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| 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"] = "AIzaSyCZ5porIw2tqjs-YEbxxJcAkJwJd_Qi3G8" | |
| 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) | |
| # Create emebeddings | |
| 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() | |
| # interview history | |
| 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 professional 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!")) | |
| # token count | |
| 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("Create an interview guideline and prepare only one questions for each topic. Make sure the questions tests the technical knowledge") | |
| # llm chain and memory | |
| 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 an interviewer strictly following the guideline in the current conversation. | |
| Candidate has no idea what the guideline is. | |
| Ask me questions and wait for my answers. Do not write explanations. | |
| Ask question like a real person, only one question at a time. | |
| Do not ask the same question. | |
| Do not repeat the question. | |
| Do ask follow-up questions if necessary. | |
| You name is GPTInterviewer. | |
| I want you to only reply as an interviewer. | |
| Do not write all the conversation at once. | |
| If there is an error, point it out. | |
| Current Conversation: | |
| {history} | |
| Candidate: {input} | |
| AI: """) | |
| 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 # Assuming the last message before the user's answer is the question | |
| 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("Professional Screen") | |
| with open('job_description.json', 'r') as f: | |
| jd = json.load(f) | |
| if jd: | |
| # initialize session states | |
| initialize_session_state_jd(jd) | |
| #st.write(st.session_state.jd_guideline) | |
| 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 submit email adress, get interview feedback imediately | |
| 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): # Corrected from history to 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.") | |