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  1. Telephone_data___.csv +0 -0
  2. app.py +130 -0
  3. data_prep.csv +2653 -0
  4. requirements.txt +3 -0
Telephone_data___.csv ADDED
The diff for this file is too large to render. See raw diff
 
app.py ADDED
@@ -0,0 +1,130 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ # -*- coding: utf-8 -*-
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+ """Final_projet_app.ipynb
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+
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+ Automatically generated by Colab.
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+
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+ Original file is located at
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+ https://colab.research.google.com/drive/1BlNoqeygGpdcMtxEP-U-wJkAVngPpPUf
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+ """
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+
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+ #Importer les packages
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+ import numpy as np
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+ import gradio as gr
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+ import pandas as pd
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+
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+ data = pd.read_csv('Telephone_data___.csv')
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+ df = pd.read_csv('data_clean.csv')
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+
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+ # Encoder les variables catégorielles
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+ from sklearn.preprocessing import LabelEncoder
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+ # Créer différents encodeurs pour les variables catégorielles
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+ encoder0 = LabelEncoder()
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+ encoder1 = LabelEncoder()
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+ encoder2 = LabelEncoder()
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+ encoders = [encoder0, encoder1, encoder2]
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+ columns = [col for col in data.columns if data[col].dtype == 'object']
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+ for i in range(len(columns)):
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+ encoders[i].fit(data[columns[i]]) # le modèle prend le temps de reconnaitre les classes
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+ # Fractionner les données
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+ x= df.drop('etat', axis = 1).values # variables prédicteurs
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+ y = df['etat'].values # variable cible
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+
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+ # importer StandardScaler
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+ from sklearn.preprocessing import StandardScaler, MinMaxScaler, normalize
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+ # Instancier StandardScaler
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+ scaler = StandardScaler()
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+ scaler.fit(x) # calcul de la moyenne et de l'écart type
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+ x = scaler.transform(x) # normalisation
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+
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+
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+ from sklearn.model_selection import train_test_split
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+ # Splitter les données en train, val et test
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+ x_train, x_vt,y_train,y_vt = train_test_split(x,y , test_size = 0.2, random_state = 42)
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+ # Splitter les données en val et test
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+ x_val, x_test, y_val, y_test = train_test_split(x_vt, y_vt, test_size = 0.5, random_state = 42)
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+
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+
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+ from sklearn.ensemble import GradientBoostingClassifier
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+ # Entrainer Gradient Boosting en utilisant les paramètres optimaux 'max_depth': 4, 'n_estimators': 80
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+ gb = GradientBoostingClassifier(max_depth = 5, n_estimators = 60)
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+ # Entrainement
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+ gb.fit(x_train, y_train)
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+
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+
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+ # importer gradio
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+ import gradio as gr
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+ # Fonction de prédiction simple
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+ def Pred_func(prix, adresse, marque, dim_ecr, ram, stockage):
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+ # Encoder les valeurs des adresse, marque, etat
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+ adresse = encoder0.transform([adresse])[0]
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+ marque = encoder1.transform([marque])[0]
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+ # vecteur des valeurs numériques
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+ x_new = np.array([prix, adresse, marque, dim_ecr, ram, stockage])
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+ x_new = x_new.reshape(1,-1) # convert en un 2D array
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+ # Normaliser les données
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+ x_new = scaler.transform(x_new)
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+ # Prédire
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+ y_pred = gb.predict(x_new)
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+ # Décoder la valeur prédite pour 'etat'
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+ y_pred_decoded = encoder2.inverse_transform([y_pred[0]])[0] # Décoder la valeur encodée
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+ return str(y_pred_decoded)
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+
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+
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+ # Fonction de prédiction multiple
