models.py

import streamlit as st
import pandas as pd
from ast import literal_eval
import altair as alt
import matplotlib.pyplot as plt

from utils import process_dataset, eval_tags, change_and_delta
from language import process_for_lang, filter_multilinguality
from pipelines import filter_pipeline_data

def main():
    # Pick revision at top
    supported_revisions = ["29_05_23", "22_05_23", "15_05_23", "08_05_23", "01_05_23", "24_04_23", "17_04_23", "10_04_23", "03_04_23", "27_03_23", "20_03_23", "13_03_23", "06_03_23", "27_02_23", "20_02_23", "13_02_23","06_02_23", "30_01_23", "24_01_23", "16_01_23", "10_01_23", "02_01_23", "19_12_22", "12_12_22", "05_12_22", "28_11_22", "22_11_22", "14_11_22", "07_11_22", "31_10_22", "24_10_22", "17_10_22", "10_10_22", "27_09_22"]
    col1, col2, col3 = st.columns(3)
    with col1:
        new = st.selectbox(
            'Last revision',
            supported_revisions,
            index=0)
    with col2:
        base = st.selectbox(
            'Old revision',
            supported_revisions,
            index=1)
    with col3:
        base_old = st.selectbox(
            'Very old revision',
            supported_revisions,
            index=2)

    # Process dataset
    old_old_data = process_dataset(base_old)
    old_data = process_dataset(base)
    data = process_dataset(new)
    old_old_data["tags"] = old_old_data.apply(eval_tags, axis=1)
    old_data["tags"] = old_data.apply(eval_tags, axis=1)
    data["tags"] = data.apply(eval_tags, axis=1)

    # High level count of models and rate of change
    total_samples_old_old = old_old_data.shape[0]
    total_samples_old = old_data.shape[0]
    total_samples = data.shape[0]

    curr_change, delta = change_and_delta(total_samples_old_old, total_samples_old, total_samples)

    col1, col2 = st.columns(2)
    with col1:
        st.metric(label="Total public models", value=total_samples, delta=total_samples-total_samples_old)
        
    with col2:
        st.metric(label="Rate of change", value=curr_change, delta=delta)

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

    with tab1:
        st.header("Languages info")

        filtered_data = data.copy()
        old_filtered_data = old_data.copy()
        old_old_filtered_data = old_old_data.copy()

        modality = st.selectbox(
            'Modalities',
            ["All", "NLP", "Audio", "Multimodal"])

        filtered_data, no_lang_count, total_langs, langs = process_for_lang(filtered_data, modality)
        old_filtered_data, no_lang_count_old, total_langs_old, langs_old = process_for_lang(old_filtered_data, modality)
        old_old_filtered_data, no_lang_count_old_old, total_langs_old_old, _ = process_for_lang(old_old_filtered_data, modality)

        v = filtered_data.shape[0]-no_lang_count
        v_old = old_filtered_data.shape[0]-no_lang_count_old
        v_old_old = old_old_filtered_data.shape[0]-no_lang_count_old_old

        col1, col2 = st.columns(2)
        with col1:
            st.metric(label="Language Specified", value=v, delta=int(v-v_old))
        with col2:
            curr_change, delta = change_and_delta(v_old_old, v_old, v)
            st.metric(label="Language Specified Rate of Change", value=curr_change, delta=delta)

        col1, col2 = st.columns(2)
        with col1:
            st.metric(label="No Language Specified", value=no_lang_count, delta=int(no_lang_count-no_lang_count_old))
        with col2:
            curr_change, delta = change_and_delta(no_lang_count_old_old, no_lang_count_old, no_lang_count)
            st.metric(label="No Language Specified Rate of Change", value=curr_change, delta=delta)

        col1, col2 = st.columns(2)
        with col1:
            st.metric(label="Total Unique Languages", value=total_langs, delta=int(total_langs-total_langs_old))
        with col2:
            curr_change, delta = change_and_delta(total_langs_old_old, total_langs_old, total_langs)
            st.metric(label="Total Unique Languages Rate of Change", value=curr_change, delta=delta)
        st.text(f"New languages {set(langs)-set(langs_old)}")
        st.text(f"Lost languages {set(langs_old)-set(langs)}")

