Update app.py

app.py

@@ -2,13 +2,11 @@ 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
+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)
@@ -16,31 +14,28 @@ 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 = 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"]
+    labels = ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"]
     img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-    H,W = 28,28
+    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)
+    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
 
-
+# Set up Gradio interface
 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
+iface = gr.Interface(fn=inference,
+                     inputs=gr.Image(source="upload"),  # source= "upload" "webcam" "canvas"
+                     outputs=gr.Label(num_top_classes=nbr_top_classes),
+                     theme="darkdefault")
+
+# Launch the interface
+iface.launch(share=True)