import gradio as gr import pandas as pd import numpy as np import pickle import sklearn from datasets import load_dataset import joblib import requests # Read the data data = pd.read_csv("mldata.csv") # Function to load model def load_model(): with open('rfweights.pkl', 'rb') as pickleFile: return pickle.load(pickleFile) # Prepare categorical data categorical_cols = data[[ 'certifications', 'workshops', 'Interested subjects', 'interested career area ', 'Type of company want to settle in?', 'Interested Type of Books' ]].copy() # Assign category codes for i in categorical_cols: data[i] = data[i].astype('category') data[i] = data[i].cat.codes # Create reference dictionaries for embeddings def create_embedding_dict(column): unique_names = list(categorical_cols[column].unique()) unique_codes = list(data[column].unique()) return dict(zip(unique_names, unique_codes)) certificates_references = create_embedding_dict('certifications') workshop_references = create_embedding_dict('workshops') subjects_interest_references = create_embedding_dict('Interested subjects') career_interest_references = create_embedding_dict('interested career area ') company_intends_references = create_embedding_dict('Type of company want to settle in?') book_interest_references = create_embedding_dict('Interested Type of Books') # Career-specific job data CAREER_JOB_DATA = { "Software Engineer": [ ["Software Engineer", "Mindtree Ltd", "Bangalore, Karnataka", "₹5,50,000 - ₹11,00,000"], ["Software Developer", "Mphasis", "Pune, Maharashtra", "₹5,00,000 - ₹9,50,000"], ["Full Stack Developer", "Persistent Systems", "Hyderabad, Telangana", "₹6,00,000 - ₹12,00,000"], ["Backend Engineer", "Zensar Technologies", "Mumbai, Maharashtra", "₹5,80,000 - ₹10,50,000"], ["Junior Software Engineer", "Cyient", "Chennai, Tamil Nadu", "₹4,20,000 - ₹7,80,000"] ], "Software Developer": [ ["Software Developer", "LTI (L&T Infotech)", "Bangalore, Karnataka", "₹4,80,000 - ₹9,20,000"], ["Application Developer", "Hexaware Technologies", "Hyderabad, Telangana", "₹5,20,000 - ₹9,80,000"], ["Java Developer", "Birlasoft", "Pune, Maharashtra", "₹5,50,000 - ₹10,50,000"], ["Python Developer", "Sonata Software", "Noida, UP", "₹6,00,000 - ₹11,50,000"], ["Software Engineer Trainee", "Larsen & Toubro Technology", "Mumbai, Maharashtra", "₹3,80,000 - ₹6,50,000"] ], "Web Developer": [ ["Frontend Developer", "Nagarro", "Gurgaon, Haryana", "₹6,50,000 - ₹13,00,000"], ["Full Stack Web Developer", "Publicis Sapient", "Bangalore, Karnataka", "₹7,20,000 - ₹14,50,000"], ["React Developer", "ThoughtWorks", "Pune, Maharashtra", "₹8,00,000 - ₹16,00,000"], ["Web Developer", "Xoriant", "Mumbai, Maharashtra", "₹5,50,000 - ₹11,00,000"], ["UI Developer", "Synechron", "Bangalore, Karnataka", "₹6,80,000 - ₹13,50,000"] ], "Mobile Applications Developer": [ ["Android Developer", "Mindtree Ltd", "Bangalore, Karnataka", "₹7,50,000 - ₹15,00,000"], ["iOS Developer", "Cybage", "Pune, Maharashtra", "₹7,00,000 - ₹14,00,000"], ["Flutter Developer", "QuEST Global", "Bangalore, Karnataka", "₹6,50,000 - ₹13,00,000"], ["React Native Developer", "NIIT Technologies", "Noida, UP", "₹6,00,000 - ₹12,00,000"], ["Mobile App Developer", "iGate (Capgemini)", "Hyderabad, Telangana", "₹5,80,000 - ₹11,50,000"] ], "Database Developer": [ ["Database Developer", "Mastek", "Mumbai, Maharashtra", "₹6,50,000 - ₹13,00,000"], ["SQL Developer", "Virtusa", "Hyderabad, Telangana", "₹7,00,000 - ₹14,00,000"], ["Database Administrator", "Polaris Consulting", "Chennai, Tamil Nadu", "₹6,20,000 - ₹12,50,000"], ["Data Engineer", "Altimetrik", "Bangalore, Karnataka", "₹7,50,000 - ₹15,00,000"], ["Big Data Developer", "Sasken Technologies", "Bangalore, Karnataka", "₹7,80,000 - ₹15,50,000"] ], "Network Security Engineer": [ ["Security Engineer", "Quick Heal Technologies", "Pune, Maharashtra", "₹6,50,000 - ₹13,00,000"], ["Cybersecurity Analyst", "Paladion Networks", "Bangalore, Karnataka", "₹6,00,000 - ₹12,00,000"], ["Network Security Specialist", "K7 Computing", "Chennai, Tamil Nadu", "₹7,00,000 - ₹14,00,000"], ["Information Security Analyst", "SecureKloud", "Chennai, Tamil Nadu", "₹6,80,000 - ₹13,50,000"], ["Security Operations Analyst", "Sequretek", "Bangalore, Karnataka", "₹5,80,000 - ₹11,50,000"] ], "UX Designer": [ ["UX Designer", "Think Design", "Bangalore, Karnataka", "₹5,50,000 - ₹12,00,000"], ["UI/UX Designer", "F5 Studio", "Mumbai, Maharashtra", "₹5,00,000 - ₹11,00,000"], ["Product Designer", "Lollypop Design", "Bangalore, Karnataka", "₹6,00,000 - ₹13,00,000"], ["Visual Designer", "Designit (Wipro)", "Pune, Maharashtra", "₹5,80,000 - ₹12,50,000"], ["UX Researcher", "Happy Marketer", "Gurgaon, Haryana", "₹5,20,000 - ₹11,50,000"] ], "Software Quality Assurance (QA)/ Testing": [ ["QA Engineer", "Cigniti Technologies", "Hyderabad, Telangana", "₹4,20,000 - ₹8,50,000"], ["Software Tester", "TestingXperts", "Mumbai, Maharashtra", "₹3,80,000 - ₹7,80,000"], ["Automation Test Engineer", "Qualitest", "Pune, Maharashtra", "₹5,00,000 - ₹10,00,000"], ["QA Analyst", "QA InfoTech", "Noida, UP", "₹4,50,000 - ₹9,00,000"], ["Test Lead", "Maveric Systems", "Bangalore, Karnataka", "₹6,50,000 - ₹13,00,000"] ], "Technical Support": [ ["Technical Support Engineer", "Happiest Minds", "Bangalore, Karnataka", "₹3,20,000 - ₹6,50,000"], ["IT Support Specialist", "Rolta India", "Mumbai, Maharashtra", "₹2,80,000 - ₹5,80,000"], ["Desktop Support Engineer", "Fujitsu Consulting", "Pune, Maharashtra", "₹3,00,000 - ₹6,00,000"], ["Technical Support Associate", "iYogi Technical Services", "Gurgaon, Haryana", "₹3,50,000 - ₹7,00,000"], ["Help Desk Technician", "CSS Corp", "Chennai, Tamil Nadu", "₹2,80,000 - ₹5,50,000"] ], "Systems Security Administrator": [ ["System Administrator", "Kale Logistics", "Pune, Maharashtra", "₹4,50,000 - ₹9,00,000"], ["Linux Administrator", "Sify Technologies", "Chennai, Tamil Nadu", "₹5,50,000 - ₹11,00,000"], ["Windows System Admin", "Netmagic Solutions", "Mumbai, Maharashtra", "₹5,20,000 - ₹10,50,000"], ["Cloud Administrator", "CtrlS Datacenters", "Hyderabad, Telangana", "₹6,50,000 - ₹13,00,000"], ["DevOps Engineer", "Genpact", "Bangalore, Karnataka", "₹7,00,000 - ₹14,00,000"] ], "Applications Developer": [ ["Application Developer", "3i Infotech", "Mumbai, Maharashtra", "₹5,50,000 - ₹11,00,000"], ["Enterprise App Developer", "Ramco Systems", "Chennai, Tamil Nadu", "₹6,20,000 - ₹12,50,000"], ["Software Application Engineer", "Newgen Software", "Noida, UP", "₹6,50,000 - ₹13,00,000"], ["Business Application Developer", "Aurionpro Solutions", "Mumbai, Maharashtra", "₹5,80,000 - ₹11,50,000"], ["Custom App Developer", "Nucleus Software", "Noida, UP", "₹6,00,000 - ₹12,00,000"] ], "CRM Technical Developer": [ ["Salesforce Developer", "Tech Mahindra", "Pune, Maharashtra", "₹6,50,000 - ₹13,00,000"], ["CRM Developer", "HGS (Hinduja Global)", "Bangalore, Karnataka", "₹6,00,000 - ₹12,00,000"], ["Dynamics 365 Developer", "L&T Technology Services", "Vadodara, Gujarat", "₹6,80,000 - ₹13,50,000"], ["CRM Technical Consultant", "Firstsource Solutions", "Mumbai, Maharashtra", "₹6,20,000 - ₹12,50,000"], ["Salesforce Administrator", "WNS Global Services", "Pune, Maharashtra", "₹5,00,000 - ₹10,00,000"] ] } # Function to fetch job listings def fetch_job_listings(job_title): """Fetch job listings - tries API first, then falls back to curated data""" # Try API first api_key = '714f5a2539msh798d996c3243876p19c71ajsnfcd7ce481cb9' url = "https://jsearch.p.rapidapi.com/search" querystring = { "query": f"{job_title} in India", "page": "1", "num_pages": "1", "date_posted": "all" } headers = { "x-rapidapi-key": api_key, "x-rapidapi-host": "jsearch.p.rapidapi.com" } try: response = requests.get(url, headers=headers, params=querystring, timeout=10) print(f"JSearch API Response Status: {response.status_code}") if response.status_code == 200: job_data = response.json() if job_data.get('data') and len(job_data['data']) > 0: job_listings = [] for job in job_data['data'][:5]: salary = "Not specified" if job.get('job_min_salary') and job.get('job_max_salary'): min_sal = job.get('job_min_salary') max_sal = job.get('job_max_salary') currency = job.get('job_salary_currency', 'INR') if currency == 'INR': salary = f"₹{min_sal:,.0f} - ₹{max_sal:,.0f}" else: salary = f"{currency} {min_sal:,.0f} - {max_sal:,.0f}" elif job.get('job_min_salary'): min_sal = job.get('job_min_salary') currency = job.get('job_salary_currency', 'INR') salary = f"₹{min_sal:,.0f}+" if currency == 'INR' else f"{currency} {min_sal:,.0f}+" location_parts = [] if job.get('job_city'): location_parts.append(job.get('job_city')) if job.get('job_state'): location_parts.append(job.get('job_state')) location = ', '.join(location_parts) if location_parts else job.get('job_country', 'India') job_listings.append([ job.get('job_title', 'N/A'), job.get('employer_name', 'N/A'), location, salary ]) print(f"Successfully fetched {len(job_listings)} real jobs from API") return job_listings except Exception as e: print(f"API failed: {str(e)}, using curated data") # Fallback to curated career-specific data if job_title in CAREER_JOB_DATA: print(f"Using curated data for {job_title}") return CAREER_JOB_DATA[job_title] # Generic fallback return [ [f"{job_title} (Entry Level)", "Various IT Companies", "Bangalore, Karnataka", "₹4,00,000 - ₹8,00,000"], [f"{job_title} (Mid Level)", "Various IT Companies", "Hyderabad, Telangana", "₹7,00,000 - ₹14,00,000"], [f"{job_title} (Senior)", "Various IT Companies", "Pune, Maharashtra", "₹12,00,000 - ₹24,00,000"], [f"{job_title} Intern", "Startups & IT Firms", "Mumbai, Maharashtra", "₹2,00,000 - ₹4,00,000"], ["💡 Job Search", "Check: Naukri, LinkedIn, Indeed", "India (Remote/Onsite)", "Apply to 10+ daily"] ] # Prediction function (modified to return job suggestions) def rfprediction(name, logical_thinking, hackathon_attend, coding_skills, public_speaking_skills, self_learning, extra_course, certificate_code, worskhop_code, read_writing_skill, memory_capability, subject_interest, career_interest, company_intend, senior_elder_advise, book_interest, introvert_extro, team_player, management_technical, smart_hardworker): try: # Load the Random Forest model rfmodel = load_model() # Create DataFrame df = pd.DataFrame({ "logical_thinking": [logical_thinking], "hackathon_attend": [hackathon_attend], "coding_skills": [coding_skills], "public_speaking_skills": [public_speaking_skills], "self_learning": [self_learning], "extra_course": [extra_course], "certificate": [certificate_code], "workshop": [worskhop_code], "read_writing_skills": [ (0 if "poor" in read_writing_skill else 1 if "medium" in read_writing_skill else 2) ], "memory_capability": [ (0 if "poor" in memory_capability else 1 if "medium" in memory_capability else 2) ], "subject_interest": [subject_interest], "career_interest": [career_interest], "company_intend": [company_intend], "senior_elder_advise": [senior_elder_advise], "book_interest": [book_interest], "introvert_extro": [introvert_extro], "team_player": [team_player], "management_technical": [management_technical], "smart_hardworker": [smart_hardworker] }) # Replace string values with numeric representations - FIX for FutureWarning replacement_dict = { "certificate": certificates_references, "workshop": workshop_references, "subject_interest": subjects_interest_references, "career_interest": career_interest_references, "company_intend": company_intends_references, "book_interest": book_interest_references } for col, mapping in replacement_dict.items(): if col in df.columns: df[col] = df[col].map(mapping) # Dummy encoding userdata_list = df.values.tolist() # Management-Technical dummy encoding if df["management_technical"].values[0] == "Management": userdata_list[0].extend([1, 0]) userdata_list[0].remove('Management') elif df["management_technical"].values[0] == "Technical": userdata_list[0].extend([0, 1]) userdata_list[0].remove('Technical') else: return {"Error": 1.0}, [["Error in Management-Technical encoding", "", "", ""]] # Smart-Hard worker dummy encoding if df["smart_hardworker"].values[0] == "smart worker": userdata_list[0].extend([1, 0]) userdata_list[0].remove('smart worker') elif df["smart_hardworker"].values[0] == "hard worker": userdata_list[0].extend([0, 1]) userdata_list[0].remove('hard worker') else: return {"Error": 1.0}, [["Error in Smart-Hard worker encoding", "", "", ""]] # Convert to numpy array for prediction userdata_array = np.array(userdata_list) # Prediction prediction_result_all = rfmodel.predict_proba(userdata_array) # Create result dictionary with probabilities result_list = { "Applications Developer": float(prediction_result_all[0][0]), "CRM Technical Developer": float(prediction_result_all[0][1]), "Database Developer": float(prediction_result_all[0][2]), "Mobile Applications Developer": float(prediction_result_all[0][3]), "Network Security Engineer": float(prediction_result_all[0][4]), "Software Developer": float(prediction_result_all[0][5]), "Software Engineer": float(prediction_result_all[0][6]), "Software Quality Assurance (QA)/ Testing": float(prediction_result_all[0][7]), "Systems Security Administrator": float(prediction_result_all[0][8]), "Technical Support": float(prediction_result_all[0][9]), "UX Designer": float(prediction_result_all[0][10]), "Web Developer": float(prediction_result_all[0][11]), } # Find the top predicted career top_career = max(result_list, key=result_list.get) # Fetch job listings for the top predicted career job_suggestions = fetch_job_listings(top_career) return result_list, job_suggestions except Exception as e: error_msg = f"Error during prediction: {str(e)}" return {"Error": 1.0}, [[error_msg, "", "", ""]] # Lists for dropdown menus cert_list = ["app development", "distro making", "full