        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"])

        models_with_langs = filter_multilinguality(filtered_data, linguality)
        models_with_langs_old = filter_multilinguality(old_filtered_data, linguality)

        df1 = models_with_langs['language_count'].value_counts()
        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"])

        models_with_langs = filtered_data[filtered_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_filtered_data[old_filtered_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 linguality_2 == "No English":
            d = orig_d.iloc[1:]
        elif linguality_2 == "Remove top 10":
            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['counts'] = final_data['counts'].fillna(0).astype(int)
        final_data['old_c'] = final_data['old_c'].fillna(0).astype(int)
        final_data["diff"] = final_data["counts"] - final_data["old_c"]
        final_data['language'] = final_data['language'].astype(str)
        st.dataframe(final_data)
        
    with tab2:
        st.header("License info")

        no_license_count = data["license"].isna().sum()
        no_license_count_old = old_data["license"].isna().sum()
        no_license_count_old_old = old_old_data["license"].isna().sum()

        
        col1, col2 = st.columns(2)
        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:
            v = total_samples-no_license_count
            v_old = total_samples_old-no_license_count_old
            v_old_old = total_samples_old-no_license_count_old_old
            curr_change, delta = change_and_delta(v_old_old, v_old, v)
            st.metric(label="License Specified Rate of Change", value=curr_change, delta=delta)

        col1, col2 = st.columns(2)
        with col1:
            st.metric(label="No License Specified", value=no_license_count, delta=int(no_license_count-no_license_count_old))
        with col2:
            curr_change, delta = change_and_delta(no_license_count_old_old, no_license_count_old, no_license_count)
            st.metric(label="No License Specified Rate of Change", value=curr_change, delta=delta)

        col1, col2 = st.columns(2)
        unique_licenses = len(data["license"].unique())
        unique_licenses_old = len(old_data["license"].unique())
        unique_licenses_old_old = len(old_old_data["license"].unique())
        with col1:
            st.metric(label="Total Unique Licenses", value=unique_licenses, delta=int(unique_licenses-unique_licenses_old))
        with col2:
            curr_change, delta = change_and_delta(unique_licenses_old_old, unique_licenses_old, unique_licenses)
            st.metric(label="Total Unique Licenses Rate of Change", value=curr_change, delta=delta)
        st.text(f"New licenses {set(data['license'].unique())-set(old_data['license'].unique())}")
        st.text(f"Old licenses {set(old_data['license'].unique())-set(data['license'].unique())}")

        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:
        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_o = tags_old["tag"]
        s_o = s_o[s_o.apply(type) == str]
        unique_tags_old = len(s_o.unique())

        tags_old_old = old_old_data["tags"].explode()
        tags_old_old = tags_old_old[tags_old_old.notna()].value_counts().rename_axis("tag").to_frame('counts').reset_index()
        s_old_old = tags_old_old["tag"]
        s_old_old = s_old_old[s_old_old.apply(type) == str]
        unique_tags_old_old = len(s_old_old.unique())

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

        col1, col2 = st.columns(2)
        v = total_samples-no_pipeline_count
        v_old = total_samples_old-no_pipeline_count_old
        v_old_old = total_samples_old_old-no_pipeline_count_old_old
        with col1:
            st.metric(label="# models that have any pipeline", value=v, delta=int(v-v_old))
        with col2:
            curr_change, delta = change_and_delta(v_old_old, v_old, v)
            st.metric(label="# models rate of change", value=curr_change, delta=delta)

        col1, col2 = st.columns(2)
        with col1:
            st.metric(label="No pipeline Specified", value=no_pipeline_count, delta=int(no_pipeline_count-no_pipeline_count_old))
        with col2:
            curr_change, delta = change_and_delta(no_pipeline_count_old_old, no_pipeline_count_old, no_pipeline_count)
            st.metric(label="No pipeline Specified rate of change", value=curr_change, delta=delta)

        col1, col2 = st.columns(2)
        with col1:
            st.metric(label="Total Unique Tags", value=unique_tags, delta=int(unique_tags-unique_tags_old))
        with col2:
            curr_change, delta = change_and_delta(unique_tags_old_old, unique_tags_old, unique_tags)
            st.metric(label="Total Unique Tags", value=curr_change, delta=delta)

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

        st.subheader("High-level metrics")

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

        filtered_data, tags = filter_pipeline_data(data, modality_filter, p, l, f, filt, o)
        filtered_data_old, old_tags = filter_pipeline_data(old_data, modality_filter, p, l, f, filt, o)
        filtered_data_old_old, old_old_tags = filter_pipeline_data(old_old_data, modality_filter, p, l, f, filt, o)
        st.subheader("Pipeline breakdown")
        

