Upload capstone_gradio_app_embedding.py

capstone_gradio_app_embedding.py

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+# -*- coding: utf-8 -*-
+"""Capstone Gradio App Embedding.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1zsT_lHGVHzG29XSb4tMF3UdA6glyWnRx
+"""
+
+from google.colab import drive
+drive.mount('/content/drive')
+
+!pip install gradio
+
+"""### **DATA PREP**"""
+
+import pandas as pd
+# # Specify the correct file paths for your data files
+# test = pd.read_csv('/content/drive/MyDrive/datasets capstone/test.csv')
+# y_train = pd.read_csv("/content/drive/MyDrive/datasets capstone/y_train.csv")
+# X_train =pd.read_csv('/content/drive/MyDrive/datasets capstone/X_train.csv')
+
+# #X_test = pd.read_csv("/content/test.csv")
+
+path = "/content/drive/MyDrive/Capstone Project /"
+
+train = pd.read_csv(path + 'Train.csv')
+
+# use lambda function to remove \t make our model more robst
+train = train.applymap(lambda x: x.replace("\t" , '' ) if isinstance (x , str) else x)
+# "  " , " "
+train = train.applymap(lambda x: x.replace("  " , ' ' ) if isinstance (x , str) else x)
+
+# drop what we don't need
+train.drop(columns=['MRG', 'user_id', 'ZONE1', 'ZONE2', 'TOP_PACK'], inplace=True)
+
+train.head(1)
+
+train["REGION"].fillna(method='ffill', inplace=True)
+train["TENURE"].fillna(method='ffill', inplace=True)
+train["MONTANT"].fillna(train["MONTANT"].median(), inplace=True)
+train["FREQUENCE_RECH"].fillna(0, inplace=True)
+train["REVENUE"].fillna(train["REVENUE"].median(), inplace=True)
+train["ARPU_SEGMENT"].fillna(0, inplace=True)
+train["FREQUENCE"].fillna(0, inplace=True)
+train["DATA_VOLUME"].fillna(0, inplace=True)
+train["ON_NET"].fillna(0, inplace=True)
+train["ORANGE"].fillna(0, inplace=True)
+train["TIGO"].fillna(0, inplace=True)
+# train["ZONE1"].fillna(0, inplace=True)
+# train["ZONE2"].fillna(0, inplace=True)
+# train["MRG"].fillna(method='ffill', inplace=True)
+train["REGULARITY"].fillna(train["REGULARITY"].mean(), inplace=True)
+#train["TOP_PACK"].fillna(method='ffill', inplace=True)
+train["FREQ_TOP_PACK"].fillna(train["FREQ_TOP_PACK"].mean(), inplace=True)
+
+train['TENURE'] = train['TENURE'].str.replace('D 3-6 month', '1', regex=True)
+train['TENURE'] = train['TENURE'].str.replace('E 6-9 month', '2', regex=True)
+train['TENURE'] = train['TENURE'].str.replace('F 9-12 month', '3', regex=True)
+train['TENURE'] = train['TENURE'].str.replace('J 21-24 month', '4', regex=True)
+train['TENURE'] = train['TENURE'].str.replace('G 12-15 month', '5', regex=True)
+train['TENURE'] = train['TENURE'].str.replace('H 15-18 month', '6', regex=True)
+train['TENURE'] = train['TENURE'].str.replace('I 18-21 month', '7', regex=True)
+train['TENURE'] = train['TENURE'].str.replace('K > 24 month', '8', regex=True)
+
+# train['TENURE'].value_counts()
+
+# Define a dictionary to map values
+region_mapping = {
+    'DAKAR': '1',
+    'THIES': '2',
+    'SAINT-LOUIS': '3',
+    'LOUGA': '4',
+    'KAOLACK': '5',
+    'DIOURBEL': '6',
+    'TAMBACOUNDA': '7',
+    'KAFFRINE': '8',
+    'KOLDA': '9',
+    'FATICK': '10',
+    'ZIGUINCHOR': '11',
+    'SEDHIOU': '12',
+    'KEDOUGOU': '13',
+    'MATAM' : '14'
+}
+
+# Use the replace method to map values
