[ADD] prediction

app.py

@@ -1,25 +1,65 @@
 import gradio as gr
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.linear_model import LogisticRegression
+from sklearn.pipeline import Pipeline
+from sklearn.metrics import accuracy_score
+import time
+import paho.mqtt.client as mqtt
 
-def sentence_builder(quantity, animal, place, activity_list, morning):
-    return f"""The {quantity} {animal}s went to the {place} where they {" and ".join(activity_list)} until the {"morning" if morning else "night"}"""
+df = pd.read_csv("/content/Churn_Modelling.csv")
+df.drop(["RowNumber","CustomerId","Surname"], axis=1, inplace=True)
+df.head()
 
+df.Balance.plot(kind="hist", figsize=(10,6))
+df.Balance = np.where(df.Balance==0, 0, 1)
+df.Balance.value_counts()
+df.Age.plot(kind="hist", figsize=(10,6))
+
+X = df.drop(["Exited","Geography","Gender"], axis=1)
+y = df["Exited"]
+X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
+
+pl = Pipeline([
+    ("scale", StandardScaler()),
+    ("logreg", LogisticRegression())
+])
+pl.fit(X_train, y_train)
+y_train_pred = pl.predict(X_train)
+y_test_pred = pl.predict(X_test)
+
+def sentence_builder(credit, age, tenure, balance, nb_prods, has_card, active, est_salary):
+    data = [{
+        "CreditScore": credit,
+        "Age": age,
+        "Tenure": tenure,
+        "Balance": balance,
+        "NumOfProducts": nb_prods,
+        "HasCrCard": has_card,
+        "IsActiveMember": active,
+        "EstimatedSalary": est_salary,
+    }]
+    df = pd.json_normalize(data)
+    return pl.predict(df)
 
 iface = gr.Interface(
     sentence_builder,
     [
-        gr.inputs.Slider(2, 20),
-        gr.inputs.Dropdown(["cat", "dog", "bird"]),
-        gr.inputs.Radio(["park", "zoo", "road"]),
-        gr.inputs.CheckboxGroup(["ran", "swam", "ate", "slept"]),
-        gr.inputs.Checkbox(label="Is it the morning?"),
+        gr.inputs.Slider(0, 10000, label='credit'),
+        gr.inputs.Slider(0, 100, label='age'),
+        gr.inputs.Slider(0, 10, label='tenure'),
+        gr.inputs.Slider(0, 10000, label='balance'),
+        gr.inputs.Slider(0, 10, label='number of products'),
+        gr.inputs.Checkbox(label="credit card"),
+        gr.inputs.Checkbox(label="active"),
+        gr.inputs.Slider(0, 200000, label='estimated salary'),
     ],
     "text",
     examples=[
-        [2, "cat", "park", ["ran", "swam"], True],
-        [4, "dog", "zoo", ["ate", "swam"], False],
-        [10, "bird", "road", ["ran"], False],
-        [8, "cat", "zoo", ["ate"], True],
+        [619, 42, 2, 0, 1, 1, 1, 101348], # Returns False 0 
+        [608, 41, 1, 83807, 1, 0, 1, 112542], # Returns True 1
     ],
 )
-
-iface.launch()
+iface.launch()