app.py

import gradio.inputs
import gradio as gr

import pickle
import pandas as pd
import sklearn
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import OrdinalEncoder
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.compose import make_column_selector as selector
#from sklearn.ensemble import HistGradientBoostingClassifier


with open('./model_leads.pck', 'rb') as f:
    model = pickle.load(f)

labels = ["No, no será cliente", "Sí, será un cliente"]

def convert(lead_origin, lead_source, do_not_email, totalvisits,total_time_spent_on_website, page_views_per_visit, last_activity, 
                     specialization, what_is_your_current_occupation, tags, city, a_free_copy_of_mastering_the_interview, last_notable_activity ):
    #Se crea un dataframe con los datos enviados
    df = pd.DataFrame()
    df['lead_origin'] = [lead_origin]
    df['lead_source'] = [lead_source]
    df['do_not_email'] = [do_not_email]
    df['totalvisits'] = [totalvisits]
    df['total_time_spent_on_website'] = [total_time_spent_on_website]
    df['page_views_per_visit'] = [page_views_per_visit]
    df['last_activity'] = [last_activity]
    df['specialization'] = [specialization]
    df['what_is_your_current_occupation'] = [what_is_your_current_occupation]
    df['tags'] = [tags]
    df['city'] = [city]
    df['a_free_copy_of_mastering_the_interview'] = [a_free_copy_of_mastering_the_interview]
    df['last_notable_activity'] = [last_notable_activity]
    #El modelo hace su predicción
    prediction = model.predict_proba(df).flatten()
    print(prediction)
    #Se devuelve el percentaje que el modelo ha predicho para cada etiqueta
    return {labels[i]: float(prediction[i]) for i in range(2)}
    #return {"No, no será cliente": prediction[0], "Sí, será un cliente": prediction[1]}


iface = gr.Interface(
    fn=convert, 
    inputs= [
             gr.inputs.Dropdown(["landing_page_submission", "api", "lead_add_form", "lead_import"]),
             gr.inputs.Dropdown(['olark_chat', 'organic_search', 'direct_traffic', 'google', 'referral_sites', 'reference', 'welingak_website', 'social_media' ,'others', 'live_chat']),
             gr.inputs.Checkbox(),
             gr.inputs.Slider(0, 150),
             gr.inputs.Slider(0, 1000),
             gr.inputs.Slider(0, 15),
             gr.inputs.Dropdown(['page_visited_on_website', 'email_opened', 'others', 'converted_to_lead',  'olark_chat_conversation', 'email_bounced', 'email_link_clicked', 'form_submitted_on_website', 'sms_sent']),
             gr.inputs.Dropdown(['not_specified', 'business_administration', 'media_and_advertising', 'management_specializations', 'travel_and_tourism', 'banking,_investment_and_insurance', 'international_business', 'e-commerce', 'services_excellence', 'rural_and_agribusiness', 'e-business']),
             gr.inputs.Dropdown(['unemployed', 'student' ,'working_professional', 'businessman', 'other', 'housewife']),
             gr.inputs.Dropdown(['interested_in_other_courses', 'ringing', 'will_revert_after_reading_the_email', 'not_specified', 'lost_to_eins', 'other_tags', 'busy', 'closed_by_horizzon', 'interested__in_full_time_mba',  'lateral_student']),
             gr.inputs.Dropdown(['mumbai', 'thane_&_outskirts', 'other_metro_cities', 'other_cities',  'other_cities_of_maharashtra', 'tier_ii_cities']),
             gr.inputs.Checkbox(),
             gr.inputs.Dropdown(['modified', 'email_opened', 'page_visited_on_website',  'other_notable_activity', 'email_link_clicked', 'olark_chat_conversation',  'sms_sent']),
    ],
    outputs="label",
    title="¿Se convetirá en cliente?",
    description="Aplicación de aprendizaje automático que predice la probabilidad de que un potencial cliente contrate los servicios de nuestra empresa",
)

iface.launch()