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| # 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 based on selection | |
| # def load_model(model_choice): | |
| # if model_choice == "Random Forest": | |
| # with open('rfweights (1).pkl', 'rb') as pickleFile: | |
| # return pickle.load(pickleFile) | |
| # elif model_choice == "Decision Tree": | |
| # with open('dtreeweights.pkl', 'rb') as pickleFile: | |
| # return pickle.load(pickleFile) | |
| # else: | |
| # raise ValueError("Invalid model selection") | |
| # # Prepare categorical data (same as original code) | |
| # categorical_cols = data[[ | |
| # 'certifications', | |
| # 'workshops', | |
| # 'Interested subjects', | |
| # 'interested career area ', | |
| # 'Type of company want to settle in?', | |
| # 'Interested Type of Books' | |
| # ]] | |
| # # 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 (same as original code) | |
| # 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') | |
| # # Prediction function (modified to accept model choice) | |
| # def rfprediction(model_choice, 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): | |
| # # Load the selected model | |
| # rfmodel = load_model(model_choice) | |
| # # Create DataFrame (same as original code) | |
| # df = pd.DataFrame.from_dict( | |
| # { | |
| # "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 | |
| # df = df.replace({ | |
| # "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 | |
| # }) | |
| # # Dummy encoding (same as original code) | |
| # userdata_list = df.values.tolist() | |
| # # Management-Technical dummy encoding | |
| # if(df["management_technical"].values == "Management"): | |
| # userdata_list[0].extend([1]) | |
| # userdata_list[0].extend([0]) | |
| # userdata_list[0].remove('Management') | |
| # elif(df["management_technical"].values == "Technical"): | |
| # userdata_list[0].extend([0]) | |
| # userdata_list[0].extend([1]) | |
| # userdata_list[0].remove('Technical') | |
| # else: | |
| # return "Error in Management-Technical encoding" | |
| # # Smart-Hard worker dummy encoding | |
| # if(df["smart_hardworker"].values == "smart worker"): | |
| # userdata_list[0].extend([1]) | |
| # userdata_list[0].extend([0]) | |
| # userdata_list[0].remove('smart worker') | |
| # elif(df["smart_hardworker"].values == "hard worker"): | |
| # userdata_list[0].extend([0]) | |
| # userdata_list[0].extend([1]) | |
| # userdata_list[0].remove('hard worker') | |
| # else: | |
| # return "Error in Smart-Hard worker encoding" | |
| # # Prediction | |
| # prediction_result_all = rfmodel.predict_proba(userdata_list) | |
| # # Create result dictionary | |
| # 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]), | |
| # } | |
| # return result_list | |
| # # Lists for dropdown menus (same as original code) | |
| # 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 (modified to include model selection) | |
| # demo = gr.Interface( | |
| # fn=rfprediction, | |
| # inputs=[ | |
| # gr.Dropdown(["Random Forest", "Decision Tree"], label="Select Machine Learning Model"), | |
| # 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=gr.Label(num_top_classes=5), | |
| # title="IT-Career Recommendation System: TMI4033 Colletive Intelligence, Group 12", | |
| # description="Members: Derrick Lim Kin Yeap 74597, Jason Jong Sheng Tat 75125, Jason Ng Yong Xing 75127, Muhamad Hazrie Bin Suhkery 73555 " | |
| # ) | |
| # url = "https://jobs-api14.p.rapidapi.com/v2/list" | |
| # querystring = { | |
| # "query":"Web Developer", | |
| # "location":"India", | |
| # "autoTranslateLocation":"false", | |
| # "remoteOnly":"false", | |
| # "employmentTypes":"fulltime;parttime;intern;contractor" | |
| # } | |
| # headers = { | |
| # "x-rapidapi-key": "714f5a2539msh798d996c3243876p19c71ajsnfcd7ce481cb9", | |
| # "x-rapidapi-host": "jobs-api14.p.rapidapi.com" | |
| # } | |
| # # Main execution | |
| # if __name__ == "__main__": | |
| # # Fetch job listings before launching the app | |
| # try: | |
| # response = requests.get(url, headers=headers, params=querystring) | |
