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_cancer_model.h5")
+
+def process_image(img):
+    img = img.resize((170,170))
+    img = np.array(img)
+    img = img/255.0
+    img = np.expand_dims(img,axis=0)
+    return img
+
+st.title("SKIN CANCER CLASSIFICATION:cancer:")
+st.write("Upload your image and see the results")
+
+file = st.file_uploader("Choose an image", type=["jpg","jpeg","png"])
+
+if file is not None:
+    img = Image.open(file)
+    st.image(img, caption="Downloaded image")
+    image=process_image(img)
+    prediction = model.predict(image)
+    predicted_class = np.argmax(prediction)
+    
+    class_names = ["Not Cancer","Cancer"]
+    st.write(class_names[predicted_class])

requirements.txt

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+streamlit
+tensorflow

skin_cancer_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0a5e0b6d9bb827df83d630ee571e4c02370a0f30959daba14fcc6c415de6f5a4
+size 165530032