+train['REGION'] = train['REGION'].replace(region_mapping)
+
+# Look at the new value_counts
+# print(train['REGION'].value_counts())
+
+"""## **FITTING AND TRAINING**"""
+
+# train.head(1)
+
+"""Select target and features"""
+
+y = train['CHURN']
+
+x = train.drop(columns='CHURN', axis=1)
+
+# y.head(3)
+
+# x.head(2)
+
+from sklearn.model_selection import train_test_split
+
+X_train, X_test, y_train, y_test = train_test_split(x,y,test_size = 0.5,random_state=45 )# , stratify=y)
+
+#Further split X_train and y_train into train and validation sets
+X_train,X_val,y_train,y_val = train_test_split(X_train,y_train,test_size = 0.3, random_state=1 )#, stratify=y)
+
+"""### SCALE NUMERICAL COLUMNS"""
+
+num_cols = ['MONTANT', 'FREQUENCE_RECH', 'REVENUE', 'ARPU_SEGMENT', 'FREQUENCE',
+       'DATA_VOLUME', 'ON_NET', 'ORANGE', 'TIGO',
+       'REGULARITY', 'FREQ_TOP_PACK']
+
+scaler = StandardScaler()
+
+X_train[num_cols] = scaler.fit_transform(X_train[num_cols])
+
+X_val[num_cols] = scaler.fit_transform(X_val[num_cols])
+
+#X_train.head(3)
+
+"""### ENCODE CATEGORICAL COLS WITH NUMERICAL VALUES WITH MANY N_UNIQUE( ) VALS
+
+"""
+
+!pip install category_encoders
+
+# import category_encoders as ce
+
+# encoder_ = ce.SumEncoder(cols=['TOP_PACK'])
+
+# encoder.fit(x, y)
+# X_train = encoder.transform(X_train)
+
+# X_val = encoder.transform(X_val)
+
+# X_test = encoder.transform(X_test)
+
+from sklearn.model_selection import train_test_split, cross_val_score
+from sklearn.metrics import accuracy_score, confusion_matrix, recall_score, precision_recall_curve, f1_score
+from sklearn.preprocessing import StandardScaler
+from sklearn.ensemble import ExtraTreesRegressor
+from sklearn.preprocessing import LabelEncoder
+
+# Create an instance
+model = ExtraTreesRegressor(
+    n_estimators=100,  # Number of trees in the forest
+    max_depth=10,  # Maximum depth of the tree
+    random_state=42  # Random seed for reproducibility
+)
+
+# Train the model
+MODEL = model.fit(X_train, y_train)
+
+"""## **Check if our model is working**"""
+
+y_pred = MODEL.predict(X_test)
+
+y_pred
+
+"""Since our model is working correctly , Inspect the X_test features , to be used as the user input to interact with the model"""
+
+X_test.head()
+
+
+
+def classifier_1(result):
+    if result > 0.9:
+        return "Customer will churn"
+    else:
+        return "Customer will not churn"
+
+def predict(REGION,TENURE	, MONTANT ,	FREQUENCE_RECH, REVENUE	, ARPU_SEGMENT ,FREQUENCE	, DATA_VOLUME	, ON_NET, ORANGE ,	TIGO, REGULARITY ,FREQ_TOP_PACK):
+    input_array = np.array([[REGION,TENURE	, MONTANT ,	FREQUENCE_RECH, REVENUE	, ARPU_SEGMENT ,FREQUENCE	, DATA_VOLUME	, ON_NET, ORANGE ,	TIGO, REGULARITY ,FREQ_TOP_PACK]])
+
+    pred = MODEL.predict(input_array)
+
+    output = classifier_1 (pred[0])
+
+    if output == "Customer will churn":
+      return [(0, output)]
+    else :
+      return [(1, output)]
+
+"""Check if the function will work"""
+
+predict(1,2,3,4,5,6,7,8,9,1,1,1,1)
+
+variable_definitions
+
+X_test['FREQ_TOP_PACK'].min() , X_test['FREQ_TOP_PACK'].max()
+
+import gradio as gr
+
+#tenure = tenure_dropdown
+REGION = gr.inputs.Slider(minimum=1, maximum=13, label='Location of each client')
