import streamlit as st
from function import GetLLMResponse

from langchain_community.llms import OpenAI
from langchain_google_genai import ChatGoogleGenerativeAI

    
# Page configuration
st.set_page_config(page_title="Interview Practice Bot",
                   page_icon="📚",
                   layout="wide",
                   initial_sidebar_state="collapsed")




def main():        
    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.")





    
    


if __name__ == "__main__":
    main()