models.py

import streamlit as st
import pandas as pd
from datasets import load_dataset
from ast import literal_eval
import altair as alt
import plotly.graph_objs as go
import matplotlib.pyplot as plt

def main():
    print("Build")
    nlp_tasks = ["text-classification", "text-generation", "text2text-generation", "token-classification", "fill-mask", "question-answering",
                "translation", "conversational", "sentence-similarity", "summarization", "multiple-choice", "zero-shot-classification", "table-question-answering"
    ]
    audio_tasks = ["automatic-speech-recognition", "audio-classification", "text-to-speech", "audio-to-audio", "voice-activity-detection"]
    cv_tasks = ["image-classification", "image-segmentation", "zero-shot-image-classification", "image-to-image", "unconditional-image-generation", "object-detection"]
    multimodal = ["feature-extraction", "text-to-image", "visual-question-answering", "image-to-text", "document-question-answering"]
    tabular = ["tabular-classification", "tabular-regression"]

    modalities = {
        "nlp": nlp_tasks,
        "audio": audio_tasks,
        "cv": cv_tasks,
        "multimodal": multimodal,
        "tabular": tabular,
        "rl": ["reinforcement-learning"]
    }

    def modality(row):
        pipeline = row["pipeline"]
        for modality, tasks in modalities.items():
            if pipeline in tasks:
                return modality
        if type(pipeline) == "str":
            return "unk_modality"
        return None

    supported_revisions = ["03_10_22", "27_09_22"]

    st.cache(allow_output_mutation=True)
    def process_dataset(version):
        # Load dataset at specified revision
        dataset = load_dataset("open-source-metrics/model-repos-stats", revision=version)

        # Convert to pandas dataframe
        data = dataset["train"].to_pandas()

        # Add modality column
        data["modality"] = data.apply(modality, axis=1)

        # Bin the model card length into some bins
        data["length_bins"] = pd.cut(data["text_length"], [0, 200, 1000, 2000, 3000, 4000, 5000, 7500, 10000, 20000, 50000])

        return data

    col1, col2 = st.columns(2)
    with col1:
        base = st.selectbox(
            'Old revision',
            supported_revisions,
            index=1)
    with col2:
        new = st.selectbox(
            'Last revision',
            supported_revisions,
            index=0)
    
    old_data = process_dataset(base)
    data = process_dataset(new)

    def eval_tags(row):
        tags = row["tags"]
        if tags == "none" or tags == [] or tags == "{}":
            return []
        if tags[0] != "[":
            tags = str([tags])
        val = literal_eval(tags)
        if isinstance(val, dict):
            return []
        return val

    old_data["tags"] = old_data.apply(eval_tags, axis=1)
    data["tags"] = data.apply(eval_tags, axis=1)

    total_samples_old = old_data.shape[0]
    total_samples = data.shape[0]
    st.metric(label="Total models", value=total_samples, delta=total_samples-total_samples_old)

    # Tabs don't work in Spaces st version
    #tab1, tab2, tab3, tab4, tab5, tab6, tab7, tab8 = st.tabs(["Language", "License", "Pipeline", "Discussion Features", "Libraries", "Model Cards", "Super users", "Raw Data"])

    tab = st.selectbox(
            'Topic of interest',
            ["Language", "License", "Pipeline", "Discussion Features", "Libraries", "Model Cards", "Super Users", "Raw Data"])

    # with tab1:
    if tab == "Language":
        st.header("Languages info")

        data.loc[data.languages == "False", 'languages'] = None
        data.loc[data.languages == {}, 'languages'] = None
        old_data.loc[old_data.languages == "False", 'languages'] = None
        old_data.loc[old_data.languages == {}, 'languages'] = None

        no_lang_count = data["languages"].isna().sum()
        no_lang_count_old = old_data["languages"].isna().sum()
        data["languages"] = data["languages"].fillna('none')
        old_data["languages"] = old_data["languages"].fillna('none')

        def make_list(row):
            languages = row["languages"]
            if languages == "none":
                return []
            return literal_eval(languages)

