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import joblib
import numpy as np
import cv2
import gradio as gr
from sklearn import svm
# 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"]
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
H, W = 28, 28
img = cv2.resize(img, (H, W))
ret, img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)
img = img.astype("float32").flatten().reshape(1, -1)
img = scaler.transform(img)
pred = project_classifier.predict_proba(img).flatten()
dictionary = dict(zip(labels, map(float, pred)))
return dictionary
# إعداد واجهة Gradio
nbr_top_classes = 3
iface = gr.Interface(fn=inference,
inputs=gr.Image(),
outputs=gr.Label(num_top_classes=nbr_top_classes),
theme="darkdefault")
iface.launch(share=True)
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