update

app.py

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+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("C:/Users/Gregory Arthur/Desktop/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()
+
+with block:
+    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", show_legend=True)
+    gr.themes="freddyaboulton/dracula_revamped"
+     
+    predict_btn= gr.Button("Predict")
+     
+    predict_btn.click(fn= predict_churn, inputs= input, outputs=output)
+
+block.launch()
+    
+