        def language_count(row):
            languages = row["languages"]
            leng = len(languages)
            return leng

        data["languages"] = data.apply(make_list, axis=1)
        data["language_count"] = data.apply(language_count, axis=1)
        old_data["languages"] = old_data.apply(make_list, axis=1)
        old_data["language_count"] = old_data.apply(language_count, axis=1)

        models_with_langs = data[data["language_count"] > 0]
        langs = models_with_langs["languages"].explode()
        langs = langs[langs != {}]
        total_langs = len(langs.unique())

        models_with_langs_old = old_data[old_data["language_count"] > 0]
        langs_old = models_with_langs_old["languages"].explode()
        langs_old = langs_old[langs_old != {}]
        total_langs_old = len(langs_old.unique())

        col1, col2, col3 = st.columns(3)
        with col1:
            v = total_samples-no_lang_count
            v_old = total_samples_old-no_lang_count_old
            st.metric(label="Language Specified", value=v, delta=int(v-v_old))
        with col2:
            st.metric(label="No Language Specified", value=no_lang_count, delta=int(no_lang_count-no_lang_count_old))
        with col3:
            st.metric(label="Total Unique Languages", value=total_langs, delta=int(total_langs-total_langs_old))

        st.subheader("Count of languages per model repo")
        st.text("Some repos are for multiple languages, so the count is greater than 1")
        linguality = st.selectbox(
            'All or just Multilingual',
            ["All", "Just Multilingual", "Three or more languages"])

        filter = 0
        st.text("Tofix: This just takes into account count of languages, it misses the multilingual tag")
        if linguality == "Just Multilingual":
            filter = 1
        elif linguality == "Three or more languages":
            filter = 2

        models_with_langs = data[data["language_count"] > filter]
        df1 = models_with_langs['language_count'].value_counts()
        models_with_langs_old = old_data[old_data["language_count"] > filter]
        df1_old = models_with_langs_old['language_count'].value_counts()
        st.bar_chart(df1)

        st.subheader("Most frequent languages")
        linguality_2 = st.selectbox(
            'All or filtered',
            ["All", "No English", "Remove top 10"])

        filter = 0
        if linguality_2 == "All":
            filter = 0
        elif linguality_2 == "No English":
            filter = 1
        else:
            filter = 2

        models_with_langs = data[data["language_count"] > 0]
        langs = models_with_langs["languages"].explode()
        langs = langs[langs != {}]
        orig_d = langs.value_counts().rename_axis("language").to_frame('counts').reset_index()
        d = orig_d
        
        models_with_langs_old = old_data[old_data["language_count"] > 0]
        langs = models_with_langs_old["languages"].explode()
        langs = langs[langs != {}]
        orig_d_old = langs.value_counts().rename_axis("language").to_frame('counts').reset_index()

        if filter == 1:
            d = orig_d.iloc[1:]
        elif filter == 2:
            d = orig_d.iloc[10:]

        # Just keep top 25 to avoid vertical scroll
        d = d.iloc[:25]

        st.write(alt.Chart(d).mark_bar().encode(
            x='counts',
            y=alt.X('language', sort=None)
        ))

        st.subheader("Raw Data")
        l = df1.rename_axis("lang_count").reset_index().rename(columns={"language_count": "r_c"})
        l_old = df1_old.rename_axis("lang_count").reset_index().rename(columns={"language_count": "old_r_c"})
        final_data =  pd.merge(
            l, l_old, how="outer", on="lang_count"
        )
        final_data["diff"] = final_data["r_c"] - final_data["old_r_c"]
        st.dataframe(final_data)
        
        d = orig_d.astype(str)
        orig_d_old = orig_d_old.astype(str).rename(columns={"counts": "old_c"})
        final_data =  pd.merge(
            d, orig_d_old, how="outer", on="language"
        )
        final_data["diff"] = final_data["counts"].astype(int) - final_data["old_c"].astype(int)

        st.dataframe(final_data)
        
