Python streamlit.subheader() Examples

The following are 3 code examples of streamlit.subheader(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module streamlit , or try the search function .
Example #1
Source File: app.py    From demo-self-driving with Apache License 2.0 6 votes vote down vote up 
def draw_image_with_boxes(image, boxes, header, description):
    # Superpose the semi-transparent object detection boxes.    # Colors for the boxes
    LABEL_COLORS = {
        "car": [255, 0, 0],
        "pedestrian": [0, 255, 0],
        "truck": [0, 0, 255],
        "trafficLight": [255, 255, 0],
        "biker": [255, 0, 255],
    }
    image_with_boxes = image.astype(np.float64)
    for _, (xmin, ymin, xmax, ymax, label) in boxes.iterrows():
        image_with_boxes[int(ymin):int(ymax),int(xmin):int(xmax),:] += LABEL_COLORS[label]
        image_with_boxes[int(ymin):int(ymax),int(xmin):int(xmax),:] /= 2

    # Draw the header and image.
    st.subheader(header)
    st.markdown(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.
Example #2
Source File: explain.py    From gobbli with Apache License 2.0 5 votes vote down vote up 
def st_lime_explanation(
    text: str,
    predict_func: Callable[[List[str]], np.ndarray],
    unique_labels: List[str],
    n_samples: int,
    position_dependent: bool = True,
):
    # TODO just use ELI5's built-in visualization when streamlit supports it:
    # https://github.com/streamlit/streamlit/issues/779
    with st.spinner("Generating LIME explanations..."):
        te = TextExplainer(
            random_state=1, n_samples=n_samples, position_dependent=position_dependent
        )
        te.fit(text, predict_func)
    st.json(te.metrics_)
    explanation = te.explain_prediction()
    explanation_df = eli5.format_as_dataframe(explanation)
    for target_ndx, target in enumerate(
        sorted(explanation.targets, key=lambda t: -t.proba)
    ):
        target_explanation_df = explanation_df[
            explanation_df["target"] == target_ndx
        ].copy()

        target_explanation_df["contribution"] = (
            target_explanation_df["weight"] * target_explanation_df["value"]
        )
        target_explanation_df["abs_contribution"] = abs(
            target_explanation_df["contribution"]
        )
        target_explanation_df = (
            target_explanation_df.drop("target", axis=1)
            .sort_values(by="abs_contribution", ascending=False)
            .reset_index(drop=True)
        )
        st.subheader(
            f"Target: {unique_labels[target_ndx]} (probability {target.proba:.4f}, score {target.score:.4f})"
        )
        st.dataframe(target_explanation_df)
Example #3
Source File: explore.py    From gobbli with Apache License 2.0 5 votes vote down vote up 
def show_example_documents(
    texts: List[str],
    labels: Union[List[str], List[List[str]]],
    filter_label: Optional[str],
    example_truncate_len: int,
    example_num_docs: int,
):
    st.header("Example Documents")

    # If we're filtered to a specific label,
    # just show it once at the top -- otherwise, show the label
    # with each example
    if filter_label is not None:
        st.subheader(f"Label: {filter_label}")
        example_labels = None
    else:
        example_labels = labels

    example_indices = safe_sample(range(len(texts)), example_num_docs)
    _show_example_documents(
        [texts[i] for i in example_indices],
        [example_labels[i] for i in example_indices]
        if example_labels is not None
        else None,
        example_truncate_len,
    )