        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"
        )

        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"
        )

        d_old = filtered_data_old_old["pipeline"].value_counts().rename_axis("pipeline").to_frame('counts').reset_index()
        grouped_data_old_old = filtered_data_old_old.groupby("pipeline").sum()[columns_of_interest]

        sums = grouped_data.sum()
        sums_old = grouped_data_old.sum()
        sums_old_old = grouped_data_old_old.sum()

        col1, col2, col3, col4 = st.columns(4)
        v = filtered_data.shape[0]
        v_old = filtered_data_old.shape[0]
        v_old_old = filtered_data_old_old.shape[0]
        with col1:
            st.metric(label="Total models", value=v, delta=int(v - v_old))
        with col2:
            curr_change, delta = change_and_delta(v_old_old, v_old, v)
            st.metric(label="Total models rate of change", value=curr_change, delta=delta)
        with col3:
            st.metric(label="Cumulative Downloads (30d)", value=sums["downloads_30d"], delta=int(sums["downloads_30d"] - sums_old["downloads_30d"]))
        with col4:
            print(sums_old_old["downloads_30d"], sums_old["downloads_30d"], sums["downloads_30d"])
            curr_change, delta = change_and_delta(sums_old_old["downloads_30d"], sums_old["downloads_30d"], sums["downloads_30d"])
            st.metric(label="Cumulative Downloads (30d) rate of change", value=curr_change, delta=delta)

        col1, col2, col3 = st.columns(3)
        with col1:
            st.metric(label="Total unique pipelines", value=len(filtered_data["pipeline"].unique()))
        with col2:
            st.metric(label="Cumulative likes", value=sums["likes"], delta=int(sums["likes"] - sums_old["likes"]))
        with col3:
            curr_change, delta = change_and_delta(sums_old_old["likes"], sums_old["likes"], sums["likes"])
            st.metric(label="Cumulative Likes rate of change", value=curr_change, delta=delta)


        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"]))
        
        col1, col2 = st.columns(2)
        with col1:
            st.metric(label="Total unique libraries", value=len(filtered_data["library"].unique()))
        with col2:
            st.metric(label="Total unique modality", value=len(filtered_data["modality"].unique()))

            
        col1, col2 = st.columns(2)
        with col1:
            st.metric(label="Total transformers models", value=len(filtered_data[filtered_data["library"] == "transformers"]))
        with col2:
            st.metric(label="Total non transformers models", value=len(filtered_data[filtered_data["library"] != "transformers"]))

        st.metric(label="Unique Tags", value=len(tags), delta=int(len(tags) - len(old_tags)))
        st.text(f"New tags {set(tags)-set(old_tags)}")
        st.text(f"Lost tags {set(old_tags)-set(tags)}")

        st.subheader("Pipeline breakdown by modality")
        col1, col2 = st.columns(2)
        with col1:
            st.metric(label="Total CV models", value=len(filtered_data[filtered_data["modality"] == "cv"]))
        with col2:
            st.metric(label="Total NLP models", value=len(filtered_data[filtered_data["modality"] == "nlp"]))

        col1, col2 = st.columns(2)
        with col1:
            st.metric(label="Total Audio models", value=len(filtered_data[filtered_data["modality"] == "audio"]))
        with col2:
            st.metric(label="Total RL models", value=len(filtered_data[filtered_data["modality"] == "rl"]))

        col1, col2 = st.columns(2)
        with col1:
            st.metric(label="Total Tabular models", value=len(filtered_data[filtered_data["modality"] == "tabular"]))
        with col2:
            st.metric(label="Total Multimodal models", value=len(filtered_data[filtered_data["modality"] == "multimodal"]))

        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:
        st.header("Social Features")

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

        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"]))