        

    #with tab2:
    if tab == "License":
        st.header("License info")

        no_license_count = data["license"].isna().sum()
        no_license_count_old = old_data["license"].isna().sum()
        col1, col2, col3 = st.columns(3)
        with col1:
            v = total_samples-no_license_count
            v_old = total_samples_old-no_license_count_old
            st.metric(label="License Specified", value=v, delta=int(v-v_old))
        with col2:
            st.metric(label="No license Specified", value=no_license_count, delta=int(no_license_count-no_license_count_old))
        with col3:
            unique_licenses = len(data["license"].unique())
            unique_licenses_old = len(old_data["license"].unique())
            st.metric(label="Total Unique Licenses", value=unique_licenses, delta=int(unique_licenses-unique_licenses_old))

        st.subheader("Distribution of licenses per model repo")
        license_filter = st.selectbox(
            'All or filtered',
            ["All", "No Apache 2.0", "Remove top 10"])

        filter = 0
        if license_filter == "All":
            filter = 0
        elif license_filter == "No Apache 2.0":
            filter = 1
        else:
            filter = 2

        d = data["license"].value_counts().rename_axis("license").to_frame('counts').reset_index()
        if filter == 1:
            d = d.iloc[1:]
        elif filter == 2:
            d = d.iloc[10:]

        # Just keep top 25 to avoid vertical scroll
        d = d.iloc[:25]

        st.write(alt.Chart(d).mark_bar().encode(
            x='counts',
            y=alt.X('license', sort=None)
        ))
        st.text("There are some edge cases, as old repos using lists of licenses.")

        st.subheader("Raw Data")
        d = data["license"].value_counts().rename_axis("license").to_frame('counts').reset_index()
        d_old = old_data["license"].value_counts().rename_axis("license").to_frame('counts').reset_index().rename(columns={"counts": "old_c"})
        final_data =  pd.merge(
            d, d_old, how="outer", on="license"
        )
        final_data["diff"] = final_data["counts"] - final_data["old_c"]
        st.dataframe(final_data)
        
    #with tab3:
    if tab == "Pipeline":
        st.header("Pipeline info")

        tags = data["tags"].explode()
        tags = tags[tags.notna()].value_counts().rename_axis("tag").to_frame('counts').reset_index()
        s = tags["tag"]
        s = s[s.apply(type) == str]
        unique_tags = len(s.unique())

        tags_old = old_data["tags"].explode()
        tags_old = tags_old[tags_old.notna()].value_counts().rename_axis("tag").to_frame('counts').reset_index()
        s = tags_old["tag"]
        s = s[s.apply(type) == str]
        unique_tags_old = len(s.unique())

        no_pipeline_count = data["pipeline"].isna().sum()
        no_pipeline_count_old = old_data["pipeline"].isna().sum()

        col1, col2, col3 = st.columns(3)
        with col1:
            v = total_samples-no_pipeline_count
            v_old = total_samples_old-no_pipeline_count_old
            st.metric(label="# models that have any pipeline", value=v, delta=int(v-v_old))
        with col2:
            st.metric(label="No pipeline Specified", value=no_pipeline_count, delta=int(no_pipeline_count-no_pipeline_count_old))
        with col3:
            st.metric(label="Total Unique Tags", value=unique_tags, delta=int(unique_tags-unique_tags_old))

        pipeline_filter = st.selectbox(
            'Modalities',
            ["All", "NLP", "CV", "Audio", "RL", "Multimodal", "Tabular"])

        filter = 0
        if pipeline_filter == "All":
            filter = 0
        elif pipeline_filter == "NLP":
            filter = 1
        elif pipeline_filter == "CV":
            filter = 2
        elif pipeline_filter == "Audio":
            filter = 3
        elif pipeline_filter == "RL":
            filter = 4
        elif pipeline_filter == "Multimodal":
            filter = 5
        elif pipeline_filter == "Tabular":
            filter = 6

