app.py

# 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()






import openai
import streamlit as st

# Set your OpenAI API key
openai.api_key = "YOUR_OPENAI_API_KEY"

def generate_question(role, topic, difficulty_level):
    prompt = f"Generate an interview question for the role of {role} on the topic of {topic} with difficulty level {difficulty_level}."
    response = openai.Completion.create(
        engine="text-davinci-003",  # or any other engine you prefer
        prompt=prompt,
        max_tokens=50
    )
    return response.choices[0].text.strip()

def evaluate_answer(question, user_answer):
    prompt = f"Question: {question}\nUser's Answer: {user_answer}\nEvaluate the answer and provide feedback. Also, provide the best possible answer."
    response = openai.Completion.create(
        engine="text-davinci-003",
        prompt=prompt,
        max_tokens=150
    )
    return response.choices[0].text.strip()

st.title("Mock Interview Bot")

role = st.selectbox("Select the role:", ["Software Engineer", "Data Scientist", "Product Manager"])
topic = st.text_input("Enter the topic:")
difficulty_level = st.selectbox("Select difficulty level:", ["Easy", "Medium", "Hard"])

if st.button("Generate Question"):
    if role and topic and difficulty_level:
        question = generate_question(role, topic, difficulty_level)
        st.session_state['current_question'] = question
        st.write(f"Question: {question}")
        st.session_state['question_answered'] = False

if 'current_question' in st.session_state and not st.session_state.get('question_answered', False):
    answer = st.text_area("Your Answer:")
    if st.button("Submit Answer"):
        if answer:
            st.session_state['user_answer'] = answer
            st.session_state['question_answered'] = True

if 'user_answer' in st.session_state:
    with st.spinner("Evaluating your answer..."):
        feedback = evaluate_answer(st.session_state['current_question'], st.session_state['user_answer'])
        st.write(f"Feedback and Best Answer:\n{feedback}")