Python streamlit.markdown() Examples

def main():
    # Render the readme as markdown using st.markdown.
    readme_text = st.markdown(get_file_content_as_string("instructions.md"))

    # Download external dependencies.
    for filename in EXTERNAL_DEPENDENCIES.keys():
        download_file(filename)

    # Once we have the dependencies, add a selector for the app mode on the sidebar.
    st.sidebar.title("What to do")
    app_mode = st.sidebar.selectbox("Choose the app mode",
        ["Show instructions", "Run the app", "Show the source code"])
    if app_mode == "Show instructions":
        st.sidebar.success('To continue select "Run the app".')
    elif app_mode == "Show the source code":
        readme_text.empty()
        st.code(get_file_content_as_string("app.py"))
    elif app_mode == "Run the app":
        readme_text.empty()
        run_the_app()

# This file downloader demonstrates Streamlit animation. 

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. 

def frame_selector_ui(summary):
    st.sidebar.markdown("# Frame")

    # The user can pick which type of object to search for.
    object_type = st.sidebar.selectbox("Search for which objects?", summary.columns, 2)

    # The user can select a range for how many of the selected objecgt should be present.
    min_elts, max_elts = st.sidebar.slider("How many %ss (select a range)?" % object_type, 0, 25, [10, 20])
    selected_frames = get_selected_frames(summary, object_type, min_elts, max_elts)
    if len(selected_frames) < 1:
        return None, None

    # Choose a frame out of the selected frames.
    selected_frame_index = st.sidebar.slider("Choose a frame (index)", 0, len(selected_frames) - 1, 0)

    # Draw an altair chart in the sidebar with information on the frame.
    objects_per_frame = summary.loc[selected_frames, object_type].reset_index(drop=True).reset_index()
    chart = alt.Chart(objects_per_frame, height=120).mark_area().encode(
        alt.X("index:Q", scale=alt.Scale(nice=False)),
        alt.Y("%s:Q" % object_type))
    selected_frame_df = pd.DataFrame({"selected_frame": [selected_frame_index]})
    vline = alt.Chart(selected_frame_df).mark_rule(color="red").encode(
        alt.X("selected_frame:Q", axis=None)
    )
    st.sidebar.altair_chart(alt.layer(chart, vline))

    selected_frame = selected_frames[selected_frame_index]
    return selected_frame_index, selected_frame

# Select frames based on the selection in the sidebar 

def object_detector_ui():
    st.sidebar.markdown("# Model")
    confidence_threshold = st.sidebar.slider("Confidence threshold", 0.0, 1.0, 0.5, 0.01)
    overlap_threshold = st.sidebar.slider("Overlap threshold", 0.0, 1.0, 0.3, 0.01)
    return confidence_threshold, overlap_threshold

# Draws an image with boxes overlayed to indicate the presence of cars, pedestrians etc. 

def show_metrics(metrics: Dict[str, Any]):
    st.header("Metrics")
    md = ""
    for name, value in metrics.items():
        md += f"- **{name}:** {value:.4f}\n"
    st.markdown(md) 

def predict_set(timeseries, y, seasonality, transformation_function, model, exog_variables=None,forecast=False, show_train_prediction=None, show_test_prediction=None):
    '''
    Predicts the in-sample train observations

    Args.
        timeseries (Pandas Series): a time series that was used to fit a model
        y (str): the target column
        seasonality (int): the seasonality frequency
        transformation_function (func): a function used to transform the target values
        model (Statsmodel object): a fitted model
        exog_variables (Pandas DataFrame): exogenous (independent) variables of your model
        forecast (bool): wether or not forecast the test set
        show_train_prediction (bool): wether or not to plot the train set predictions
        show_test_prediction (bool): wether or not to plot the test set predictions
    '''
    timeseries = timeseries.to_frame()
    timeseries[y] = transformation_function(timeseries[y])

    if forecast:
        timeseries['ŷ'] = transformation_function(model.forecast(len(timeseries), exog=exog_variables))
    else:
        timeseries['ŷ'] = transformation_function(model.predict())
    
    if show_train_prediction and forecast == False:
        timeseries[[y, 'ŷ']].iloc[-(seasonality*3):].plot(color=['green', 'red'])

        plt.ylabel(y)
        plt.xlabel('')
        plt.title('Train set predictions')
        st.pyplot()
    elif show_test_prediction and forecast:
        timeseries[[y, 'ŷ']].iloc[-(seasonality*3):].plot(color=['green', 'red'])

        plt.ylabel(y)
        plt.xlabel('')
        plt.title('Test set predictions')
        st.pyplot()

    try:
        rmse = sqrt(mean_squared_error(timeseries[y].iloc[-(seasonality*3):], timeseries['ŷ'].iloc[-(seasonality*3):]))
        aic = model.aic
        bic = model.bic
        hqic = model.hqic
        mape = np.round(mean_abs_pct_error(timeseries[y].iloc[-(seasonality*3):], timeseries['ŷ'].iloc[-(seasonality*3):]), 2)
        mae = np.round(mean_absolute_error(timeseries[y].iloc[-(seasonality*3):], timeseries['ŷ'].iloc[-(seasonality*3):]), 2)
    except ValueError:
        error_message = '''
                        There was a problem while we calculated the model metrics. 
                        Usually this is due a problem with the format of the DATE column. 
                        Be sure it is in a valid format for Pandas to_datetime function
                        '''
        raise ValueError(error_message)
    
    metrics_df = pd.DataFrame(data=[rmse, aic, bic, hqic, mape, mae], columns = ['{} SET METRICS'.format('TEST' if forecast else 'TRAIN')], index = ['RMSE', 'AIC', 'BIC', 'HQIC', 'MAPE', 'MAE'])
    st.markdown('### **Metrics**')
    st.dataframe(metrics_df)