        st.subheader("High-level metrics")
        filtered_data = data[data['pipeline'].notna()]
        filtered_data_old = old_data[old_data['pipeline'].notna()]

        if filter == 1:
            filtered_data = data[data["modality"] == "nlp"]
            filtered_data_old = old_data[old_data["modality"] == "nlp"]
        elif filter == 2:
            filtered_data = data[data["modality"] == "cv"]
            filtered_data_old = old_data[old_data["modality"] == "cv"]
        elif filter == 3:
            filtered_data = data[data["modality"] == "audio"]
            filtered_data_old = old_data[old_data["modality"] == "audio"]
        elif filter == 4:
            filtered_data = data[data["modality"] == "rl"]
            filtered_data_old = old_data[old_data["modality"] == "rl"]
        elif filter == 5:
            filtered_data = data[data["modality"] == "multimodal"]
            filtered_data_old = old_data[old_data["modality"] == "multimodal"]
        elif filter == 6:
            filtered_data = data[data["modality"] == "tabular"]
            filtered_data_old = old_data[old_data["modality"] == "tabular"]

        col1, col2, col3 = st.columns(3)
        with col1:
            p = st.selectbox(
                'What pipeline do you want to see?',
                ["all", *filtered_data["pipeline"].unique()]
            )
        with col2:
            l = st.selectbox(
                'What library do you want to see?',
                ["all", "not transformers", *filtered_data["library"].unique()]
            )
        with col3:
            f = st.selectbox(
                'What framework support? (transformers)',
                ["all", "py", "tf", "jax"]
            ) 

        col1, col2 = st.columns(2)
        with col1:
            filt = st.multiselect(
                label="Tags (All by default)",
                options=s.unique(),
                default=None)
        with col2:
            o = st.selectbox(
                label="Operation (for tags)",
                options=["Any", "All", "None"]
            )

        def filter_fn(row):
            tags = row["tags"]
            tags[:] = [d for d in tags if isinstance(d, str)]
            if o == "All":
                if all(elem in tags for elem in filt):
                    return True

            s1 = set(tags)
            s2 = set(filt)
            if o == "Any":
                if bool(s1 & s2):
                    return True
            if o == "None":
                if len(s1.intersection(s2)) == 0:
                    return True
            return False

        
        if p != "all":
            filtered_data = filtered_data[filtered_data["pipeline"] == p]
            filtered_data_old = filtered_data_old[filtered_data_old["pipeline"] == p]
        if l != "all" and l != "not transformers":
            filtered_data = filtered_data[filtered_data["library"] == l]
            filtered_data_old = filtered_data_old[filtered_data_old["library"] == l]
        if l == "not transformers":
            filtered_data = filtered_data[filtered_data["library"] != "transformers"]
            filtered_data_old = filtered_data_old[filtered_data_old["library"] != "transformers"]
        if f != "all":
            if f == "py":
                filtered_data = filtered_data[filtered_data["pytorch"] == 1]
                filtered_data_old = filtered_data_old[filtered_data_old["pytorch"] == 1]
            elif f == "tf":
                filtered_data = filtered_data[filtered_data["tensorflow"] == 1]
                filtered_data_old = filtered_data_old[filtered_data_old["tensorflow"] == 1]
            elif f == "jax":
                filtered_data = filtered_data[filtered_data["jax"] == 1]
                filtered_data_old = filtered_data_old[filtered_data_old["jax"] == 1]
        if filt != []:
            filtered_data = filtered_data[filtered_data.apply(filter_fn, axis=1)]
            filtered_data_old = filtered_data_old[filtered_data_old.apply(filter_fn, axis=1)]


        d = filtered_data["pipeline"].value_counts().rename_axis("pipeline").to_frame('counts').reset_index()
        columns_of_interest = ["downloads_30d", "likes", "pytorch", "tensorflow", "jax"]
        grouped_data = filtered_data.groupby("pipeline").sum()[columns_of_interest]
        final_data = pd.merge(
            d, grouped_data, how="outer", on="pipeline"
        )
        sums = grouped_data.sum()

