First commit.

.gitignore

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+venv/
+.idea/

download.py

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+import thermostat
+
+data = thermostat.load("imdb-bert-lig", cache_dir="~/datasets")

requirements.txt

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+thermostat-datasets==1.0.1

run.py

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+import datasets
+import glob
+import json
+import pandas as pd
+import streamlit as st
+import sys
+import textwrap
+
+from thermostat import load
+from thermostat.data.thermostat_configs import builder_configs
+
+
+nlp = datasets
+
+HTML_WRAPPER = """<div>{}</div>"""
+#HTML_WRAPPER = """<div style="overflow-x: auto; border: 1px solid #e6e9ef; border-radius: 0.25rem; padding: 1rem;
+#                  margin-bottom: 2.5rem">{}</div>"""
+MAX_SIZE = 40000000000
+if len(sys.argv) > 1:
+    path_to_datasets = sys.argv[1]
+else:
+    path_to_datasets = None
+
+
+# Hack to extend the width of the main pane.
+def _max_width_():
+    max_width_str = f"max-width: 1000px;"
+    st.markdown(
+        f"""
+    <style>
+    .reportview-container .main .block-container{{
+        {max_width_str}
+    }}
+    th {{
+        text-align: left;
+        font-size: 110%;
+
+
+     }}
+
+    tr:hover {{
+        background-color: #ffff99;
+        }}
+
+    </style>
+    """,
+        unsafe_allow_html=True,
+    )
+
+
+_max_width_()
+
+
+def render_features(features):
+    if isinstance(features, dict):
+        return {k: render_features(v) for k, v in features.items()}
+    if isinstance(features, nlp.features.ClassLabel):
+        return features.names
+
+    if isinstance(features, nlp.features.Value):
+        return features.dtype
+
+    if isinstance(features, nlp.features.Sequence):
+        return {"[]": render_features(features.feature)}
+    return features
+
+
+app_state = st.experimental_get_query_params()
+start = True
+loaded = True
+INITIAL_SELECTION = ""
+
+app_state.setdefault("dataset", "glue")
+if len(app_state.get("dataset", [])) == 1:
+    app_state["dataset"] = app_state["dataset"][0]
+    INITIAL_SELECTION = app_state["dataset"]
+#print(INITIAL_SELECTION)
+
+if start:
+    # Logo and sidebar decoration.
+    st.sidebar.markdown(
+        """<center>
+    <a href="https://github.com/DFKI-NLP/thermostat">
+    </a>
+    </center>""",
+        unsafe_allow_html=True,
+    )
+    st.sidebar.image("logo.png", width=300)
+    st.sidebar.markdown(
+        "<center><h2><a href='https://github.com/DFKI-NLP/thermostat'>github/DFKI-NLP/thermostat</h2></a></center>",
+        unsafe_allow_html=True,
+    )
+    st.sidebar.markdown(
+        """
+    <center>
+        <a target="_blank" href="https://huggingface.co/docs/datasets/">datasets Docs</a>
+    </center>""",
+        unsafe_allow_html=True,
+    )
+    st.sidebar.subheader("")
+
+    # Interaction with the nlp libary.
+    # @st.cache
+    def get_confs():
+        """  Get the list of confs for a dataset. """
+        confs = builder_configs
+        if confs and len(confs) > 1:
+            return confs
+        else:
+            return []
+
+    # @st.cache(allow_output_mutation=True)
+    def get(conf):
+        """ Get a dataset from name and conf """
+        ds = load(conf, cache_dir=path_to_datasets)
+        return ds, False
+
+    # Dataset select box.
+    datasets = []
+    selection = None
+
+    if path_to_datasets is None:
+        list_of_datasets = nlp.list_datasets(with_community_datasets=False)
+    else:
+        list_of_datasets = sorted(glob.glob(path_to_datasets + "*"))
+    for i, dataset in enumerate(list_of_datasets):
+        dataset = dataset.split("/")[-1]
+        if INITIAL_SELECTION and dataset == INITIAL_SELECTION:
+            selection = i
+        datasets.append(dataset)
+
+    st.experimental_set_query_params(**app_state)
+
+    # Side bar Configurations.
+    configs = get_confs()
+    conf_avail = len(configs) > 0
+    conf_option = None
+    if conf_avail:
+        start = 0
+        for i, conf in enumerate(configs):
+            if conf.name == app_state.get("config", None):
+                start = i
+        conf_option = st.sidebar.selectbox(
+            "Thermostat configuration", configs, index=start, format_func=lambda a: a.name
+        )
+        app_state["config"] = conf_option.name
+
+    else:
+        if "config" in app_state:
+            del app_state["config"]
+    st.experimental_set_query_params(**app_state)
+
+    dts, fail = get(str(conf_option.name) if conf_option else None)
+
+    # Main panel setup.
+    if fail:
+        st.markdown(
+            "Dataset is too large to browse or requires manual download. Check it out in the datasets library! \n\n "
+            "Size: "
+            + str(dts.info.size_in_bytes)
+            + "\n\n Instructions: "
+            + str(dts.manual_download_instructions)
+        )
+    else:
+        d = dts
+        keys = list(d[0].__dict__.keys())
+
+        st.header(
