Create app.py

app.py

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+from sklearn.datasets import fetch_openml
+from sklearn.preprocessing import StandardScaler
+from sklearn.model_selection import train_test_split
+import gradio as gr
+import requests
+from sklearn import svm, metrics
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import confusion_matrix
+import numpy as np
+import cv2
+
+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)
+
+predicted = project_classifier.predict(X_test_img)
+
+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 = np.array(img)
+    img = img.astype("float32")
+    img = img.flatten()
+    img = img.reshape(1,H*W)
+    img = scaler.transform(img)
+    pred = project_classifier.predict_proba(img).flatten()
+    dictionary = dict(zip(labels, map(float, pred)))
+    return dictionary
+
+
+nbr_top_classes = 3
+gr.Interface(fn=inference,
+             inputs=gr.inputs.Image(source="upload"), # source= "upload" "webcam" "canvas"
+             outputs=gr.outputs.Label(num_top_classes=nbr_top_classes),
+             theme="darkdefault").launch(share=True) #, debug=True Use in Colab