        d_old = filtered_data_old["pipeline"].value_counts().rename_axis("pipeline").to_frame('counts').reset_index()
        grouped_data_old = filtered_data_old.groupby("pipeline").sum()[columns_of_interest]
        final_data_old = pd.merge(
            d_old, grouped_data_old, how="outer", on="pipeline"
        )
        sums = grouped_data.sum()
        sums_old = grouped_data_old.sum()

        col1, col2, col3 = st.columns(3)
        with col1:
            st.metric(label="Total models", value=filtered_data.shape[0], delta=int(filtered_data.shape[0] - filtered_data_old.shape[0]))
        with col2:
            st.metric(label="Cumulative Downloads (30d)", value=sums["downloads_30d"], delta=int(sums["downloads_30d"] - sums_old["downloads_30d"]))
        with col3:
            st.metric(label="Cumulative likes", value=sums["likes"], delta=int(sums["likes"] - sums_old["likes"]))

        col1, col2, col3 = st.columns(3)
        with col1:
            st.metric(label="Total in PT", value=sums["pytorch"], delta=int(sums["pytorch"] - sums_old["pytorch"]))
        with col2:
            st.metric(label="Total in TF", value=sums["tensorflow"], delta=int(sums["tensorflow"] - sums_old["tensorflow"]))
        with col3:
            st.metric(label="Total in JAX", value=sums["jax"], delta=int(sums["jax"] - sums_old["jax"]))
        
        st.metric(label="Unique Tags", value=unique_tags, delta=int(unique_tags - unique_tags_old))

        

        st.subheader("Count of models per pipeline")
        st.write(alt.Chart(d).mark_bar().encode(
            x='counts',
            y=alt.X('pipeline', sort=None)
        ))

        st.subheader("Aggregated data")
        st.dataframe(final_data)

        st.subheader("Most common model types (specific to transformers)")
        d = filtered_data["model_type"].value_counts().rename_axis("model_type").to_frame('counts').reset_index()
        d = d.iloc[:15]
        st.write(alt.Chart(d).mark_bar().encode(
            x='counts',
            y=alt.X('model_type', sort=None)
        ))

        st.subheader("Most common library types (Learn more in library tab)")
        d = filtered_data["library"].value_counts().rename_axis("library").to_frame('counts').reset_index().head(15)
        st.write(alt.Chart(d).mark_bar().encode(
            x='counts',
            y=alt.X('library', sort=None)
        ))
        
        st.subheader("Tags by count")
        tags = filtered_data["tags"].explode()
        tags = tags[tags.notna()].value_counts().rename_axis("tag").to_frame('counts').reset_index()
        st.write(alt.Chart(tags.head(30)).mark_bar().encode(
            x='counts',
            y=alt.X('tag', sort=None)
        ))
        
        st.subheader("Raw Data")
        columns_of_interest = [
            "repo_id", "author", "model_type", "files_per_repo", "library",
            "downloads_30d", "likes", "pytorch", "tensorflow", "jax"]
        raw_data = filtered_data[columns_of_interest]
        st.dataframe(raw_data)
        
        

        # todo : add activity metric


    #with tab4:
    if tab == "Discussion Features":
        st.header("Discussions Tab info")

        columns_of_interest = ["prs_count", "prs_open", "prs_merged", "prs_closed", "discussions_count", "discussions_open", "discussions_closed"]
        sums = data[columns_of_interest].sum()
        sums_old = old_data[columns_of_interest].sum()

        col1, col2, col3, col4 = st.columns(4)
        with col1:
            st.metric(label="Total PRs", value=sums["prs_count"],delta=int(sums["prs_count"] - sums_old["prs_count"]))
        with col2:
            st.metric(label="PRs opened", value=sums["prs_open"], delta=int(sums["prs_open"] - sums_old["prs_open"]))
        with col3:
            st.metric(label="PRs merged", value=sums["prs_merged"], delta=int(sums["prs_merged"] - sums_old["prs_merged"]))
        with col4:
            st.metric(label="PRs closed", value=sums["prs_closed"], delta=int(sums["prs_closed"] - sums_old["prs_closed"]))

