AI-Powered Job Finder
', unsafe_allow_html=True) st.markdown('Upload your resume and find relevant jobs
', unsafe_allow_html=True) # Initialize session state variables if 'resume_text' not in st.session_state: st.session_state.resume_text = "" if 'resume_parsed' not in st.session_state: st.session_state.resume_parsed = False if 'parsed_data' not in st.session_state: st.session_state.parsed_data = {} if 'job_results' not in st.session_state: st.session_state.job_results = [] if 'search_completed' not in st.session_state: st.session_state.search_completed = False if 'suggested_job_roles' not in st.session_state: st.session_state.suggested_job_roles = [] if 'jobs_by_role' not in st.session_state: st.session_state.jobs_by_role = {} # Define the JSON schema for resume parsing RESUME_SCHEMA = { "schema": { "basic_info": { "name": "string", "email": "string", "phone": "string", "location": "string" }, "professional_summary": "string", "skills": ["string"], "technical_skills": ["string"], "soft_skills": ["string"], "experience": [{ "job_title": "string", "company": "string", "duration": "string", "description": "string" }], "education": [{ "degree": "string", "institution": "string", "year": "string" }], "certifications": ["string"], "years_of_experience": "number" } } # Function to extract text from PDF def extract_text_from_pdf(pdf_file): pdf_reader = PyPDF2.PdfReader(pdf_file) text = "" for page_num in range(len(pdf_reader.pages)): text += pdf_reader.pages[page_num].extract_text() return text # Function to parse resume with Gemini def parse_resume_with_gemini(resume_text): try: # Configure the Gemini API client = genai.Client(api_key=os.getenv('GEMINI_API_KEY')) # Construct the prompt with schema prompt = f""" Parse the following resume text and extract information according to this exact JSON schema: {json.dumps(RESUME_SCHEMA, indent=2)} Resume text: {resume_text} Make sure to follow the schema exactly. If any information is not available, use empty strings or empty arrays as appropriate. Return ONLY the JSON object with no additional text. """ # Get the model # Generate the response response = client.models.generate_content(model="gemini-2.0-flash", contents=prompt) # Parse the response to get JSON try: parsed_data = json.loads(response.text) return parsed_data except json.JSONDecodeError: # Try to extract JSON from the text if not directly parseable import re json_match = re.search(r'```json\n(.*?)\n```', response.text, re.DOTALL) if json_match: return json.loads(json_match.group(1)) else: st.error("Could not parse the response as JSON") return RESUME_SCHEMA["schema"] except Exception as e: st.error(f"Error parsing resume: {str(e)}") return RESUME_SCHEMA["schema"] # Function to search for jobs def search_jobs(query, location="", page=1): try: conn = http.client.HTTPSConnection("jsearch.p.rapidapi.com") # Format the query string search_query = query.replace(" ", "%20") if location: search_query += f"%20in%20{location.replace(' ', '%20')}" headers = { 'X-RapidAPI-Key': os.getenv('RAPIDAPI_KEY'), 'X-RapidAPI-Host': "jsearch.p.rapidapi.com" } conn.request("GET", f"/search?query={search_query}&page={page}&num_pages=1", headers=headers) res = conn.getresponse() data = res.read() return json.loads(data.decode("utf-8")) except Exception as e: st.error(f"Error searching for jobs: {str(e)}") return {"data": []} if 'filter_remote_only' not in st.session_state: st.session_state.filter_remote_only = False if 'filter_employment_types' not in st.session_state: st.session_state.filter_employment_types = [] if 'filter_date_posted' not in st.session_state: st.session_state.filter_date_posted = 0 if 'min_salary' not in st.session_state: st.session_state.min_salary = 0 if 'max_salary' not in st.session_state: st.session_state.max_salary = 1000000 if 'filter_company_types' not in st.session_state: st.session_state.filter_company_types = [] # Function to apply filters to job results def apply_filters(jobs): filtered_jobs = [] for job in jobs: # Check remote filter if st.session_state.filter_remote_only and not job.get('job_is_remote', False): continue # Check employment type filter if st.session_state.filter_employment_types and