Spaces:
Sleeping
Sleeping
| import streamlit as st | |
| import pandas as pd | |
| import sqlite3 | |
| from transformers import pipeline | |
| # Database Setup | |
| conn = sqlite3.connect("career_coach.db", check_same_thread=False) | |
| c = conn.cursor() | |
| c.execute('''CREATE TABLE IF NOT EXISTS users (name TEXT, skills TEXT, experience TEXT, education TEXT, goals TEXT)''') | |
| conn.commit() | |
| # Hugging Face Setup | |
| interview_model = pipeline("text-generation", model="gpt2") # You can replace with a more suitable model | |
| resume_model = pipeline("text-classification", model="distilbert-base-uncased") | |
| # Career Profile Class | |
| class CareerProfile: | |
| def __init__(self, name, skills, experience, education, goals): | |
| self.name = name | |
| self.skills = skills | |
| self.experience = experience | |
| self.education = education | |
| self.goals = goals | |
| def save_to_db(self): | |
| c.execute("INSERT INTO users VALUES (?, ?, ?, ?, ?)", | |
| (self.name, self.skills, self.experience, self.education, self.goals)) | |
| conn.commit() | |
| # Job Recommender Class | |
| class JobRecommender: | |
| def __init__(self, user_skills): | |
| self.user_skills = user_skills | |
| self.jobs_df = pd.read_csv("jobs_data.csv") # Load job dataset | |
| def recommend_jobs(self): | |
| return self.jobs_df[self.jobs_df['Skills'].str.contains(self.user_skills, case=False, na=False)].head(5) | |
| # Interview Coach Class | |
| class InterviewCoach: | |
| def __init__(self, question): | |
| self.question = question | |
| def get_feedback(self, response): | |
| prompt = f"Evaluate the following response for a job interview question '{self.question}': {response}" | |
| result = interview_model(prompt, max_length=200, num_return_sequences=1) | |
| return result[0]['generated_text'] | |
| # Resume Enhancer Class | |
| class ResumeEnhancer: | |
| def __init__(self, resume_text): | |
| self.resume_text = resume_text | |
| def analyze_resume(self): | |
| prompt = f"Analyze this resume and suggest improvements for ATS compatibility: {self.resume_text}" | |
| result = resume_model(prompt) | |
| return result[0]['label'] # Assuming the model returns the label for the analysis | |
| # Streamlit UI | |
| st.sidebar.title("Virtual Career Coach") | |
| page = st.sidebar.radio("Navigation", ["Home", "Job Recommendations", "Interview Coach", "Resume Enhancer", "Career Growth"]) | |
| if page == "Home": | |
| st.title("Career Profile Setup") | |
| name = st.text_input("Name") | |
| skills = st.text_area("Skills") | |
| experience = st.text_area("Experience") | |
| education = st.text_area("Education") | |
| goals = st.text_area("Career Goals") | |
| if st.button("Save Profile"): | |
| profile = CareerProfile(name, skills, experience, education, goals) | |
| profile.save_to_db() | |
| st.success("Profile saved successfully!") | |
| elif page == "Job Recommendations": | |
| st.title("Job Recommendations") | |
| user_skills = st.text_input("Enter your skills for job matching") | |
| if st.button("Find Jobs"): | |
| recommender = JobRecommender(user_skills) | |
| jobs = recommender.recommend_jobs() | |
| st.write(jobs) | |
| elif page == "Interview Coach": | |
| st.title("AI-Powered Interview Coach") | |
| question = st.text_input("Enter a job interview question") | |
| response = st.text_area("Your Response") | |
| if st.button("Get Feedback"): | |
| coach = InterviewCoach(question) | |
| feedback = coach.get_feedback(response) | |
| st.write(feedback) | |
| elif page == "Resume Enhancer": | |
| st.title("Resume & Cover Letter Enhancer") | |
| resume_text = st.text_area("Paste your resume text") | |
| if st.button("Analyze Resume"): | |
| enhancer = ResumeEnhancer(resume_text) | |
| feedback = enhancer.analyze_resume() | |
| st.write(feedback) | |
| elif page == "Career Growth": | |
| st.title("Career Growth Suggestions") | |
| st.write("Coming Soon: Personalized Learning Paths, Networking Strategies, and Industry Insights!") | |