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app.py
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#!/usr/bin/env python
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# coding: utf-8
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#dosyayı py olarak kaydet ve komut satırını kullanarak streamlit run streamlit.py
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import streamlit as st
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from tensorflow.keras.models import load_model
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from PIL import Image
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import numpy as np
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import cv2
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model=load_model('Malaria_cnn.h5')
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def process_image(img):
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img=img.resize((30,30))
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img=np.array(img)
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img=img/255.0
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img=np.expand_dims(img,axis=0)
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return img
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st.title('Malaria Parazit Tarama')
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st.write('Resim sec ve model tahmin etsin')
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file=st.file_uploader('Bir resim seç', type= ['jpg','jpeg','png'])
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class_names=['Normal','Parazit']
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if file is not None:
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img=Image.open(file)
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st.image(img,caption='yuklenen resim')
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image=process_image(img)
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prediction=model.predict(image)
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predicted_class=np.argmax(prediction)
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st.write(class_names[predicted_class])
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