job.get('job_employment_type') not in st.session_state.filter_employment_types: continue # Check date posted filter (in days) if st.session_state.filter_date_posted > 0: current_time = int(time.time()) posted_time = job.get('job_posted_at_timestamp', 0) days_ago = (current_time - posted_time) / (60 * 60 * 24) if days_ago > st.session_state.filter_date_posted: continue # Check salary filter if job.get('job_min_salary') is not None and job.get('job_min_salary') < st.session_state.min_salary: continue if job.get('job_max_salary') is not None and job.get('job_max_salary') > st.session_state.max_salary: continue # Check company type filter if st.session_state.filter_company_types and job.get('employer_company_type') not in st.session_state.filter_company_types: continue # All filters passed, add job to filtered results filtered_jobs.append(job) return filtered_jobs # Function to generate job role suggestions based on skills using Gemini def suggest_job_roles(skills): try: # Configure the Gemini API client = genai.Client(api_key=os.getenv('GEMINI_API_KEY')) # Construct the prompt for job role suggestions prompt = f""" Based on the following skills extracted from a resume, suggest 3-5 relevant job roles that this person could apply for. Skills: {', '.join(skills)} Return only a JSON array of strings with the job role titles. For example: ["Software Developer", "Data Engineer", "DevOps Engineer"] Make the job roles specific and relevant to the skills provided. """ # Generate the response response = client.models.generate_content(model="gemini-2.0-flash", contents=prompt) # Parse the response to get job roles try: # Try to parse as direct JSON job_roles = json.loads(response.text) return job_roles except json.JSONDecodeError: # Try to extract JSON from text import re json_match = re.search(r'\[.*\]', response.text, re.DOTALL) if json_match: return json.loads(json_match.group(0)) else: # Fallback st.warning("Could not automatically generate job roles. Using default suggestions.") return ["Software Developer", "Data Analyst", "Project Manager"] except Exception as e: st.error(f"Error suggesting job roles: {str(e)}") return ["Software Developer", "Data Analyst", "Project Manager"] # Function to search for jobs with multiple queries def search_jobs_for_roles(job_roles, location="", page=1): all_jobs = {} for role in job_roles: with st.spinner(f'Searching for {role} jobs...'): result = search_jobs(role, location, page) jobs = result.get('data', []) all_jobs[role] = jobs return all_jobs # Resume Upload Section st.subheader("Step 1: Upload Your Resume First") uploaded_file = st.file_uploader("Upload your resume (PDF format)", type=['pdf']) if uploaded_file is not None: with st.spinner('Processing your resume...'): # Extract text from the PDF resume_text = extract_text_from_pdf(uploaded_file) st.session_state.resume_text = resume_text # Parse the resume parsed_data = parse_resume_with_gemini(resume_text) st.session_state.parsed_data = parsed_data st.session_state.resume_parsed = True # Extract all skills tech_skills = parsed_data.get("technical_skills", []) general_skills = parsed_data.get("skills", []) soft_skills = parsed_data.get("soft_skills", []) all_skills = list(set(tech_skills + general_skills + soft_skills)) # Generate job role suggestions if all_skills: st.session_state.suggested_job_roles = suggest_job_roles(all_skills) if st.session_state.resume_parsed: st.markdown("---") st.subheader("Your Resume Information") # Display the parsed information with st.expander("Resume Parsed Information", expanded=True): col1, col2 = st.columns(2) with col1: st.markdown("### Basic Information") basic_info = parsed_data.get("basic_info", {}) st.write(f"**Name:** {basic_info.get('name', 'Not found')}") st.write(f"**Email:** {basic_info.get('email', 'Not found')}") st.write(f"**Phone:** {basic_info.get('phone', 'Not found')}") st.write(f"**Location:** {basic_info.get('location', 'Not found')}") st.markdown("### Experience") for exp in parsed_data.get("experience", []): st.markdown(f"**{exp.get('job_title', 'Role')} at {exp.get('company', 'Company')}**") st.write(f"*{exp.get('duration', 'Duration not specified')}*") st.write(exp.get('description', 