+            "Thermostat configuration: "
+            + (conf_option.name if conf_option else "")
+        )
+
+        st.markdown(
+            "*Homepage*: "
+            + d.info.homepage
+        )
+
+        md = """
+        %s
+        """ % (
+            d.info.description.replace("\\", "  ")
+        )
+        st.markdown(md)
+
+        step = 50
+        offset = st.sidebar.number_input(
+            "Offset (Size: %d)" % len(d),
+            min_value=0,
+            max_value=int(len(d)) - step,
+            value=0,
+            step=step,
+        )
+
+        citation = None #st.sidebar.checkbox("Show Citations", False)
+        table = not st.sidebar.checkbox("Show List View", False)
+        show_features = st.sidebar.checkbox("Show Features", True)
+        show_atts = st.sidebar.checkbox("Show Attribution Scores", False)
+        md = """
+```
+%s
+```
+""" % (
+            d.info.citation.replace("\\", "").replace("}", " }").replace("{", "{ "),
+        )
+        if citation:
+            st.markdown(md)
+        # st.text("Features:")
+        #if show_features:
+        #    on_keys = st.multiselect("Features", keys, keys)
+        #    #st.write(render_features(d.features))
+        #else:
+        on_keys = keys
+
+        # Remove some keys
+        on_keys = [k for k in on_keys if k in ['predictions', 'true_label', 'predicted_label']]
+
+        if not table:
+            # Full view.
+            for item in range(offset, offset + step):
+                st.text("        ")
+                st.text("                  ----  #" + str(item))
+                st.text("        ")
+                # Use st to write out.
+                for k in on_keys:
+                    v = getattr(d[item], k)
+                    st.subheader(k)
+                    if isinstance(v, str):
+                        out = v
+                        st.text(textwrap.fill(out, width=120))
+                    elif (
+                            isinstance(v, bool)
+                            or isinstance(v, int)
+                            or isinstance(v, float)
+                    ):
+                        st.text(v)
+                    else:
+                        st.write(v)
+
+        else:
+            # Table view. Use Pandas.
+            df, heatmap_htmls = [], []
+            for item in range(offset, offset + step):
+                df_item = {}
+                df_item["_number"] = item
+                for k in on_keys:
+                    v = getattr(d[item], k)
+
+                    # Remove [PAD] tokens from attributions and input_ids
+                    if k in ['attributions', 'input_ids']:
+                        v = [vi for vi in v if vi != 0 or vi != 0.0]
+
+                    if isinstance(v, str):
+                        out = v
+                        df_item[k] = textwrap.fill(out, width=50)
+                    elif (
+                            isinstance(v, bool)
+                            or isinstance(v, int)
+                            or isinstance(v, float)
+                    ):
+                        df_item[k] = v
+                    else:
+                        out = json.dumps(v, indent=2, sort_keys=True)
+                        df_item[k] = out
+
+                # Add heatmap viz
+                html = getattr(d[item], 'heatmap').render(labels=show_atts)
+                html = html.replace("\n", " ")
+                heatmap_htmls.append(HTML_WRAPPER.format(html))
+
+                df.append(df_item)
+            df2 = df
+            df = pd.DataFrame(df).set_index("_number")
+
+            def hover(hover_color="#ffff99"):
+                return dict(
+                    selector="tr:hover",
+                    props=[("background-color", "%s" % hover_color)],
+                )
+
+            styles = [
+                hover(),
+                dict(
+                    selector="th",
+                    props=[("font-size", "150%"), ("text-align", "center")],
+                ),
+                dict(selector="caption", props=[("caption-side", "bottom")]),
+            ]
+            # Table view. Use pands styling.
+            style = df.style.set_properties(
+                **{"text-align": "left", "white-space": "pre"}
+            ).set_table_styles([dict(selector="th", props=[("text-align", "left")])])
+            style = style.set_table_styles(styles)  # Setting the style appears to be broken for streamlit+pandas
+
+            for i, heatmap_html in enumerate(heatmap_htmls):
+                st.write(HTML_WRAPPER.format(heatmap_html), unsafe_allow_html=True)
+                st.table(df.iloc[[i]])
+                st.markdown(""" --- """)
+
+    # Additional dataset installation and sidebar properties.
+    md = """
+    ### Code
+
+    ```python
+    !pip install thermostat_datasets
+    from thermostat import load
+    dataset = load(
+       '%s)
+    ```
+
+    """ % (
+        (conf_option.name + "'") if conf_option else "",
+    )
+    st.sidebar.markdown(md)