Rename README.md to app.py

README.md

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-license: apache-2.0
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app.py

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+# Fonction de prédiction
+import gradio as gr
+import joblib
+import pandas as pd
+import numpy as np
+from keras.models import load_model
+
+# importer les encodeurs
+encoders = joblib.load(f'encoders.joblib')
+#for i in range(len(cat_data.columns)):
+  #encoders.append(joblib.load(f'{cat_data.columns[i]}_encoder.joblib'))
+# importer le modèle
+model = load_model('DNN_model.h5')
+# importer le scaler
+scaler = joblib.load('scaler.joblib')
+# Fonction de prédiction simple
+def prediction_func(age, job, marital, education, default, housing, loan, contact, month, day_of_week, duration, campaign, pdays, previous, poutcome):
+  # encoder les valeurs
+  job = encoders[0].transform([job])[0]
+  marital = encoders[1].transform([marital])[0]
+  education = encoders[2].transform([education])[0]
+  default = encoders[3].transform([default])[0]
+  housing = encoders[4].transform([housing])[0]
+  loan = encoders[5].transform([loan])[0]
+  contact = encoders[6].transform([contact])[0]
+  month = encoders[7].transform([month])[0]
+  day_of_week = encoders[8].transform([day_of_week])[0]
+  poutcome = encoders[9].transform([poutcome])[0]
+  # vecteur des valeurs
+  x_new = np.array([age, job, marital, education, default, housing, loan, contact, month, day_of_week, duration, campaign, pdays, previous, poutcome]).reshape(1, -1)
+  # normaliser les valeurs
+  x_new = scaler.transform(x_new)
+  # prédire la valeur
+  y_pred = np.round(model.predict(x_new))
+  # retourner
+  if y_pred == 1:
+    return 'Souscrire'
+  else:
+    return 'Pas souscrire'
+
+    # load les valeurs uniques
+# importer les uniques
+uniques = joblib.load(f'uniques.joblib')
+#for i in range(len(cat_data.columns)):
+  #uniques.append(joblib.load(f'{cat_data.columns[i]}_unique.joblib'))
+# créer les inputs
+inputs = [gr.Number(label="age"),
+          gr.Dropdown(uniques[0], label="job"),
+          gr.Dropdown(uniques[1], label="marital"),
+          gr.Dropdown(uniques[2], label="education"),
+          gr.Dropdown(uniques[3], label="default"),
+          gr.Dropdown(uniques[4], label="housing"),
+          gr.Dropdown(uniques[5], label="loan"),
+          gr.Dropdown(uniques[6], label="contact"),
+          gr.Dropdown(uniques[7], label="month"),
+          gr.Dropdown(uniques[8], label="day_of_week"),
+          gr.Number(label="duration"),
+          gr.Number(label="campaign"),
+          gr.Number(label="pdays"),
+          gr.Number(label="previous"),
+          gr.Dropdown(uniques[9], label="poutcome")]
+# créer les outputs
+outputs = gr.Textbox(label = 'Souscription')
+# Interface
+Interface =gr.Interface(fn = prediction_func,
+             inputs = inputs,
+             outputs = outputs,
+             title = 'Bank Marketing Prediction', theme='earneleh/paris')
+Interface.launch()