        col1, col2, col3 = st.columns(3)
        with col1:
            st.metric(label="Total discussions", value=sums["discussions_count"], delta=int(sums["discussions_count"] - sums_old["discussions_count"]))
        with col2:
            st.metric(label="Discussions open", value=sums["discussions_open"], delta=int(sums["discussions_open"] - sums_old["discussions_open"]))
        with col3:
            st.metric(label="Discussions closed", value=sums["discussions_closed"], delta=int(sums["discussions_closed"] - sums_old["discussions_closed"]))

        filtered_data = data[["repo_id", "prs_count", "prs_open", "prs_merged", "prs_closed", "discussions_count", "discussions_open", "discussions_closed"]].sort_values("prs_count", ascending=False).reset_index(drop=True)
        st.dataframe(filtered_data)

    #with tab5:
    if tab == "Libraries":
        st.header("Library info")

        no_library_count = data["library"].isna().sum()
        no_library_count_old = old_data["library"].isna().sum()
        col1, col2, col3 = st.columns(3)
        with col1:
            v = total_samples-no_library_count
            v_old = total_samples_old-no_library_count_old
            st.metric(label="# models that have any library", value=v, delta=int(v-v_old))
        with col2:
            st.metric(label="No library Specified", value=no_library_count, delta=int(no_library_count-no_library_count_old))
        with col3:
            v = len(data["library"].unique())
            v_old = len(old_data["library"].unique())
            st.metric(label="Total Unique library", value=v, delta=int(v-v_old))


        st.subheader("High-level metrics")
        filtered_data = data[data['library'].notna()]
        filtered_data_old = old_data[old_data['library'].notna()]

        col1, col2 = st.columns(2)
        with col1:
            lib = st.selectbox(
                'What library do you want to see? ',
                ["all", "not transformers", *filtered_data["library"].unique()]
            )
        with col2:
            pip = st.selectbox(
                'What pipeline do you want to see? ',
                ["all", *filtered_data["pipeline"].unique()]
            )

        if pip != "all" :
            filtered_data = filtered_data[filtered_data["pipeline"] == pip]
            filtered_data_old = filtered_data_old[filtered_data_old["pipeline"] == pip]
        if lib != "all" and lib != "not transformers":
            filtered_data = filtered_data[filtered_data["library"] == lib]
            filtered_data_old = filtered_data_old[filtered_data_old["library"] == lib]
        if lib == "not transformers":
            filtered_data = filtered_data[filtered_data["library"] != "transformers"]
            filtered_data_old = filtered_data_old[filtered_data_old["library"] != "transformers"]

        d = filtered_data["library"].value_counts().rename_axis("library").to_frame('counts').reset_index()
        grouped_data = filtered_data.groupby("library").sum()[["downloads_30d", "likes"]]
        final_data = pd.merge(
            d, grouped_data, how="outer", on="library"
        )
        sums = grouped_data.sum()

        d_old = filtered_data_old["library"].value_counts().rename_axis("library").to_frame('counts').reset_index()
        grouped_data_old = filtered_data_old.groupby("library").sum()[["downloads_30d", "likes"]]
        final_data_old = pd.merge(
            d_old, grouped_data_old, how="outer", on="library"
        ).add_suffix('_old')
        final_data_old = final_data_old.rename(index=str, columns={"library_old": "library"})
        sums_old = grouped_data_old.sum()

        col1, col2, col3 = st.columns(3)
        with col1:
            v = filtered_data.shape[0]
            v_old = filtered_data_old.shape[0]
            st.metric(label="Total models", value=v, delta=int(v-v_old))
        with col2:
            st.metric(label="Cumulative Downloads (30d)", value=sums["downloads_30d"], delta=int(sums["downloads_30d"]-sums_old["downloads_30d"]))
        with col3:
            st.metric(label="Cumulative likes", value=sums["likes"], delta=int(sums["likes"]-sums_old["likes"]))

        st.subheader("Most common library types (Learn more in library tab)")
        d = filtered_data["library"].value_counts().rename_axis("library").to_frame('counts').reset_index().head(15)
        st.write(alt.Chart(d).mark_bar().encode(
            x='counts',
            y=alt.X('library', sort=None)
        ))

