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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)