| # job_listings = response.json() | |
| # print("Job Listings Retrieved Successfully") | |
| # # You could potentially store or process job_listings here | |
| # except requests.RequestException as e: | |
| # print(f"Error fetching job listings: {e}") | |
| # demo.launch(share=True) | |
| 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 based on selection | |
| def load_model(model_choice): | |
| if model_choice == "Random Forest": | |
| with open('rfweights (1).pkl', 'rb') as pickleFile: | |
| return pickle.load(pickleFile) | |
| elif model_choice == "Decision Tree": | |
| with open('dtreeweights.pkl', 'rb') as pickleFile: | |
| return pickle.load(pickleFile) | |
| else: | |
| raise ValueError("Invalid model selection") | |
| # Prepare categorical data | |
| categorical_cols = data[[ | |
| 'certifications', | |
| 'workshops', | |
| 'Interested subjects', | |
| 'interested career area ', | |
| 'Type of company want to settle in?', | |
| 'Interested Type of Books' | |
| ]] | |
| # 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') | |
| # Function to fetch job listings | |
| def fetch_job_listings(job_title): | |
| url = "https://jobs-api14.p.rapidapi.com/v2/list" | |
| querystring = { | |
| "query": job_title, | |
| "location": "India", | |
| "autoTranslateLocation": "false", | |
| "remoteOnly": "false", | |
| "employmentTypes": "fulltime;parttime;intern;contractor" | |
| } | |
| headers = { | |
| "x-rapidapi-key": "714f5a2539msh798d996c3243876p19c71ajsnfcd7ce481cb9", | |
| "x-rapidapi-host": "jobs-api14.p.rapidapi.com" | |
| } | |
| try: | |
| response = requests.get(url, headers=headers, params=querystring) | |
| job_data = response.json() | |
| # Process and format job listings | |
| if job_data.get('jobs'): | |
| job_listings = [] | |
| for job in job_data['jobs'][:5]: # Limit to 5 job listings | |
| job_listings.append([ | |
| job.get('title', 'N/A'), | |
| job.get('company', 'N/A'), | |
| job.get('location', 'N/A'), | |
| job.get('salary', 'Not specified') | |
| ]) | |
| return job_listings | |
| else: | |
| return [['No job listings', 'found', 'for this', 'career path']] | |
| except requests.RequestException as e: | |
| return [['Error', 'fetching', 'job listings', str(e)]] | |
| # Prediction function (modified to return job suggestions) | |
| def rfprediction(model_choice, 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): | |
| # Load the selected model | |
| rfmodel = load_model(model_choice) | |
| # Create DataFrame | |
| df = pd.DataFrame.from_dict( | |
| { | |
| "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 | |
| df = df.replace({ | |
| "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 | |
| }) | |
| # Dummy encoding | |
| userdata_list = df.values.tolist() | |
| # Management-Technical dummy encoding | |
| if(df["management_technical"].values == "Management"): | |
| userdata_list[0].extend([1]) | |
| userdata_list[0].extend([0]) | |
| userdata_list[0].remove('Management') | |
| elif(df["management_technical"].values == "Technical"): | |
| userdata_list[0].extend([0]) | |
| userdata_list[0].extend([1]) | |
| userdata_list[0].remove('Technical') | |
| else: | |
| return "Error in Management-Technical encoding" | |
| # Smart-Hard worker dummy encoding | |
| if(df["smart_hardworker"].values == "smart worker"): | |
| userdata_list[0].extend([1]) | |
| userdata_list[0].extend([0]) | |
| userdata_list[0].remove('smart worker') | |
| elif(df["smart_hardworker"].values == "hard worker"): | |
| userdata_list[0].extend([0]) | |
| userdata_list[0].extend([1]) | |
| userdata_list[0].remove('hard worker') | |
| else: | |
| return "Error in Smart-Hard worker encoding" | |
| # Prediction | |
| prediction_result_all = rfmodel.predict_proba(userdata_list) | |
| # 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 | |
| # 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.Dropdown(["Random Forest", "Decision Tree"], label="Select Machine Learning Model"), | |
| 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=True) |