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+ def Pred_func_csv(file):
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+ # Lire le fichier csv
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+ df = pd.read_csv(file)
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+ # prédictions
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+ predictions = []
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+ # Boucle sur les lignes du dataframe
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+ for row in df.iloc[:, :].values:
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+ # nouvelle ligne avec les valeurs des Fuel_Type, Seller_Type et Transmission encodées
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+ new_row = np.array([row[0], encoder0.transform([row[1]])[0], encoder1.transform([row[2]])[0], row[3], row[4],row[5] ])
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+ new_row = new_row.reshape(1,-1) # convertir en un 2D array
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+ # Normaliser les données
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+ new_row = scaler.transform(new_row)
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+ # Prédire
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+ y_pred = gb.predict(new_row)
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+ # Décoder la valeur prédite pour 'etat'
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+ y_pred_decoded = encoder2.inverse_transform([y_pred[0]])[0] # Décoder la valeur encodée
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+ predictions.append(y_pred_decoded) # Ajouter la prédiction à la liste
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+ df['etat'] = predictions
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+ df.to_csv('predictions.csv', index = False)
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+ return 'predictions.csv'
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+
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+
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+ # définir les blocks afin de pouvoir ajouter plusieurs interfaces
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+ demo = gr.Blocks(theme = gr.themes.Glass())
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+
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+ # autres themes
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+ #gr.themes.Base() gr.themes.Default() gr.themes.Glass() gr.themes.Monochrome() gr.themes.Soft()
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+
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+
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+ # Interface Gradio pour interagir avec la fonction
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+ interface1 = gr.Interface(
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+ fn=Pred_func, # Fonction à appeler
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+ inputs=[
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+ gr.Number(label="prix"),
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+ gr.Dropdown(choices= list(np.unique(data.adresse)), label='Adresse'), # on utilise list pour bcp de valeur
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+ gr.Dropdown(choices= list(np.unique(data.marque)), label='Marque'), # on utilise list pour bcp de valeur
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+ gr.Number(label="Dimension de l'écran"),
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+ gr.Number(label="RAM (Go)"),
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+ gr.Number(label="Stockage (Go)")
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+ ],
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+ outputs = gr.Textbox(label='etat'), # Sortie sous forme de texte
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+ title="Prédiction de l'état d'un Téléphone portable", # Titre de l'interface
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+ )
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+ # Créer l'interface 2 permettant de faire une prédiction multiple en partant d'un fichier csv
118
+ interface2 = gr.Interface(fn = Pred_func_csv,
119
+ inputs = gr.File(label='Upload a csv file'),
120
+ outputs = gr.File(label='Download a csv file'),
121
+ title="Predict the statut of mobile phone",
122
+ description = """This machine learning model allows us to predict the statut of mobile phone with his specifications.
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+ """)
124
+
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+ # faire un tabbing (regrouper en onglet) des interfaces
126
+ with demo:
127
+ gr.TabbedInterface([interface1, interface2], ['Simple Prediction', 'Prédiction multiple'])
128
+
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+ # lancer l'interface
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+ demo.launch()
data_prep.csv ADDED
@@ -0,0 +1,2653 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 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1930
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1935
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1936
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1938
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1939
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1971
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1978
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2003
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+ 730000,32,27,6.0,6,256,3
2619
+ 330000,36,19,6.0,12,256,3
2620
+ 470000,31,19,6.0,12,256,3
2621
+ 200000,5,6,6.0,8,128,3
2622
+ 35000,5,3,6.0,12,64,3
2623
+ 35000,5,3,6.0,3,64,3
2624
+ 200000,33,27,6.0,12,64,3
2625
+ 115000,5,27,6.0,12,64,3
2626
+ 140000,5,27,6.0,12,128,3
2627
+ 64900,5,19,6.0,12,64,3
2628
+ 19900,5,0,6.0,12,128,3
2629
+ 420000,32,19,7.0,12,256,3
2630
+ 420000,23,6,6.0,12,128,3
2631
+ 170000,23,6,6.0,12,128,3
2632
+ 245000,23,27,6.0,12,256,3
2633
+ 55000,23,19,6.0,12,128,3
2634
+ 175000,23,6,6.0,8,128,3
2635
+ 79900,10,8,6.0,6,128,3
2636
+ 64900,5,8,6.0,12,128,3
2637
+ 520000,32,27,6.0,6,256,3
2638
+ 200000,32,19,6.0,12,256,3
2639
+ 145000,31,24,6.0,8,128,3
2640
+ 150000,36,27,5.0,4,64,3
2641
+ 330000,23,27,6.0,12,256,3
2642
+ 192000,23,27,6.0,12,256,3
2643
+ 155000,23,6,6.0,12,128,3
2644
+ 270000,23,6,6.0,12,128,3
2645
+ 115000,32,27,6.0,3,64,3
2646
+ 125000,13,27,6.0,3,64,3
2647
+ 180000,13,27,6.0,4,128,3
2648
+ 359000,41,27,6.0,12,128,3
2649
+ 210000,13,27,5.0,4,256,3
2650
+ 630000,23,27,6.0,12,128,3
2651
+ 410000,23,27,6.0,12,128,3
2652
+ 365000,6,27,6.0,6,128,3
2653
+ 160000,23,27,6.0,12,64,3
requirements.txt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ gradio
2
+ numpy
3
+ scikit-learn