Update app.py

app.py

@@ -1,65 +1,19 @@
+import keras
 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
 
-df = pd.read_csv("./Churn_Modelling.csv")
-df.drop(["RowNumber","CustomerId","Surname"], axis=1, inplace=True)
-df.head()
+model = keras.models.load_model('model.h5')
 
-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 bool(pl.predict(df)[0])
+def analyse(image):
+  data = image.reshape((1, 128, 128, 3))
+  return model.predict(data)
 
 iface = gr.Interface(
-    sentence_builder,
-    [
-        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'),
-    ],
+    analyse,
+    gr.inputs.Image(shape=(128,128)),
     "text",
     examples=[
-        [619, 42, 2, 0, 1, 1, 1, 101348], # Returns False 0
-        [608, 41, 1, 83807, 1, 0, 1, 112542], # Returns True 1
     ],
 )
 iface.launch()