Update app.py

app.py

@@ -1,22 +1,12 @@
-from sklearn.datasets import fetch_openml
-from sklearn.preprocessing import StandardScaler
-from sklearn.model_selection import train_test_split
-import gradio as gr
+import joblib
 import numpy as np
 import cv2
+import gradio as gr
 from sklearn import svm
 
-# Load and preprocess data
-X, y = fetch_openml("mnist_784", version=1, return_X_y=True, as_frame=False)
-scaler = StandardScaler()
-X = scaler.fit_transform(X)
-
-X_train_img, X_test_img, y_train_img, y_test_img = train_test_split(
-    X, y, test_size=0.2, shuffle=True)
-
-# Create a classifier
-project_classifier = svm.SVC(kernel='linear', probability=True)
-project_classifier.fit(X_train_img, y_train_img)
+# تحميل النموذج وscaler
+project_classifier = joblib.load('model.pkl')
+scaler = joblib.load('scaler.pkl')
 
 def inference(img):
     labels = ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"]
@@ -30,12 +20,11 @@ def inference(img):
     dictionary = dict(zip(labels, map(float, pred)))
     return dictionary
 
-# Set up Gradio interface
+# إعداد واجهة Gradio
 nbr_top_classes = 3
 iface = gr.Interface(fn=inference,
-                     inputs=gr.Image(source="upload"),  # source= "upload" "webcam" "canvas"
+                     inputs=gr.Image(source="upload"),
                      outputs=gr.Label(num_top_classes=nbr_top_classes),
                      theme="darkdefault")
 
-# Launch the interface
 iface.launch(share=True)