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from sklearn.datasets import fetch_openml |
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from sklearn.preprocessing import StandardScaler |
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from sklearn.model_selection import train_test_split |
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import gradio as gr |
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import requests |
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from sklearn import svm, metrics |
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from sklearn.linear_model import LogisticRegression |
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from sklearn.metrics import confusion_matrix |
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import numpy as np |
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import cv2 |
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X, y = fetch_openml("mnist_784", version=1, return_X_y=True, as_frame=False) |
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scaler = StandardScaler() |
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X = scaler.fit_transform(X) |
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X_train_img, X_test_img, y_train_img, y_test_img = train_test_split( |
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X, y, test_size=0.2, shuffle=True) |
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project_classifier = svm.SVC(kernel='linear',probability=True) |
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project_classifier.fit(X_train_img, y_train_img) |
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predicted = project_classifier.predict(X_test_img) |
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def inference(img): |
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labels =["0","1", "2","3", "4","5", "6","7", "8","9"] |
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img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) |
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H,W = 28,28 |
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img = cv2.resize(img, (H, W)) |
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ret,img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY) |
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img = np.array(img) |
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img = img.astype("float32") |
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img = img.flatten() |
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img = img.reshape(1,H*W) |
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img = scaler.transform(img) |
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pred = project_classifier.predict_proba(img).flatten() |
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dictionary = dict(zip(labels, map(float, pred))) |
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return dictionary |
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nbr_top_classes = 3 |
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gr.Interface(fn=inference, |
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inputs=gr.inputs.Image(source="upload"), |
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outputs=gr.outputs.Label(num_top_classes=nbr_top_classes), |
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theme="darkdefault").launch(share=True) |
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