'No description available')) st.write("---") with col2: st.markdown("### Skills") # Technical skills st.write("**Technical Skills:**") tech_skills = parsed_data.get("technical_skills", []) if tech_skills: st.write(", ".join(tech_skills)) else: st.write("No technical skills found") # Soft skills st.write("**Soft Skills:**") soft_skills = parsed_data.get("soft_skills", []) if soft_skills: st.write(", ".join(soft_skills)) else: st.write("No soft skills found") # General skills st.write("**General Skills:**") skills = parsed_data.get("skills", []) if skills: st.write(", ".join(skills)) else: st.write("No general skills found") st.markdown("### Education") for edu in parsed_data.get("education", []): st.write(f"**{edu.get('degree', 'Degree')}** - {edu.get('institution', 'Institution')}") st.write(f"*{edu.get('year', 'Year not specified')}*") st.write(f"**Years of Experience:** {parsed_data.get('years_of_experience', 'Not specified')}") st.markdown("---") st.subheader("Step 2: Job Search") # Location input only (job roles are now automated) location = st.text_input("Enter your preferred location (e.g., 'New York', 'Remote')") # Display suggested job roles if available if st.session_state.resume_parsed and st.session_state.suggested_job_roles: st.markdown("### Suggested Job Roles Based on Your Skills") # Add custom roles to session state if not already there if 'custom_job_roles' not in st.session_state: st.session_state.custom_job_roles = [] # Combine suggested and custom roles all_job_roles = list(st.session_state.suggested_job_roles) + list(st.session_state.custom_job_roles) # Create a row with text input and add button col1, col2 = st.columns([3, 1]) with col1: custom_role = st.text_input("Add your own job role", key="custom_role_input") with col2: if st.button("Add Role"): if custom_role and custom_role not in all_job_roles: st.session_state.custom_job_roles.append(custom_role) st.success(f"Added: {custom_role}") # Rerun to update the interface st.rerun() # Display job roles as selectable options (both suggested and custom) selected_roles = st.multiselect( "Select job roles to search for", options=all_job_roles, default=st.session_state.suggested_job_roles ) # Display job roles as selectable options # selected_roles = st.multiselect( # "Select job roles to search for", # options=st.session_state.suggested_job_roles, # default=st.session_state.suggested_job_roles # ) # Add filter options to sidebar st.sidebar.markdown("### Filter Options") # Remote work filter st.sidebar.checkbox("Remote Only", key="filter_remote_only") # Employment type filter employment_types = ["FULLTIME", "PARTTIME", "CONTRACTOR", "INTERN"] st.sidebar.multiselect( "Employment Type", employment_types, default=None, key="filter_employment_types" ) # Date posted filter date_options = { "Any time": 0, "Past 24 hours": 1, "Past week": 7, "Past month": 30 } selected_date = st.sidebar.selectbox( "Date Posted", options=list(date_options.keys()), index=0 ) st.session_state.filter_date_posted = date_options[selected_date] # Salary range filter (only if salary data is available) st.sidebar.markdown("### Salary Range") col1, col2 = st.sidebar.columns(2) with col1: st.number_input("Min ($)", value=0, step=10000, key="min_salary") with col2: st.number_input("Max ($)", value=1000000, step=10000, key="max_salary") # Company type filter company_types = ["Public", "Private", "Nonprofit", "Government", "Startup", "Other"] st.sidebar.multiselect( "Company Type", company_types, default=None, key="filter_company_types" ) # Search button if st.button("Search Jobs"): if selected_roles: with st.spinner('Searching for jobs across selected roles...'): # Search for jobs for each selected role jobs_by_role = search_jobs_for_roles(selected_roles, location) # Store the results in session state st.session_state.jobs_by_role = jobs_by_role st.session_state.search_completed = True else: st.warning("Please select at least one job role") else: # If no resume uploaded or no job roles suggested st.info("Upload your resume first to get AI-suggested job roles based on your skills") # Manual job search fallback search_query = st.text_input("Or enter your job search query