Python streamlit.write方法代碼示例

# 需要導入模塊: import streamlit [as 別名]
# 或者: from streamlit import write [as 別名]
def load_data(sql_path, table_path, use_small=False):
    sql_data = []
    table_data = {}
    st.write("Loading data from %s" % sql_path)
    with open(sql_path) as lines:
        for idx, line in enumerate(lines):
            if use_small and idx >= 1000:
                break
            sql = json.loads(line.strip())
            sql_data.append(sql)
    with open(table_path) as lines:
        for line in lines:
            tab = json.loads(line.strip())
            table_data[tab[u'id']] = tab

    for sql in sql_data:
        assert sql[u'table_id'] in table_data
    return sql_data, table_data 

# 需要導入模塊: import streamlit [as 別名]
# 或者: from streamlit import write [as 別名]
def get_mov_base():
    links = load_links()
    movies_embeddings_tensor, key_to_id, id_to_key = get_embeddings()
    meta = load_omdb_meta()

    popular = pd.read_csv(DATAPATH + 'movie_counts.csv')[:SHOW_TOPN_MOVIES]
    st.write(popular['id'])
    mov_base = {}

    for i, k in list(meta.items()):
        tmdid = int(meta[i]['tmdbId'])
        if tmdid > 0 and popular['id'].isin([i]).any():
            movieid = pd.to_numeric(links.loc[tmdid]['movieId'])
            if isinstance(movieid, pd.Series):
                continue
            mov_base[int(movieid)] = meta[i]['omdb']['Title']

    return mov_base 

# 需要導入模塊: import streamlit [as 別名]
# 或者: from streamlit import write [as 別名]
def plot_dist(alpha_value: float, beta_value: float, data: np.ndarray = None):
    beta_dist = beta(alpha_value, beta_value)

    xs = np.linspace(0, 1, 1000)
    ys = beta_dist.pdf(xs)

    fig, ax = plt.subplots(figsize=(7, 3))
    ax.plot(xs, ys)
    ax.set_xlim(0, 1)
    ax.set_xlabel("x")
    ax.set_ylabel("P(x)")

    if data is not None:
        likelihoods = beta_dist.pdf(data)
        sum_log_likelihoods = np.sum(beta_dist.logpdf(data))
        ax.vlines(data, ymin=0, ymax=likelihoods)
        ax.scatter(data, likelihoods, color="black")
        st.write(
            f"""
_Under your alpha={alpha_slider:.2f} and beta={beta_slider:.2f},
the sum of log likelihoods is {sum_log_likelihoods:.2f}_
"""
        )
    st.pyplot(fig) 

# 需要導入模塊: import streamlit [as 別名]
# 或者: from streamlit import write [as 別名]
def download_file(file_path):
    # Don't download the file twice. (If possible, verify the download using the file length.)
    if os.path.exists(file_path):
        if "size" not in EXTERNAL_DEPENDENCIES[file_path]:
            return
        elif os.path.getsize(file_path) == EXTERNAL_DEPENDENCIES[file_path]["size"]:
            return

    # These are handles to two visual elements to animate.
    weights_warning, progress_bar = None, None
    try:
        weights_warning = st.warning("Downloading %s..." % file_path)
        progress_bar = st.progress(0)
        with open(file_path, "wb") as output_file:
            with urllib.request.urlopen(EXTERNAL_DEPENDENCIES[file_path]["url"]) as response:
                length = int(response.info()["Content-Length"])
                counter = 0.0
                MEGABYTES = 2.0 ** 20.0
                while True:
                    data = response.read(8192)
                    if not data:
                        break
                    counter += len(data)
                    output_file.write(data)

                    # We perform animation by overwriting the elements.
                    weights_warning.warning("Downloading %s... (%6.2f/%6.2f MB)" %
                        (file_path, counter / MEGABYTES, length / MEGABYTES))
                    progress_bar.progress(min(counter / length, 1.0))

    # Finally, we remove these visual elements by calling .empty().
    finally:
        if weights_warning is not None:
            weights_warning.empty()
        if progress_bar is not None:
            progress_bar.empty()

# This is the main app app itself, which appears when the user selects "Run the app". 

# 需要導入模塊: import streamlit [as 別名]
# 或者: from streamlit import write [as 別名]
def print_sample_data(index, sql_data, table_data):
    query = qu.Query(sql_data[index]['sql']['sel'], sql_data[index]['sql']['agg'], 
        sql_data[index]['sql']['conds'])
    st.write('**Sample data:**')
    st.write('*Question*: %s' % sql_data[index][u'question'])
    st.write('*Query*: %s' % repr(query))
    st.write('*Table columns*: %s' % ', '.join(['{}: {}'.format(i, x) for i,x in \
        enumerate(table_data[sql_data[index][u'table_id']][u'header'])])) 

# 需要導入模塊: import streamlit [as 別名]
# 或者: from streamlit import write [as 別名]
def on_connect(client, userdata, flags, rc):
    st.write(
        f"Connected with result code {str(rc)} to MQTT broker on {MQTT_BROKER}"
    )


# The callback for when a PUBLISH message is received from the server. 

# 需要導入模塊: import streamlit [as 別名]
# 或者: from streamlit import write [as 別名]
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.