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

        likes = []
        for r in supported_revisions:
            likes.append(process_dataset(r)["likes"].sum()) 

        source = pd.DataFrame({
            'revision': supported_revisions[::-1],
            'likes': likes[::-1],
        })

        st.subheader("Total likes")
        st.write(alt.Chart(source).mark_bar().encode(
            x=alt.X('revision', sort=alt.EncodingSortField(field="revision", op="count", order='ascending')),
            y='likes'
        ))

        st.subheader("Likes Rate of Change")
        diffs = source["likes"].pct_change()
        source = pd.DataFrame({
            'revision': supported_revisions[::-1][1:],
            'likes_change': diffs[1:],
        })

        print(source[["revision", "likes_change"]])
        st.write(alt.Chart(source).mark_bar().encode(
            x=alt.X('revision', sort=alt.EncodingSortField(field="revision", op="count", order='ascending')),
            y='likes_change'
        ))
        


        st.subheader("Raw Data")
        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:
        st.header("Library info")

        no_library_count = data["library"].isna().sum()
        no_library_count_old = old_data["library"].isna().sum()
        no_library_count_old_old = 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))

        col1, col2, col3 = st.columns(3)
        with col1:
            v = total_samples-no_library_count
            v_old = total_samples_old-no_library_count_old
            v_old_old = total_samples_old_old-no_library_count_old_old
            curr_change, delta = change_and_delta(v_old_old, v_old, v)
            st.metric(label="# models that have any library change", value=curr_change, delta=delta)
        with col2:
            curr_change, delta = change_and_delta(no_library_count_old_old, no_library_count_old, no_library_count)
            st.metric(label="No library Specified Change", value=curr_change, delta=delta)
        with col3:
            v = len(data["library"].unique())
            v_old = len(old_data["library"].unique())
            v_old_old = len(old_old_data["library"].unique())
            curr_change, delta = change_and_delta(v_old_old, v_old, v)
            st.metric(label="Total Unique library", value=curr_change, delta=delta)

        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:
        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]
        rows_old_old = 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]

        cond = old_old_data["has_model_index"] | old_old_data["has_text"]
        with_model_card_old_old = old_old_data[cond]
        c_model_card_old_old = with_model_card_old_old.shape[0]

        st.subheader("High-level metrics")
        col1, col2, col3, col4 = st.columns(4)
        with col1:
            st.metric(label="# with model card file", value=c_model_card, delta=int(c_model_card-c_model_card_old))
        with col2:
            curr_change, delta = change_and_delta(c_model_card_old_old, c_model_card_old, c_model_card)
            st.metric(label="# with model card file change", value=curr_change, delta=delta)   
        with col3:
            st.metric(label="# without model card file", value=rows-c_model_card, delta=int((rows-c_model_card)-(rows_old-c_model_card_old)))
        with col4:
            curr_change, delta = change_and_delta(rows_old_old-c_model_card_old_old, rows_old-c_model_card_old, rows-c_model_card)
            st.metric(label="# without model card file change", value=curr_change, delta=delta)   

        with_index = data["has_model_index"].sum()
        with_index_old = old_data["has_model_index"].sum()
        with_index_old_old = old_old_data["has_model_index"].sum()
        with col1:
            st.metric(label="# with model index", value=with_index, delta=int(with_index-with_index_old))
        with col2:
            curr_change, delta = change_and_delta(with_index_old_old, with_index_old, with_index)
            st.metric(label="# with model index change", value=curr_change, delta=delta)   
        with col3:
            st.metric(label="# without model index", value=rows-with_index, delta=int((rows-with_index)-(rows_old-with_index_old)))
        with col4:
            curr_change, delta = change_and_delta(rows_old_old-with_index_old_old, rows_old-with_index_old, rows-with_index)
            st.metric(label="# without model index change",  value=curr_change, delta=delta)

        with_text = data["has_text"]
        with_text_old = old_data["has_text"]
        with_text_old_old = old_old_data["has_text"]

        with_text_sum = with_text.sum()
        with_text_old_sum = with_text_old.sum()
        with_text_old_old_sum = with_text_old_old.sum()
        with col1:
            st.metric(label="# with model card text", value=with_text_sum, delta=int(with_text_sum-with_text_old_sum))
        with col2:
            curr_change, delta = change_and_delta(with_text_old_old_sum, with_text_old_sum, with_text_sum)
            st.metric(label="# with model card text change", value=curr_change, delta=delta)   
        with col3:
            st.metric(label="# without card text", value=rows-with_text_sum, delta=int((rows-with_text_sum)-(with_text_old_sum)))
        with col4:
            curr_change, delta = change_and_delta(rows_old_old-with_text_old_old_sum, rows_old-with_text_old_sum, rows-with_text_sum)
            st.metric(label="# without card text change", value=curr_change, delta=delta)   

        st.subheader("Length (chars) of model card content")
        fig, _ = plt.subplots() 
        _ = 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:
        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 tab8:
        st.header("Raw Data")
        d = data.astype(str)
        st.dataframe(d)


if __name__ == '__main__':
    main()