+TENURE = gr.inputs.Slider(minimum=1, maximum=8, label="Duration in network")
+MONTANT = gr.inputs.Slider(minimum=22, maximum=470000, label="Top up amount")
+FREQUENCE_RECH = gr.inputs.Slider(minimum=1, maximum=131, label="income frequency")
+REVENUE = gr.inputs.Slider(minimum=1, maximum=532177, label="ARPU_SEGMENT")
+ARPU_SEGMENT = gr.inputs.Slider(minimum=1, maximum= 177392, label="FREQUENCE")
+FREQUENCE = gr.inputs.Slider(minimum=1, maximum=91, label="DATA_VOLUME")
+DATA_VOLUME =gr.inputs.Slider(minimum=0, maximum=1702309, label="ON_NET")
+ON_NET = gr.inputs.Slider(minimum=0, maximum=36687, label="ORANGE")
+ORANGE = gr.inputs.Slider(minimum=0, maximum= 6721, label="TIGO")
+TIGO = gr.inputs.Slider(minimum=0, maximum=4174, label="ZONE1")
+REGULARITY = gr.inputs.Slider(minimum=1, maximum=62, label="ZONE2")
+FREQ_TOP_PACK = gr.inputs.Slider(minimum=1, maximum= 592, label="REGULARITY")
+
+
+op = gr.outputs.HighlightedText(color_map={"Customer will churn":"pink", "Customer will not churn":"yellow"})
+
+gr.Interface(predict , inputs = [REGION,TENURE, MONTANT ,	FREQUENCE_RECH, REVENUE	, ARPU_SEGMENT ,FREQUENCE	, DATA_VOLUME	, ON_NET, ORANGE ,TIGO, REGULARITY ,FREQ_TOP_PACK], outputs=op,
+             live = True).launch(debug=True)
+
+import gradio as gr
+
+# Input sliders
+REGION = gr.inputs.Slider(minimum=1, maximum=13, label='Location of each client')
+TENURE = gr.inputs.Slider(minimum=1, maximum=8, label="Duration in network")
+MONTANT = gr.inputs.Slider(minimum=22, maximum=470000, label="Top-up amount")
+FREQUENCE_RECH = gr.inputs.Slider(minimum=1, maximum=131, label="Income frequency")
+REVENUE = gr.inputs.Slider(minimum=1, maximum=532177, label="ARPU_SEGMENT")
+ARPU_SEGMENT = gr.inputs.Slider(minimum=1, maximum=177392, label="FREQUENCE")
+FREQUENCE = gr.inputs.Slider(minimum=1, maximum=91, label="DATA_VOLUME")
+DATA_VOLUME = gr.inputs.Slider(minimum=0, maximum=1702309, label="ON_NET")
+ON_NET = gr.inputs.Slider(minimum=0, maximum=36687, label="ORANGE")
+ORANGE = gr.inputs.Slider(minimum=0, maximum=6721, label="TIGO")
+TIGO = gr.inputs.Slider(minimum=0, maximum=4174, label="ZONE1")
+REGULARITY = gr.inputs.Slider(minimum=1, maximum=62, label="ZONE2")
+FREQ_TOP_PACK = gr.inputs.Slider(minimum=1, maximum=592, label="REGULARITY")
+
+# Output configuration
+op = gr.outputs.HighlightedText(color_map={"Customer will churn": "pink", "Customer will not churn": "yellow"})
+
+# Create and launch the interface
+gr.Interface(predict, inputs=[REGION, TENURE, MONTANT, FREQUENCE_RECH, REVENUE, ARPU_SEGMENT, FREQUENCE,
+                              DATA_VOLUME, ON_NET, ORANGE, TIGO, REGULARITY, FREQ_TOP_PACK], outputs=op,
+             live=False).launch(debug=False)
+
+# # Map numerical values to labels
+# tenure_labels = {
+#     0: "3-6 months",
+#     1: "6-9 months",
+#     2: "9-12 months",
+#     3: "12-15 months",
+#     4: "15-18 months",
+#     5: "18-21 months",
+#     6: "21-24 months",
+#     7: "> 24 months"
+# }
+
+# # Reverse the mapping for predictions
+# tenure_values = {v: k for k, v in tenure_labels.items()}
+
+# # Create a dropdown menu with labels
+# tenure_dropdown = gr.inputs.Dropdown(list(tenure_labels.values()), label="TENURE")
+