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import gradio
import cv2
from sklearn.naive_bayes import BernoulliNB
import pickle
import numpy as np
# multiclass_model = pickle.load(open('models/MulticlassModel_200x200', 'rb'))
ensemble_model = pickle.load(open('EnsembleModels_200x200', 'rb'))
examples = ['images/test2.jpg','images/test4.jpg','images/test6.jpg',
"images/Incom.jpg", "images/DLC.jpg", 'images/EHD.jpg',
'images/IDR.jpg','images/PPD.jpg','images/PSLF.jpg' ,'images/SCD.jpg',
'images/TLF.jpg']
def preprocess(img):
img = cv2.resize(img, (200,200))
img = cv2.adaptiveThreshold(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY),255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,11,2)
img = np.reshape(img, (1,200*200))/255
return img
def predict(img):
img = preprocess(img)
categories = {
"Inco": 2,
"Teac": 1,
"Cons": 0,
"Publ": 4,
"Econ": 3,
"Reaf": 5}
proba = np.zeros((6))
for key in categories.keys():
proba[categories[key]] = ensemble_model[key].predict_proba(img)[:,0]
return proba
def generate_results(proba):
categories = [
"DLC",
"TLF",
"IDR",
"EHD",
"PLSF",
"REA",
"UNKNOWN"]
scores = [0,0,0,0,0,0,0]
choice = np.where(proba == np.amin(proba))[0]
if len(choice)>1:
choice = 6
scores[int(choice)] = 1
results = dict(zip(categories, scores))
return results
def inference(img):
proba = predict(img)
results = generate_results(proba)
return results
demo = gradio.Interface(
fn=inference,
inputs=gradio.Image(),
outputs=gradio.Label(),
title='Document Classification',
description='Loan Document Classification Using A Naive Bayes Classifier Ensemble',
article='The purpose of this demo was to provide a simple baseline for the classification of document images. View the complete write up here https://github.com/PatrickTyBrown/document_classification/blob/main/project_writeup.pdf\n\n\nLinkedin: https://www.linkedin.com/in/patrick-ty-brown/\nGithub: https://github.com/PatrickTyBrown/document_classification\nPortfolio: https://sites.google.com/view/patrick-brown/home',
examples=examples)
demo.launch()
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