        

        st.subheader("Aggregated Data")
        final_data =  pd.merge(
            final_data, final_data_old, how="outer", on="library"
        )
        final_data["counts_diff"] = final_data["counts"] - final_data["counts_old"]
        final_data["downloads_diff"] = final_data["downloads_30d"] - final_data["downloads_30d_old"]
        final_data["likes_diff"] = final_data["likes"] - final_data["likes_old"] 

        st.dataframe(final_data)
        
        st.subheader("Raw Data")
        columns_of_interest = ["repo_id", "author", "files_per_repo", "library", "downloads_30d", "likes"]
        filtered_data = filtered_data[columns_of_interest]
        st.dataframe(filtered_data)

    #with tab6:
    if tab == "Model Cards":
        st.header("Model cards")

        columns_of_interest = ["has_model_index", "has_metadata", "has_text", "text_length"]
        rows = data.shape[0]
        rows_old = old_data.shape[0]

        cond = data["has_model_index"] | data["has_text"]
        with_model_card = data[cond]
        c_model_card = with_model_card.shape[0]

        cond = old_data["has_model_index"] | old_data["has_text"]
        with_model_card_old = old_data[cond]
        c_model_card_old = with_model_card_old.shape[0]

        st.subheader("High-level metrics")
        col1, col2, col3 = st.columns(3)
        with col1:
            st.metric(label="# models with model card file", value=c_model_card, delta=int(c_model_card-c_model_card_old))
        with col2:
            st.metric(label="# models without model card file", value=rows-c_model_card, delta=int((rows-c_model_card)-(rows_old-c_model_card_old)))
        
        with_index = data["has_model_index"].sum()
        with_index_old = old_data["has_model_index"].sum()
        with col1:
            st.metric(label="# models with model index", value=with_index, delta=int(with_index-with_index_old))
        with col2:
            st.metric(label="# models without model index", value=rows-with_index, delta=int((rows-with_index)-(rows_old-with_index_old)))

        with_text = data["has_text"]
        with_text_old = old_data["has_text"]
        with col1:
            st.metric(label="# models with model card text", value=with_text.sum(), delta=int(with_text.sum()-with_text_old.sum()))
        with col2:
            st.metric(label="# models without model card text", value=rows-with_text.sum(), delta=int((rows-with_text.sum())-(rows_old-with_text_old.sum())))

        
        st.subheader("Length (chars) of model card content")
        fig, ax = plt.subplots() 
        ax = data["length_bins"].value_counts().plot.bar()
        st.metric(label="# average length of model card (chars)", value=data[with_text]["text_length"].mean())
        st.pyplot(fig)

        st.subheader("Tags (Read more in Pipeline tab)")
        tags = data["tags"].explode()
        tags = tags[tags.notna()].value_counts().rename_axis("tag").to_frame('counts').reset_index()
        st.write(alt.Chart(tags.head(30)).mark_bar().encode(
            x='counts',
            y=alt.X('tag', sort=None)
        ))

    #with tab7:
    if tab == "Super Users":
        st.header("Authors")
        st.text("This info corresponds to the repos owned by the authors")
        authors = data.groupby("author").sum().drop(["text_length", "Unnamed: 0"], axis=1).sort_values("downloads_30d", ascending=False)
        d = data["author"].value_counts().rename_axis("author").to_frame('counts').reset_index()
        final_data = pd.merge(
            d, authors, how="outer", on="author"
        )
        st.dataframe(final_data)

    #with tab2:
    if tab == "Raw Data":
        st.header("Raw Data")
        d = data.astype(str)
        st.dataframe(d)


if __name__ == '__main__':
    main()