# 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("Mock -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 from langchain_google_genai import ChatGoogleGenerativeAI import re 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}." llm = ChatGoogleGenerativeAI(model="gemini-pro", google_api_key=st.secrets["GOOGLE_API_KEY"]) response = llm.invoke(prompt) response = response.content return response 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." llm = ChatGoogleGenerativeAI(model="gemini-pro", google_api_key=st.secrets["GOOGLE_API_KEY"]) response = llm.invoke(prompt) response = response.content return response # ---------------------- 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}." llm = ChatGoogleGenerativeAI(model="gemini-pro", google_api_key=st.secrets["GOOGLE_API_KEY"]) response = llm.invoke(prompt) response = response.content return response def evaluate_answer(question, user_answer): prompt = f"Question: {question}\nUser's Answer: {user_answer}\nEvaluate the answer, give a score out of 100, and provide feedback. Also, provide the best possible answer." llm = ChatGoogleGenerativeAI(model="gemini-pro", google_api_key=st.secrets["GOOGLE_API_KEY"]) response = llm.invoke(prompt) evaluation = response.content # Extract score and feedback from the evaluation # Extract score using regular expressions score_match = re.search(r'(\d+)/100', evaluation) score = int(score_match.group(1)) if score_match else 0 # Extract feedback feedback = evaluation.split('\n', 1)[1] if '\n' in evaluation else evaluation return score, feedback def generate_report(): st.write("### Interview Report") for i in range(st.session_state['total_questions']): st.write(f"**Question {i+1}:** {st.session_state['questions'][i]}") st.write(f"**Your Answer:** {st.session_state['answers'][i]}") st.write(f"**Score:** {st.session_state['scores'][i]}") st.write(f"**Feedback:** {st.session_state['feedback'][i]}") st.write("---") # Initialize session state if 'questions' not in st.session_state: st.session_state['questions'] = [] if 'answers' not in st.session_state: st.session_state['answers'] = [] if 'feedback' not in st.session_state: st.session_state['feedback'] = [] if 'scores' not in st.session_state: st.session_state['scores'] = [] if 'current_question' not in st.session_state: st.session_state['current_question'] = 0 if 'total_questions' not in st.session_state: st.session_state['total_questions'] = 10 if 'question_answered' not in st.session_state: st.session_state['question_answered'] = False if 'interview_started' not in st.session_state: st.session_state['interview_started'] = False st.title("Mock Interview Bot") if not st.session_state['interview_started']: 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"] } role = st.selectbox('Select Role', list(roles_and_topics.keys())) topic = st.selectbox('Select Topic', roles_and_topics[role]) difficulty_level = st.selectbox("Select difficulty level:", ["Easy", "Medium", "Hard"]) if st.button("Start Interview"): if role and topic and difficulty_level: st.session_state['questions'] = [generate_question(role, topic, difficulty_level) for _ in range(st.session_state['total_questions'])] st.session_state['current_question'] = 0 st.session_state['interview_started'] = True st.session_state['question_answered'] = False if st.session_state['interview_started']: current_question = st.session_state['current_question'] if current_question < st.session_state['total_questions']: st.write(f"Question {current_question + 1}: {st.session_state['questions'][current_question]}") if not st.session_state['question_answered']: answer = st.text_area("Your Answer:", key=f"answer_{current_question}") if st.button("Submit Answer"): if answer: st.session_state['answers'].append(answer) score, feedback = evaluate_answer(st.session_state['questions'][current_question], answer) st.session_state['scores'].append(score) st.session_state['feedback'].append(feedback) st.session_state['question_answered'] = True st.write(f"Score: {score}") st.write(f"Feedback: {feedback}") if st.session_state['question_answered']: if st.button("Next Question"): st.session_state['current_question'] += 1 st.session_state['question_answered'] = False else: st.write("Interview Complete! Generating Report...") generate_report()