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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("my_cnn_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("Kanser Resmi Sınıflandırma :cancer:")
+
+st.write("Resim seç ve Model kanser olup olmadığını tahmin etsin")
+file=st.file_uploader("Bir resim seçiniz:", type=["jpg", "jpeg", "png"])
+
+if file is not None:
+    img = Image.open(file)
+    st.image(img, caption="Yüklenen resim")
+    image = process_image(img)
+    prediction = model.predict(image)
+    predicted_class = np.argmax(prediction)
+
+    class_names = ["Kanser Değil", "Kanser"]
+    st.write(class_names[predicted_class])

requirements.txt

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+streamlit
+tensorflow