Kalbe-x-Bangkit
commited on
Commit
•
5aa195d
1
Parent(s):
716a982
Update app.py
Browse files
app.py
CHANGED
@@ -74,7 +74,25 @@ model = load_model()
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# image = cv2.imdecode(file_bytes, 1)
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# st.image(image, caption='Uploaded Image.', use_column_width=True)
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-
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# Utility Functions
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@@ -244,7 +262,7 @@ def compute_gradcam(model_gradcam, img_path, layer_name='bn'):
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# Load the original model
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# Now use this modified model in your application
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model_gradcam =
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preprocessed_input = load_image(img_path)
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predictions = model_gradcam.predict(preprocessed_input)
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@@ -496,7 +514,7 @@ if uploaded_file is not None:
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with col3:
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if st.button('Generate Grad-CAM'):
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st.write("Loading model...")
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model_gradcam =
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# Compute and show Grad-CAM
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st.write("Generating Grad-CAM visualizations")
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try:
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# image = cv2.imdecode(file_bytes, 1)
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# st.image(image, caption='Uploaded Image.', use_column_width=True)
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@st.cache_resource
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def load_gradcam_model():
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model = keras.models.load_model('./model_renamed.h5', compile=False)
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model.compile(
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loss={
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"bbox": "mse",
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"class": "sparse_categorical_crossentropy"
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},
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optimizer=tf.keras.optimizers.Adam(),
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metrics={
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"bbox": ['mse'],
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"class": ['accuracy']
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}
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)
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return model
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model_gradcam = load_gradcam_model()
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# Utility Functions
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# Load the original model
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# Now use this modified model in your application
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model_gradcam = load_gradcam_model()
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preprocessed_input = load_image(img_path)
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predictions = model_gradcam.predict(preprocessed_input)
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with col3:
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if st.button('Generate Grad-CAM'):
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st.write("Loading model...")
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model_gradcam = load_gradcam_model()
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# Compute and show Grad-CAM
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st.write("Generating Grad-CAM visualizations")
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try:
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