manually (e.g., 'Python Developer')") if st.button("Search Jobs"): if search_query: with st.spinner('Searching for jobs...'): # Search for jobs job_results = search_jobs(search_query, location) # Store the results in session state st.session_state.job_results = job_results.get('data', []) st.session_state.jobs_by_role = {search_query: job_results.get('data', [])} st.session_state.search_completed = True else: st.warning("Please enter a search query or upload your resume for job suggestions") # Display Results if st.session_state.search_completed: st.markdown("---") st.subheader("Job Search Results") if st.session_state.jobs_by_role: total_jobs_found = sum(len(jobs) for jobs in st.session_state.jobs_by_role.values()) st.success(f"Found a total of {total_jobs_found} jobs matching your criteria") # Display jobs grouped by role for role, jobs in st.session_state.jobs_by_role.items(): if jobs: # Apply filters to this role's jobs filtered_jobs = apply_filters(jobs) if filtered_jobs: with st.expander(f"📌 {role} Jobs ({len(filtered_jobs)})", expanded=False): st.markdown(f"### {role} Positions") # Calculate skill match percentages if resume is uploaded if st.session_state.resume_parsed: # Extract all skills from resume tech_skills = set(st.session_state.parsed_data.get("technical_skills", [])) general_skills = set(st.session_state.parsed_data.get("skills", [])) soft_skills = set(st.session_state.parsed_data.get("soft_skills", [])) all_skills = tech_skills.union(general_skills).union(soft_skills) # Add match score to each job for job in filtered_jobs: if job.get('job_description'): desc = job.get('job_description', '').lower() matched_skills = [skill for skill in all_skills if skill.lower() in desc] match_percentage = int((len(matched_skills) / max(1, len(all_skills))) * 100) job['match_percentage'] = match_percentage job['matched_skills'] = matched_skills else: job['match_percentage'] = 0 job['matched_skills'] = [] # Sort by match percentage filtered_jobs = sorted(filtered_jobs, key=lambda x: x.get('match_percentage', 0), reverse=True) # Display each job in this role category for job_idx, job in enumerate(filtered_jobs): # Customize job title based on match percentage if resume uploaded if st.session_state.resume_parsed and 'match_percentage' in job: job_title = f"{job_idx+1}. {job.get('job_title', 'Job Title Not Available')} - {job.get('employer_name', 'Company Not Available')} " job_title += f"[Match: {job.get('match_percentage')}%]" else: job_title = f"{job_idx+1}. {job.get('job_title', 'Job Title Not Available')} - {job.get('employer_name', 'Company Not Available')}" # Use a container instead of an expander to avoid nesting job_container = st.container() # Add a visual separator between jobs st.markdown("---") # Display job title with formatted styling job_container.markdown(f"#### {job_title}") # Create columns for job details cols = job_container.columns([2, 1]) with cols[0]: # Job details st.write(f"**Company:** {job.get('employer_name', 'Not Available')}") st.write(f"**Location:** {job.get('job_city', 'Not Available')}, {job.get('job_country', 'Not Available')}") st.write(f"**Employment Type:** {job.get('job_employment_type', 'Not Available')}") # Remote information st.write(f"**Remote:** {'Yes' if job.get('job_is_remote') else 'No'}") # Date posted and expiration if job.get('job_posted_at_datetime_utc'): st.write(f"**Posted:** {job.get('job_posted_at_datetime_utc', 'Not Available')}") # Salary information if job.get('job_min_salary') and job.get('job_max_salary'): st.write(f"**Salary Range:** ${job.get('job_min_salary', 'Not Available')} - ${job.get('job_max_salary', 'Not Available')} {job.get('job_salary_currency', 'USD')}") with cols[1]: # Enhanced skills match section if st.session_state.resume_parsed: match_percentage = job.get('match_percentage', 0) matched_skills = job.get('matched_skills', []) # Create a visual progress bar for match percentage st.markdown("### Skills Match") # Color coding based on match percentage if match_percentage > 70: bar_color = "green" elif match_percentage > 40: bar_color = "orange" else: bar_color = "red" # Display progress bar st.progress(match_percentage / 100) st.markdown(f"