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app.py

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+  
+#Deployement avec Gradio
+#fonctions de predictions
+import gradio as gr
+import joblib
+import pandas as pd
+import numpy as np
+#importer les encoders
+encoder0 = joblib.load('job.joblib')
+encoder1 = joblib.load('marital.joblib')
+encoder2 = joblib.load('education.joblib')
+encoder3 = joblib.load('housing.joblib')
+encoder4 = joblib.load('loan.joblib')
+encoder5 = joblib.load('contact.joblib')
+encoder6 = joblib.load('month.joblib')
+encoder7 = joblib.load('day_of_week.joblib')
+encoder8 = joblib.load('poutcome.joblib')
+
+#Importer les listes 
+
+job = joblib.load('job_list.joblib')
+marital = joblib.load('marital_list.joblib')
+education = joblib.load('education_list.joblib')
+housing = joblib.load('housing_list.joblib')
+loan = joblib.load('loan_list.joblib')
+contact = joblib.load('contact_list.joblib')
+month = joblib.load('month_list.joblib')
+day_of_week = joblib.load('day_of_week_list.joblib')
+poutcome = joblib.load('poutcome_list.joblib')
+y = joblib.load('y_list.joblib')
+
+
+#importer le model
+gb_model = joblib.load('gb_model.joblib')
+#importer le normaliser
+scaler = joblib.load('scaler.joblib')
+
+def pred_fun(age,job,marital,education,housing,loan,contact,month,day_of_week,duration,campaign,pdays,previous,poutcome):
+    #Encoder les variables marque, transmission, quartier
+    job = encoder0.transform([job])[0]
+    marital = encoder1.transform([marital])[0]
+    education = encoder2.transform([education])[0]
+    housing = encoder3.transform([housing])[0]
+    loan = encoder4.transform([loan])[0]
+    contact = encoder5.transform([contact])[0]
+    month = encoder6.transform([month])[0]
+    day_of_week = encoder7.transform([day_of_week])[0]
+    poutcome = encoder8.transform([poutcome])[0]
+    
+    
+    
+    #vecteurs des valeurs numerique
+    x_new = np.array( (age,job,marital,education,housing,loan,contact,month,day_of_week,duration,campaign,pdays,previous,poutcome))
+    x_new = x_new.reshape(1, -1)
+    #normaliser les données
+    x_new = scaler.transform(x_new)
+    #predire
+    y_pred = gb_model.predict(x_new)
+    #arrondir
+    y_pred = round(y_pred[0], 2)
+    return f"{'Yes'if y_pred == 1 else 'no'}"
+    
+
+def pred_fun_csv(file):
+    #lire le fichier csv
+    df = pd.read_csv(file)
+    predictions = []
+    for row in df.iloc[:, :].values:
+        # new_row = np.array([
+        #     encoder0.transform([row[0]])[0],
+        #     row[1],
+        #     encoder1.transform([row[2]])[0],
+        #     row[3],
+        #     encoder2.transform([row[4]])[0]
+        #     ])
+        # new_row = new_row.reshape(1, -1)
+        # new_row = scaler.transform(new_row)
+        
+        # y_pred = gb_model.predict(new_row)
+        predictions.append(pred_fun(row[0], row[1], row[2], row[3], row[4], row[5], row[6], row[7], row[8], row[9], row[10], row[11], row[12], row[13]))
+    df['y'] = predictions
+    df.to_csv('predictions.csv', index = False)
+    return 'predictions.csv'
+    
+    
+demo = gr.Blocks(theme = gr.themes.Monochrome())
+#creer les inputes
+inputs = [
+    gr.Number(label = 'age'),
+    gr.Dropdown(choices = job, label = 'job'),
+    gr.Dropdown(choices = marital, label = 'martial'),
+    gr.Dropdown(choices = education, label = 'education'),
+    gr.Dropdown(choices = housing, label = 'housing'),
+    gr.Dropdown(choices = loan, label = 'loan'),
+    gr.Dropdown(choices = contact, label = 'contact'),
+    gr.Dropdown(choices = month, label = 'month'),
+    gr.Dropdown(choices = day_of_week, label = 'day_of_week'),
+    gr.Number(label = 'duration'),
+    gr.Number(label = 'campaign'),
+    gr.Number(label = 'pdays'),
+    gr.Number(label = 'previous'),
+    gr.Dropdown(choices = poutcome, label = 'poutcome')  
+]
+
+Outputs = gr.Textbox(label = 'Etat')
+
+interface1 = gr.Interface(
+    fn = pred_fun,
+    inputs = inputs,
+    outputs = Outputs,
+    title = "Saisir les donnees",
+    description = """Cette modele predi si un client va s'ouscrire ou non a partir de quelques informations"""
+)
+
+interface2 = gr.Interface(
+    fn = pred_fun_csv,
+    inputs = gr.File(label = 'Televerser le fichier csv'),
+    outputs = gr.File(label = 'Telecharger le ficher csv'),
+    title = "Televerser un fichier csv",
+    description = """Cette modele predi si une voiture est une voiture d'occasion ou bien si elle est Venante a partir de quelques informations"""
+)
+
+with demo:
+    gr.TabbedInterface([interface1, interface2], ['simple prediction', 'multiple predictions'])
+    
+demo.launch()

contact.joblib

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contact_list.joblib

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day_of_week.joblib

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day_of_week_list.joblib

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education.joblib

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education_list.joblib

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gb_model.joblib

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housing.joblib

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housing_list.joblib

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job.joblib

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job_list.joblib

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loan.joblib

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loan_list.joblib

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marital.joblib

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marital_list.joblib

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month.joblib

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month_list.joblib

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poutcome.joblib

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poutcome_list.joblib

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requirements.txt

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+pandas
+numpy
+joblib
+gradio
+scikit-learn
+xgboost

scaler.joblib

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y.joblib

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y_list.joblib

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