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Python streamlit.cache方法代码示例

本文整理汇总了Python中streamlit.cache方法的典型用法代码示例。如果您正苦于以下问题:Python streamlit.cache方法的具体用法?Python streamlit.cache怎么用?Python streamlit.cache使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在streamlit的用法示例。


在下文中一共展示了streamlit.cache方法的3个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: get_file_content_as_string

# 需要导入模块: import streamlit [as 别名]
# 或者: from streamlit import cache [as 别名]
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
# function as well, so we can reuse the images across runs. 
开发者ID:streamlit,项目名称:demo-self-driving,代码行数:9,代码来源:app.py

示例2: get_batch

# 需要导入模块: import streamlit [as 别名]
# 或者: from streamlit import cache [as 别名]
def get_batch(device):
    # gets a random batch using cached load
    @st.cache
    def load_batch():
        return pickle.load(open(DATAPATH + 'batch.pkl', 'rb'))
    # todo remove randomness
    return [i.to(device) for i in random.choice(load_batch())] 
开发者ID:awarebayes,项目名称:RecNN,代码行数:9,代码来源:streamlit_demo.py

示例3: run_the_app

# 需要导入模块: import streamlit [as 别名]
# 或者: from streamlit import cache [as 别名]
def run_the_app():
    # To make Streamlit fast, st.cache allows us to reuse computation across runs.
    # In this common pattern, we download data from an endpoint only once.
    @st.cache
    def load_metadata(url):
        return pd.read_csv(url)

    # This function uses some Pandas magic to summarize the metadata Dataframe.
    @st.cache
    def create_summary(metadata):
        one_hot_encoded = pd.get_dummies(metadata[["frame", "label"]], columns=["label"])
        summary = one_hot_encoded.groupby(["frame"]).sum().rename(columns={
            "label_biker": "biker",
            "label_car": "car",
            "label_pedestrian": "pedestrian",
            "label_trafficLight": "traffic light",
            "label_truck": "truck"
        })
        return summary

    # An amazing property of st.cached functions is that you can pipe them into
    # one another to form a computation DAG (directed acyclic graph). Streamlit
    # recomputes only whatever subset is required to get the right answer!
    metadata = load_metadata(os.path.join(DATA_URL_ROOT, "labels.csv.gz"))
    summary = create_summary(metadata)

    # Uncomment these lines to peek at these DataFrames.
    # st.write('## Metadata', metadata[:1000], '## Summary', summary[:1000])

    # Draw the UI elements to search for objects (pedestrians, cars, etc.)
    selected_frame_index, selected_frame = frame_selector_ui(summary)
    if selected_frame_index == None:
        st.error("No frames fit the criteria. Please select different label or number.")
        return

    # Draw the UI element to select parameters for the YOLO object detector.
    confidence_threshold, overlap_threshold = object_detector_ui()

    # Load the image from S3.
    image_url = os.path.join(DATA_URL_ROOT, selected_frame)
    image = load_image(image_url)

    # Add boxes for objects on the image. These are the boxes for the ground image.
    boxes = metadata[metadata.frame == selected_frame].drop(columns=["frame"])
    draw_image_with_boxes(image, boxes, "Ground Truth",
        "**Human-annotated data** (frame `%i`)" % selected_frame_index)

    # Get the boxes for the objects detected by YOLO by running the YOLO model.
    yolo_boxes = yolo_v3(image, confidence_threshold, overlap_threshold)
    draw_image_with_boxes(image, yolo_boxes, "Real-time Computer Vision",
        "**YOLO v3 Model** (overlap `%3.1f`) (confidence `%3.1f`)" % (overlap_threshold, confidence_threshold))

# This sidebar UI is a little search engine to find certain object types. 
开发者ID:streamlit,项目名称:demo-self-driving,代码行数:55,代码来源:app.py


注:本文中的streamlit.cache方法示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台,相关代码片段筛选自各路编程大神贡献的开源项目,源码版权归原作者所有,传播和使用请参考对应项目的License;未经允许,请勿转载。