Update app.py

app.py

@@ -1,126 +1,115 @@
-  
-#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'])
-    
+  
+#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:
+        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 un client va s'ouscrire ou non a partir de quelques informations"""
+)
+
+with demo:
+    gr.TabbedInterface([interface1, interface2], ['simple prediction', 'multiple predictions'])
+    
 demo.launch()