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
+
+# Load the model
+model = load_model('my_cnn_model.h5')
+
+#yeni gelen resmi modelin girdi boyutuna uygun hale getirelim 
+def process_image(image): 
+    image = image.resize((170,170)) 
+    image = np.array(image) 
+    image = image / 255.0  
+    image = np.expand_dims(image, axis=0) # burada modelin beklediği gibi bir girdi oluşturduk
+    return image
+
+
+st.title("Skin Cancer Classification - Metehan Ayhan")
+st.write("This is a simple image classification web app to predict the type of skin cancer.")
+st.write("Please upload a skin image for the prediction.")
+
+file = st.file_uploader("Please upload an image file", type=["jpg", "png", "jpeg"])
+
+if file is None:
+    st.text("You haven't uploaded an image file")
+else:
+    image = Image.open(file) # resmi aç
+    st.image(image, use_column_width=True, caption='Image:') # resmi gösterelim
+    predictions = model.predict(process_image(image))
+    predicted_class = np.argmax(predictions) # en yüksek olasılığa sahip sınıfı al
+    
+    class_names = ['Cancer', 'Not Cancer']
+
+    st.write(class_names[predicted_class], "with", round(100*np.max(predictions), 2), "% probability")

my_cnn_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7ea7242edd7b03e5943667efad8cd57477780e50fb51b22dc957cde9d2779c9a
+size 165525592

requirements.txt

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+streamlit
+tensorflow