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| import streamlit as st | |
| from streamlit_option_menu import option_menu | |
| from app_utils import switch_page | |
| from PIL import Image | |
| 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"] = "AIzaSyCA4__JMC_ZIQ9xQegIj5LOMLhSSrn3pMw" | |
| im = Image.open("icon.png") | |
| def app(): | |
| lan = st.selectbox("#### Language", ["English", "中文"]) | |
| if lan == "English": | |
| home_title = "AI Interviewer" | |
| home_introduction = "Welcome to AI Interviewer, empowering your interview preparation with generative AI." | |
| st.markdown( | |
| "<style>#MainMenu{visibility:hidden;}</style>", | |
| unsafe_allow_html=True | |
| ) | |
| st.image(im, width=100) | |
| st.markdown(f"""# {home_title}""", unsafe_allow_html=True) | |
| st.markdown("""\n""") | |
| # st.markdown("#### Greetings") | |
| st.markdown("Welcome to AI Interviewer! 👏 AI Interviewer is your personal interviewer powered by generative AI that conducts mock interviews." | |
| "You can upload your resume and enter job descriptions, and AI Interviewer will ask you customized questions. Additionally, you can configure your own Interviewer!") | |
| st.markdown("""\n""") | |
| role = st.text_input("Enter your role") | |
| if role: | |
| st.markdown(f"Your role is {role}") | |
| llm = ChatGoogleGenerativeAI( | |
| model="gemini-pro") | |
| prompt = f"Provide the tech stack and responsibilities for the top 3 job recommendations based on the role: {role}. " + """ | |
| For each job recommendation, list the required tech stack and associated responsibilities without giving any title or role name. | |
| Ensure the information is detailed and precise. | |
| follwoing is for example purpose, have our response in this format: | |
| ] | |
| """ | |
| analysis = llm.invoke(prompt) | |
| st.write(analysis.content) | |
| if 'tech_stack' not in st.session_state: | |
| st.session_state.tech_stack = "" | |
| if 'responsibilities' not in st.session_state: | |
| st.session_state.responsibilities = "" | |
| with st.form(key='input_form'): | |
| tech_stack = st.text_input("Enter preferred tech stack", key='tech_stack') | |
| responsibilities = st.text_input("Enter responsibilities", key='responsibilities') | |
| difficulty_level = st.selectbox("Select difficulty level", ["Easy", "Medium", "Hard"], key='difficulty_level') | |
| certification_link = " " | |
| certification_link = st.text_input("Enter certification link (optional)", key='certification_link') | |
| submit_button = st.form_submit_button(label='Submit') | |
| if submit_button: | |
| if tech_stack and responsibilities: | |
| llm2 = ChatGoogleGenerativeAI(model="gemini-pro") | |
| prompt = f"""Tech stack: {tech_stack}\nResponsibilities: {responsibilities} | |
| create a job description based on tech stack, responsibilities and give tech stack, responsibilities and qualifications for job description | |
| example - | |
| Tech stack: all technical stack here | |
| Qualifications: all qualifications here | |
| Responsibilities: all responsibilities here | |
| """ | |
| response = llm2.invoke(prompt) | |
| if certification_link: | |
| jd = response.content + f"Difficulty Level of interview is: {difficulty_level}" + f"Person has done certifications, here is certification link: {certification_link}" | |
| else: | |
| jd = response.content + f"Difficulty Level of interview is: {difficulty_level}" | |
| if jd: | |
| # Save the jd into a json file | |
| with open("job_description.json", "w") as f: | |
| json.dump(jd, f) | |
| st.success("Job description saved successfully!") |