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import streamlit as st
from function import GetLLMResponse
from langchain_community.llms import OpenAI
from langchain_google_genai import ChatGoogleGenerativeAI
def app():
roles_and_topics = {
"Front-End Developer": ["HTML/CSS", "JavaScript and Frameworks (React, Angular, Vue.js)", "Responsive Design", "Browser Compatibility"],
"Back-End Developer": ["Server-Side Languages (Node.js, Python, Ruby, PHP)", "Database Management (SQL, NoSQL)", "API Development", "Server and Hosting Management"],
"Full-Stack Developer": ["Combination of Front-End and Back-End Topics", "Integration of Systems", "DevOps Basics"],
"Mobile Developer": ["Android Development (Java, Kotlin)", "iOS Development (Swift, Objective-C)", "Cross-Platform Development (Flutter, React Native)"],
"Data Scientist": ["Statistical Analysis", "Machine Learning Algorithms", "Data Wrangling and Cleaning", "Data Visualization"],
"Data Analyst": ["Data Collection and Processing", "SQL and Database Querying", "Data Visualization Tools (Tableau, Power BI)", "Basic Statistics"],
"Machine Learning Engineer": ["Supervised and Unsupervised Learning", "Model Deployment", "Deep Learning", "Natural Language Processing"],
"DevOps Engineer": ["Continuous Integration/Continuous Deployment (CI/CD)", "Containerization (Docker, Kubernetes)", "Infrastructure as Code (Terraform, Ansible)", "Cloud Platforms (AWS, Azure, Google Cloud)"],
"Cloud Engineer": ["Cloud Architecture", "Cloud Services (Compute, Storage, Networking)", "Security in the Cloud", "Cost Management"],
"Cybersecurity Analyst": ["Threat Detection and Mitigation", "Security Protocols and Encryption", "Network Security", "Incident Response"],
"Penetration Tester": ["Vulnerability Assessment", "Ethical Hacking Techniques", "Security Tools (Metasploit, Burp Suite)", "Report Writing and Documentation"],
"Project Manager": ["Project Planning and Scheduling", "Risk Management", "Agile and Scrum Methodologies", "Stakeholder Communication"],
"UX/UI Designer": ["User Research", "Wireframing and Prototyping", "Design Principles", "Usability Testing"],
"Quality Assurance (QA) Engineer": ["Testing Methodologies", "Automation Testing", "Bug Tracking", "Performance Testing"],
"Blockchain Developer": ["Blockchain Fundamentals", "Smart Contracts", "Cryptographic Algorithms", "Decentralized Applications (DApps)"],
"Digital Marketing Specialist": ["SEO/SEM", "Social Media Marketing", "Content Marketing", "Analytics and Reporting"],
"AI Research Scientist": ["AI Theory", "Algorithm Development", "Neural Networks", "Natural Language Processing"],
"AI Engineer": ["AI Model Deployment", "Machine Learning Engineering", "Deep Learning", "AI Tools and Frameworks"],
"Generative AI Specialist (GenAI)": ["Generative Models", "GANs (Generative Adversarial Networks)", "Creative AI Applications", "Ethics in AI"],
"Generative Business Intelligence Specialist (GenBI)": ["Automated Data Analysis", "Business Intelligence Tools", "Predictive Analytics", "AI in Business Strategy"]
}
levels = ['Beginner','Intermediate','Advanced']
Question_Difficulty = ['Easy','Medium','Hard']
st.header("Select AI:")
model = st.radio("Model", [ "Gemini","Open AI",])
st.write("Selected option:", model)
# Header and description
st.title("Interview Practice Bot 📚")
st.text("Choose the role and topic for your Interview.")
# User input for quiz generation
## Layout in columns
col4, col1, col2 = st.columns([1, 1, 1])
col5, col3 = st.columns([1, 1])
with col4:
selected_level = st.selectbox('Select level of understanding', levels)
with col1:
selected_topic_level = st.selectbox('Select Role', list(roles_and_topics.keys()))
with col2:
selected_topic = st.selectbox('Select Topic', roles_and_topics[selected_topic_level])
with col5:
selected_Question_Difficulty = st.selectbox('Select Question Difficulty', Question_Difficulty)
with col3:
num_quizzes = st.slider('Number of Questions', min_value=1, max_value= 10, value=1)
submit = st.button('Generate Questions')
st.write(selected_topic_level, selected_topic, num_quizzes, selected_Question_Difficulty, selected_level, model)
# Final Response
if submit:
questions,answers = GetLLMResponse(selected_topic_level, selected_topic, num_quizzes, selected_Question_Difficulty, selected_level, model)
with st.spinner("Generating Quizzes..."):
questions,answers = GetLLMResponse(selected_topic_level, selected_topic, num_quizzes, selected_Question_Difficulty, selected_level, model)
st.success("Quizzes Generated!")
# Display questions and answers in a table
if questions:
st.subheader("Quiz Questions and Answers:")
# Prepare data for the table
col1, col2 = st.columns(2)
with col1:
st.subheader("Questions")
st.write(questions)
with col2:
st.subheader("Answers")
st.write(answers)
else:
st.warning("No Quiz Questions and Answers")
else:
st.warning("Click the 'Generate Quizzes' button to create quizzes.")
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