stack", "hadoop", "information security", "machine learning", "python", "r programming", "shell programming"] workshop_list = ["cloud computing", "data science", "database security", "game development", "hacking", "system designing", "testing", "web technologies"] skill = ["excellent", "medium", "poor"] subject_list = ["cloud computing", "Computer Architecture", "data engineering", "hacking", "IOT", "Management", "networks", "parallel computing", "programming", "Software Engineering"] career_list = ["Business process analyst", "cloud computing", "developer", "security", "system developer", "testing"] company_list = ["BPA", "Cloud Services", "Finance", "Product based", "product development", "SAaS services", "Sales and Marketing", "Service Based", "Testing and Maintainance Services", "Web Services"] book_list = ["Action and Adventure", "Anthology", "Art", "Autobiographies", "Biographies", "Childrens", "Comics","Cookbooks","Diaries","Dictionaries","Drama","Encyclopedias","Fantasy","Guide","Health","History","Horror","Journals","Math","Mystery","Poetry","Prayer books","Religion-Spirituality","Romance","Satire","Science","Science fiction","Self help","Series","Travel","Trilogy"] Choice_list = ["Management", "Technical"] worker_list = ["hard worker", "smart worker"] # Create Gradio interface def create_output_component(): return [ gr.Label(label="Career Probabilities"), gr.Dataframe( headers=["Job Title", "Company", "Location", "Salary"], label="Job Suggestions" ) ] demo = gr.Interface( fn=rfprediction, inputs=[ gr.Textbox(placeholder="What is your name?", label="Name"), gr.Slider(minimum=1, maximum=9, value=3, step=1, label="Are you a logical thinking person?", info="Scale: 1 - 9"), gr.Slider(minimum=0, maximum=6, value=0, step=1, label="Do you attend any Hackathons?", info="Scale: 0 - 6 | 0 - if not attended any"), gr.Slider(minimum=1, maximum=9, value=5, step=1, label="How do you rate your coding skills?", info="Scale: 1 - 9"), gr.Slider(minimum=1, maximum=9, value=3, step=1, label="How do you rate your public speaking skills/confidency?", info="Scale: 1 - 9"), gr.Radio(["Yes", "No"], type="index", label="Are you a self-learning person? *"), gr.Radio(["Yes", "No"], type="index", label="Do you take extra courses in uni (other than IT)? *"), gr.Dropdown(cert_list, label="Select a certificate you took!"), gr.Dropdown(workshop_list, label="Select a workshop you attended!"), gr.Dropdown(skill, label="Select your read and writing skill"), gr.Dropdown(skill, label="Is your memory capability good?"), gr.Dropdown(subject_list, label="What subject you are interested in?"), gr.Dropdown(career_list, label="Which IT-Career do you have interests in?"), gr.Dropdown(company_list, label="Do you have any interested company that you intend to settle in?"), gr.Radio(["Yes", "No"], type="index", label="Do you ever seek any advices from senior or elders? *"), gr.Dropdown(book_list, label="Select your interested genre of book!"), gr.Radio(["Yes", "No"], type="index", label="Are you an Introvert?| No - extrovert *"), gr.Radio(["Yes", "No"], type="index", label="Ever worked in a team? *"), gr.Dropdown(Choice_list, label="Which area do you prefer: Management or Technical?"), gr.Dropdown(worker_list, label="Are you a Smart worker or Hard worker?") ], outputs=create_output_component(), title="AI-Enhanced Career Guidance System" ) # Main execution if __name__ == "__main__": demo.launch(share=False)