| import streamlit as st |
| 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"] = "AIzaSyD-61G3GhSY97O-X2AlpXGv1MYBBMRFmwg" |
|
|
| @dataclass |
| class Message: |
| """class for keeping track of interview history.""" |
| origin: Literal["human", "ai"] |
| message: str |
|
|
| def save_vector(text): |
| """embeddings""" |
|
|
| nltk.download('punkt') |
| text_splitter = NLTKTextSplitter() |
| texts = text_splitter.split_text(text) |
| |
| embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001") |
| docsearch = FAISS.from_texts(texts, embeddings) |
| return docsearch |
|
|
| def initialize_session_state_jd(jd): |
| """ initialize session states """ |
| if "user_responses" not in st.session_state: |
| st.session_state.user_responses = [] |
| if 'jd_docsearch' not in st.session_state: |
| st.session_state.jd_docserch = save_vector(jd) |
| if 'jd_retriever' not in st.session_state: |
| st.session_state.jd_retriever = st.session_state.jd_docserch.as_retriever(search_type="similarity") |
| if 'jd_chain_type_kwargs' not in st.session_state: |
| Interview_Prompt = PromptTemplate(input_variables=["context", "question"], |
| template=templates.jd_template) |
| st.session_state.jd_chain_type_kwargs = {"prompt": Interview_Prompt} |
| if 'jd_memory' not in st.session_state: |
| st.session_state.jd_memory = ConversationBufferMemory() |
| |
| if "jd_history" not in st.session_state: |
| st.session_state.jd_history = [] |
| st.session_state.jd_history.append(Message("ai", |
| "Hello, Welcome to the interview. I am your interviewer today. I will ask you Technical questions regarding the job description you submitted." |
| "Please start by introducting a little bit about yourself. Note: The maximum length of your answer is 4097 tokens!")) |
| |
| if "token_count" not in st.session_state: |
| st.session_state.token_count = 0 |
| if "jd_guideline" not in st.session_state: |
| llm = ChatGoogleGenerativeAI( |
| model="gemini-pro") |
| st.session_state.jd_guideline = RetrievalQA.from_chain_type( |
| llm=llm, |
| chain_type_kwargs=st.session_state.jd_chain_type_kwargs, chain_type='stuff', |
| retriever=st.session_state.jd_retriever, memory=st.session_state.jd_memory).run(f"Create a list of DSA interview questions that comprehensively test the technical knowledge of candidates.") |
| if "jd_screen" not in st.session_state: |
| llm = ChatGoogleGenerativeAI( |
| model="gemini-pro") |
| PROMPT = PromptTemplate( |
| input_variables=["history", "input"], |
| template="""I want you to act as a technical interviewer, strictly following the guideline in the current conversation. |
| Candidate has no idea what the guideline is. |
| Ask me technical questions related to {job_role}, including Data Structures and Algorithms (DSA), conceptual questions related to {job_role}, and role-specific questions. Wait for my answers after each question. Do not write explanations. |
| Ask questions like a real technical interviewer, focusing on one concept at a time. |
| Do not ask the same question repeatedly. |
| Do not repeat the question verbatim. |
| Ask follow-up questions if necessary to clarify or probe deeper into the candidate's understanding. |
| You are the Technical Interviewer. |
| Respond only as a technical interviewer. |
| Do not write the entire conversation at once. |
| If there is an error in my response, point it out. |
| |
| Current Conversation: |
| {history} |
| |
| Candidate: {input} |
| Technical Interviewer: """) |
|
|
|
|
| st.session_state.jd_screen = ConversationChain(prompt=PROMPT, llm=llm, |
| memory=st.session_state.jd_memory) |
| if 'jd_feedback' not in st.session_state: |
| llm = ChatGoogleGenerativeAI( |
| model="gemini-pro") |
| st.session_state.jd_feedback = ConversationChain( |
| prompt=PromptTemplate(input_variables=["history", "input"], template=templates.feedback_template), |
| llm=llm, |
| memory=st.session_state.jd_memory, |
| ) |
|
|
| def answer_call_back(): |
| formatted_history = [] |
| for message in st.session_state.jd_history: |
| if message.origin == "human": |
| formatted_message = {"speaker": "user", "text": message.message} |
| else: |
| formatted_message = {"speaker": "assistant", "text": message.message} |
| formatted_history.append(formatted_message) |
|
|
| user_answer = st.session_state.get('answer', '') |
|
|
| answer = st.session_state.jd_screen.run(input=user_answer, history=formatted_history) |
|
|
| if user_answer: |
| st.session_state.jd_history.append(Message("human", user_answer)) |
| if st.session_state.jd_history and len(st.session_state.jd_history) > 1: |
| last_question = st.session_state.jd_history[-2].message |
| st.session_state.user_responses.append({"question": last_question, "answer": user_answer}) |
|
|
| if answer: |
| st.session_state.jd_history.append(Message("ai", answer)) |
|
|
| return answer |
|
|
| def app(): |
| st.title("Technical Screen") |
|
|
|
|
| with open('job_description.json', 'r') as f: |
| jd = json.load(f) |
|
|
| |
|
|
| if jd: |
| |
| initialize_session_state_jd(jd) |
| |
| credit_card_placeholder = st.empty() |
| col1, col2, col3 = st.columns(3) |
| with col1: |
| feedback = st.button("Get Interview Feedback") |
| with col2: |
| guideline = st.button("Show me interview guideline!") |
| with col3: |
| myresponse = st.button("Show my responses") |
| chat_placeholder = st.container() |
| answer_placeholder = st.container() |
| audio = None |
| |
| if guideline: |
| st.write(st.session_state.jd_guideline) |
| if feedback: |
| evaluation = st.session_state.jd_feedback.run("please give evalution regarding the interview") |
| st.markdown(evaluation) |
| st.download_button(label="Download Interview Feedback", data=evaluation, file_name="interview_feedback.txt") |
| st.stop() |
| if myresponse: |
| with st.container(): |
| st.write("### My Interview Responses") |
| for idx, message in enumerate(st.session_state.jd_history): |
| if message.origin == "ai": |
| st.write(f"**Question {idx//2 + 1}:** {message.message}") |
| else: |
| st.write(f"**My Answer:** {message.message}\n") |
|
|
| else: |
| with answer_placeholder: |
| voice = 0 |
| if voice: |
| print(voice) |
| else: |
| answer = st.chat_input("Your answer") |
| if answer: |
| st.session_state['answer'] = answer |
| audio = answer_call_back() |
| with chat_placeholder: |
| for answer in st.session_state.jd_history: |
| if answer.origin == 'ai': |
| if audio: |
| with st.chat_message("assistant"): |
| st.write(answer.message) |
| else: |
| with st.chat_message("assistant"): |
| st.write(answer.message) |
| else: |
| with st.chat_message("user"): |
| st.write(answer.message) |
|
|
| credit_card_placeholder.caption(f""" |
| Progress: {int(len(st.session_state.jd_history) / 50 * 100)}% completed.""") |
| else: |
| st.info("Please submit a job description to start the interview.") |
|
|
