up

app.py

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+import datetime
+from huggingface_hub import Repository
+import os
+import pandas as pd
+import streamlit as st
+import altair as alt
+import numpy as np
+import plotly.graph_objects as go
+
+today = datetime.date.today()
+year, week, _ = today.isocalendar()
+
+DATASET_REPO_URL = (
+    "https://huggingface.co/datasets/huggingface/transformers-stats-space-data"
+)
+
+DATA_FILENAME = f"data_{week}_{year}.csv"
+DATA_FILE = os.path.join("data", DATA_FILENAME)
+
+MODELS_TO_TRACK = ["wav2vec2", "whisper"]
+
+repo = Repository(local_dir="data", clone_from=DATASET_REPO_URL, use_auth_token=True)
+
+valid_weeks = []
+download_results = []
+model_download_results = {model_name: [] for model_name in MODELS_TO_TRACK}
+
+# loop over past data, finding where we have data saved (valid weeks) and tracking monthly downloads for each week
+for i in range(1, week + 1)[::-1]:
+    data_filename = f"data_{i}_{year}.csv"
+    data_file = os.path.join("data", data_filename)
+
+    if os.path.exists(data_file):
+        valid_weeks.append(i)
+
+        dataframe = pd.read_csv(data_file)
+        df_audio = dataframe[dataframe["modality"] == "audio"]
+
+        audio_int_downloads = {model: int(x.replace(",", "")) for model, x in
+                               zip(df_audio["model_names"], df_audio["num_downloads"].values)}
+
+        download_results.append(sum(audio_int_downloads.values()))
+        for model_name in MODELS_TO_TRACK:
+            model_download_results[model_name].append(audio_int_downloads.get(model_name))
+
+last_year = year - 1
+last_week = 52
+data_filename = f"data_{last_week}_{last_year}.csv"
+data_file = os.path.join("data", data_filename)
+
+if os.path.exists(data_file):
+    valid_weeks.append(0)
+
+    dataframe = pd.read_csv(data_file)
+    df_audio = dataframe[dataframe["modality"] == "audio"]
+
+    audio_int_downloads = {model: int(x.replace(",", "")) for model, x in
+                           zip(df_audio["model_names"], df_audio["num_downloads"].values)}
+
+    download_results.append(sum(audio_int_downloads.values()))
+    for model_name in MODELS_TO_TRACK:
+        model_download_results[model_name].append(audio_int_downloads.get(model_name))
+
+fig = go.Figure()
+fig.update_layout(
+    title="Monthly downloads",
+    xaxis_title="Week",
+    yaxis_title="Downloads",)
+
+fig.add_trace(
+            go.Scatter(x=valid_weeks, y=download_results, mode='lines+markers', name="Total")
+        )
+
+for model_name in MODELS_TO_TRACK:
+    fig.add_trace(
+                go.Scatter(x=valid_weeks, y=model_download_results[model_name], mode='lines+markers', name=model_name)
+            )
+
+st.title("Audio Stats")
+st.plotly_chart(fig)
+
+
+week = st.selectbox(
+    "Week",
+    valid_weeks,
+    index=0,
+    help="Filter the download results by week"
+)
+
+DATA_FILENAME = f"data_{week}_{year}.csv"
+DATA_FILE = os.path.join("data", DATA_FILENAME)
+
+with open(DATA_FILE, "r") as f:
+    dataframe = pd.read_csv(DATA_FILE)
+
+st.header(f"Stats for year {year} and week {week}")
+
+# print audio
+df_audio = dataframe[dataframe["modality"] == "audio"]
+audio_int_downloads = np.array(
+    [int(x.replace(",", "")) for x in df_audio["num_downloads"].values]
+)
+source = pd.DataFrame(
+    {
+        "Number of total downloads": audio_int_downloads,
+        "Model architecture name": df_audio["model_names"].values,
+    }
+)
+bar_chart = (
+    alt.Chart(source)
+    .mark_bar()
+    .encode(
+        y="Number of total downloads",
+        x=alt.X("Model architecture name", sort=None),
+    )
+)
+st.subheader(f"Top audio downloads last 30 days")
+st.altair_chart(bar_chart, use_container_width=True)
+
+st.subheader("Audio stats last 30 days")
+
+dataframe = dataframe[dataframe["modality"] == "audio"].drop("modality", axis=1)
+dataframe.loc["Total"] = dataframe.sum(numeric_only=True)
+total_audio_downloads = sum(audio_int_downloads)
+
+# nice formatting
+dataframe.at["Total", "num_downloads"] = "{:,}".format(total_audio_downloads)
+dataframe.at["Total", "model_names"] = ""
+dataframe.at["Total", "download_per_model"] = ""
+
+st.table(dataframe)

requirements.txt

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+huggingface_hub
+plotly