import gradio as gr
import hopsworks
import joblib
import pandas as pd

features =  ['work_year',
             'experience_level',
             'company_size',
             'eur',
             'gbp',
             'usd',
             'engineer',
             'scientist',
             'research',
             'analyst',
             'analytics_engineer',
             'applied_scientist',
             'bi_developer',
             'business_intelligence_analyst',
             'business_intelligence_engineer',
             'data_analyst',
             'data_architect',
             'data_engineer',
             'data_manager',
             'data_science_consultant',
             'data_science_manager',
             'data_scientist',
             'ml_engineer',
             'machine_learning_engineer',
             'machine_learning_scientist',
             'research_analyst',
             'research_engineer',
             'research_scientist',
             'gdp',
             'cpi']


labels = ['(16454.999, 122000.0]', '(122000.0, 170000.0]', '(170000.0, 329700.0]']

project = hopsworks.login()
fs = project.get_feature_store()

mr = project.get_model_registry()
model = mr.get_model("salary_model", version=5)
model_dir = model.download()
model = joblib.load(model_dir + "/model.pkl")
print("Model downloaded")

import requests

def get_gdp_by_country_code(country_code, year=2023, index='FP.CPI.TOTL'):
    # World Bank API endpoint for GDP data
    api_url = f'http://api.worldbank.org/v2/country/{country_code}/indicator/{index}?data={year}&format=json'


    # Make a GET request to the API
    response = requests.get(api_url)

    # Check if the request was successful (status code 200)
    if response.status_code == 200:
        # Parse the JSON response
        data = response.json()

        # Extract the GDP value from the response
        gdp_value = data[1][0]['value'] if data[1] else None

        return gdp_value
    else:
        # If the request was not successful, print an error message
        print(f"Error: Unable to fetch data. Status code: {response.status_code}")
        return None

def salary(work_year,
             experience_level,
             company_size,
             currency,
             job_title,
             country)-> str:

    other_param = {}

    other_param['gdp'] = get_gdp_by_country_code(country, work_year, 'NY.GDP.MKTP.CD')
    other_param['cpi'] = get_gdp_by_country_code(country, work_year, 'FP.CPI.TOTL')

    jobs =   ['analytics_engineer',
             'applied_scientist',
             'bi_developer',
             'business_intelligence_analyst',
             'business_intelligence_engineer',
             'data_analyst',
             'data_architect',
             'data_engineer',
             'data_manager',
             'data_science_consultant',
             'data_science_manager',
             'data_scientist',
             'ml_engineer',
             'machine_learning_engineer',
             'machine_learning_scientist',
             'research_analyst',
             'research_engineer',
             'research_scientist']

    jobs_flag ={}

    for name in jobs:
        if name == job_title.lower().replace(' ', '_'):
            jobs_flag[name] = True
        else:
            jobs_flag[name] = False

    role = [
        'engineer',
        'scientist',
        'research',
        'analyst'
    ]

    role_flag = {}

    for name in role:
        if role in job_title.lower():
            role_flag[name]= True
        else:
            role_flag[name] = False

    currency_flag = {
        'eur': False,
        'gbp': False,
        'usd': False
    }

    currency_flag[currency.lower()] = True

    company_size_dic = {
        'S': 0,
        'M': 1,
        'L': 2,
    }

    other_param['company_size'] = company_size_dic[company_size]

    experience_level_map = {
        'EN': 0,
        'MI': 1,
        'SE': 2,
        'EX': 3
    }

    other_param['experience_level'] = experience_level_map[experience_level]

    params = {}

    params.update(other_param)
    params.update(jobs_flag)
    params.update(currency_flag)
    params.update(role_flag)


    df = pd.DataFrame(params)
    print("Predicting")
    print(df)

    res = model.predict(df)

    print(f"{labels[res[0]]} $")
    return f"{labels[res[0]]} $"

job_title_options = [
        'Analytics Engineer', 'Applied Scientist', 'BI Developer',
        'Business Intelligence Analyst', 'Business Intelligence Engineer',
        'Data Analyst', 'Data Architect', 'Data Engineer', 'Data Manager',
        'Data Science Consultant', 'Data Science Manager', 'Data Scientist',
        'ML Engineer', 'Machine Learning Engineer', 'Machine Learning Scientist',
        'Research Analyst', 'Research Engineer', 'Research Scientist'
    ]

demo = gr.Interface(
    fn=salary,
    title="Salary prediction",
    description="Prediction of the salary in USD",
    allow_flagging="never",
    inputs=[
        gr.components.Number(label='work_year'),
        gr.components.Select(label='experience_level', options=['EN', 'MI', 'SE', 'EX']),
        gr.components.Select(label='company_size', options=['S', 'M', 'L']),
        gr.components.Select(label='currency', options=['EUR', 'GBP', 'USD']),
        gr.components.Select(label='job_title', options=job_title_options),
        gr.components.TextInput(label='country')
    ],
    outputs=gr.Text())

demo.launch(debug=True)