What is the person's name") |
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skills = chain.run(input_documents=doc.similarity_search("What are the person's skills?"), question="What are the person's skills?") |
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resume_info = {"Name": name, |
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"Skills": skills, |
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} |
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st.session_state["Resume Info"] = resume_info |
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st.sidebar.info("PDF Read Successfully!") |
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st.header("Job Details") |
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st.session_state["Job Description"] = st.text_area(label="**Write your job description here**", height=300) |
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