app.py

app.py

@@ -1,117 +0,0 @@
-import joblib 
-import pandas as pd
-import numpy as np
-import gradio as gr
-import pandas as pd
-import numpy as np
-from sklearn.linear_model import LogisticRegression
-from sklearn.feature_selection import SelectKBest
-from sklearn.preprocessing import MinMaxScaler, OneHotEncoder
-from sklearn.impute import SimpleImputer
-from sklearn.pipeline import Pipeline
-from sklearn.utils.class_weight import compute_class_weight
-import gradio as gr
-import joblib
-import warnings
-
-warnings.filterwarnings("ignore")
-
-model= joblib.load("models/LR.joblib")
-
-model
-
-test= pd.read_csv("dataframes/Vodafone_churn.csv")
-test
-
-##testing our model
-model.predict(test)
-
-##creating a function to return a string depending on the output of the model
-
-def classify(num):
-    if num == 0:
-        return "Customer will not Churn"
-    else:
-        return "Customer will churn"
-
-
-"""creating a function for my gradion fn
-defining my parameters which my fucntion will accept, and are the same as the features I trained my model on"""
-
-
-def predict_churn(SeniorCitizen, Partner, Dependents, tenure, InternetService,
-                  OnlineSecurity, OnlineBackup, DeviceProtection, TechSupport,
-                  StreamingTV, StreamingMovies, Contract, PaperlessBilling,
-                  PaymentMethod, MonthlyCharges, TotalCharges): 
-
-     
-     ##in the code below, I am created a list of my input features
-
-    input_data = [
-        SeniorCitizen, Partner, Dependents, tenure, InternetService,
-        OnlineSecurity, OnlineBackup, DeviceProtection, TechSupport,
-        StreamingTV, StreamingMovies, Contract, PaperlessBilling,
-        PaymentMethod, MonthlyCharges, TotalCharges
-    ]    
-##I am changing my features into a dataframe since that is how I trained my model
-
-    input_df = pd.DataFrame([input_data], columns=[
-        "SeniorCitizen", "Partner", "Dependents", "tenure", "InternetService",
-        "OnlineSecurity", "OnlineBackup", "DeviceProtection", "TechSupport",
-        "StreamingTV", "StreamingMovies", "Contract", "PaperlessBilling",
-        "PaymentMethod", "MonthlyCharges", "TotalCharges"
-    ])
-
-
-    pred = model.predict(input_df) ##I am making a prediction on the input data.
-
-    output = classify(pred[0]) ## I am passing the first predction through my classify function I created earlier
-
-    if output == "Customer will not Churn":
-        return [(0, output)]
-    else:
-        return [(1, output)]   ##setting my function to return the binary classification and the written output
-
-output = gr.outputs.HighlightedText(color_map={
-    "Customer will not Churn": "green",
-    "Customer will churn": "red"
-}) ##assigning colors to the respective output 
-
-##building my interface and wrapping my model in the function
-
-##using gradio blocks to beautify my output
-
-block= gr.Blocks() ##instatiating my blocks class
-
-with block:
-    gr.Markdown(""" # Welcome to My Customer Churn Prediction App""")
-    
-    input=[gr.inputs.Slider(minimum=0, maximum= 1, step=1, label="SeniorCitizen: Select 1 for Yes and 0 for No"),
-        gr.inputs.Radio(["Yes", "No"], label="Partner: Do You Have a Partner?"),
-        gr.inputs.Radio(["Yes", "No"], label="Dependents: Do You Have a Dependent?"),
-        gr.inputs.Number(label="tenure: How Long Have You Been with Vodafone in Months?"),
-        gr.inputs.Radio(["DSL", "Fiber optic", "No"], label="InternetService"),
-        gr.inputs.Radio(["Yes", "No", "No internet service"], label="OnlineSecurity"),
-        gr.inputs.Radio(["Yes", "No", "No internet service"], label="OnlineBackup"),
-        gr.inputs.Radio(["Yes", "No", "No internet service"], label="DeviceProtection"),
-        gr.inputs.Radio(["Yes", "No", "No internet service"], label="TechSupport"),
-        gr.inputs.Radio(["Yes", "No", "No internet service"], label="StreamingTV"),
-        gr.inputs.Radio(["Yes", "No", "No internet service"], label="StreamingMovies"),
-        gr.inputs.Radio(["Month-to-month", "One year", "Two year"], label="Contract"),
-        gr.inputs.Radio(["Yes", "No"], label="PaperlessBilling"),
-        gr.inputs.Radio([
-            "Electronic check", "Mailed check", "Bank transfer (automatic)", "Credit card (automatic)"
-        ], label="PaymentMethod"),
-        gr.inputs.Number(label="MonthlyCharges"),
-        gr.inputs.Number(label="TotalCharges")]
-     
-    output= gr.outputs.HighlightedText(color_map={
-     "Customer will not Churn": "green",
-     "Customer will churn": "red"}, label= "Your Output")     
-    predict_btn= gr.Button("Predict")
-     
-    predict_btn.click(fn= predict_churn, inputs= input, outputs=output)
-
-block.launch()
-    
-