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Avoid warnings caused by deprecated streamlit usages #49

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Avoid warnings caused by deprecated streamlit usages #49

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906a759
avoid ugly warnings by migrating from st.cache to st.cache_data and s…
chenkigba Apr 27, 2024
46b4e62
avoid ugly warnings by migrating from st.cache to st.cache_data and s…
chenkigba Apr 27, 2024
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17 changes: 9 additions & 8 deletions streamlit_app.py
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Original file line number Diff line number Diff line change
Expand Up @@ -84,14 +84,14 @@ def download_file(file_path):

# This is the main app app itself, which appears when the user selects "Run the app".
def run_the_app():
# To make Streamlit fast, st.cache allows us to reuse computation across runs.
# To make Streamlit fast, st.cache_data allows us to reuse computation across runs.
# In this common pattern, we download data from an endpoint only once.
@st.experimental_memo
@st.cache_data
def load_metadata(url):
return pd.read_csv(url)

# This function uses some Pandas magic to summarize the metadata Dataframe.
@st.experimental_memo
@st.cache_data
def create_summary(metadata):
one_hot_encoded = pd.get_dummies(metadata[["frame", "label"]], columns=["label"])
summary = one_hot_encoded.groupby(["frame"]).sum().rename(columns={
Expand Down Expand Up @@ -164,7 +164,7 @@ def frame_selector_ui(summary):
return selected_frame_index, selected_frame

# Select frames based on the selection in the sidebar
@st.cache(hash_funcs={np.ufunc: str})
@st.cache_data(hash_funcs={np.ufunc: str})
def get_selected_frames(summary, label, min_elts, max_elts):
return summary[np.logical_and(summary[label] >= min_elts, summary[label] <= max_elts)].index

Expand Down Expand Up @@ -196,15 +196,15 @@ def draw_image_with_boxes(image, boxes, header, description):
st.image(image_with_boxes.astype(np.uint8), use_column_width=True)

# Download a single file and make its content available as a string.
@st.experimental_singleton(show_spinner=False)
@st.cache_resource (show_spinner=False)
def get_file_content_as_string(path):
url = 'https://raw.githubusercontent.com/streamlit/demo-self-driving/master/' + path
response = urllib.request.urlopen(url)
return response.read().decode("utf-8")

# This function loads an image from Streamlit public repo on S3. We use st.cache on this
# This function loads an image from Streamlit public repo on S3. We use st.cache_data on this
# function as well, so we can reuse the images across runs.
@st.experimental_memo(show_spinner=False)
@st.cache_data(show_spinner=False)
def load_image(url):
with urllib.request.urlopen(url) as response:
image = np.asarray(bytearray(response.read()), dtype="uint8")
Expand All @@ -215,7 +215,8 @@ def load_image(url):
# Run the YOLO model to detect objects.
def yolo_v3(image, confidence_threshold, overlap_threshold):
# Load the network. Because this is cached it will only happen once.
@st.cache(allow_output_mutation=True)
# For unserializable object, we use st.cache_resource() to migrate from st.cache().
@st.cache_resource()
def load_network(config_path, weights_path):
net = cv2.dnn.readNetFromDarknet(config_path, weights_path)
output_layer_names = net.getLayerNames()
Expand Down