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('skin_canser_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('Skin Canser Classification :cancer:')
+st.write('Upload Test Image')
+
+
+file = st.file_uploader('Enter Image', type=['jpg','png','jpeg'])
+
+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 = ['Canser !','Not Canser !']
+    st.write(class_names[predicted_class])

requierements.txt

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+streamlit
+tensorflow

skin_canser_cnn_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:07f747e3ead8331022f9bf06a9b52771d1fe22be08c86086b886c8403a53e7f9
+size 165525616