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Malaria_cnn.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:28807354f64a12ab5775dcfbafe1d71f660b36b48149b3882e8c42481c47c7c5
+size 6286184

app.py

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

requirements.txt

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+streamlit
+tensorflow
+opencv-python