Upload 3 files

app.py

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+import streamlit as st 
+from tensorflow.keras.models import load_model
+from PIL import Image
+import numpy as np
+
+ 
+model=load_model('cnn_model.h5')
+ 
+
+def process_image(img):
+    img=img.resize((170,170))
+    img=np.array(img)
+    img=img/255.0 #normalize
+    img=np.expand_dims(img,axis=0)
+    return img
+
+st.title("Cancer Image Classification :cancer:")
+st.write("Select image and model predicts whether it is cancer")
+         
+file=st.file_uploader('Bir Resim Sec',type=['jpg','jpeg','png'])
+
+if file is not None:
+    img=Image.open(file)
+    st.image(img,caption='uploaded image')
+    image= process_image(img)
+    prediction=model.predict(image)
+    predicted_class=np.argmax(prediction)
+
+    class_names=['Non Cancer','Cancer']
+
+    st.write(class_names[predicted_class])

cnn_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7014d594d672d43c71288b5ff4f3b798796d1e027d09c2c321fe750765512c4b
+size 165525616

requirements.txt

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+streamlit
+tensorflow