add app.py (#1)

- add app (6f60dfe566112bfc047db2bc4764c0fd0b83d166)

app.py

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+import gradio as gr
+
+
+import pandas as pd
+import json
+from datasets import load_dataset
+import requests
+from huggingface_hub import list_datasets, list_models, list_spaces
+from collections import Counter
+import numpy as np
+
+
+def compute_ranking(df, column, method="sum", keep="last"):
+    df_rank = df.groupby("author").aggregate({column: method})[[column]]
+    df_rank = df_rank.sort_values(by=column)
+    df_rank.reset_index(drop=True, inplace=True)
+    df_rank["top_perc"] = df_rank.apply(lambda x: f"{100 * (1-(x.name/len(df_rank))):.2f}", axis=1)
+    df_rank = df_rank.drop_duplicates(subset=column, keep=keep)
+    df_rank = df_rank.rename({column: "value"}, axis='columns')
+    return df_rank
+
+class NpEncoder(json.JSONEncoder):
+    def default(self, obj):
+        if isinstance(obj, np.integer):
+            return int(obj)
+        if isinstance(obj, np.floating):
+            return float(obj)
+        if isinstance(obj, np.ndarray):
+            return obj.tolist()
+        return super(NpEncoder, self).default(obj)
+
+ds = load_dataset("open-source-metrics/model-repos-stats", split="train")
+df = ds.to_pandas()
+
+df_ranks = {}
+df_ranks["likes"] = compute_ranking(df, "likes")
+df_ranks["downloads"] = compute_ranking(df, "downloads_30d")
+df_ranks["repos"] = compute_ranking(df, "repo_id", method="count")
+
+with open("./html_template.html", "r") as f:
+    template = f.read()
+
+def create_user_summary(user_name):
+    summary = {}
+    
+    df_user = df.loc[df["author"]==user_name]
+    
+    r = requests.get(f'https://huggingface.co/api/users/{user_name}/likes')
+    user_datasets = list_datasets(author="lvwerra")
+    user_spaces = list_spaces(author="lvwerra")
+    
+    summary["likes_user_total"] = df_user["likes"].sum()
+    summary["likes_user_given"] = len(r.json())
+    summary["likes_user_top"] = df_ranks["likes"][df_ranks["likes"]["value"]>=summary["likes_user_total"]].iloc[0]["top_perc"]
+    summary["likes_repo_most"] = df_user.sort_values(by="likes", ascending=False).iloc[0]["repo_id"]
+    summary["likes_repo_most_n"] = df_user.sort_values(by="likes", ascending=False).iloc[0]["likes"]
+
+    summary["downloads_user_total"] = df_user["downloads_30d"].sum()
+    summary["downloads_user_top"] = df_ranks["downloads"][df_ranks["downloads"]["value"]>=summary["downloads_user_total"]].iloc[0]["top_perc"]
+    summary["downlods_repo_most"] = df_user.sort_values(by="downloads_30d", ascending=False).iloc[0]["repo_id"]
+    summary["downlods_repo_most_n"] = df_user.sort_values(by="downloads_30d", ascending=False).iloc[0]["downloads_30d"]
+
+    summary["repos_model_total"] = len(df_user)
+    summary["repos_model_top"] = df_ranks["repos"][df_ranks["repos"]["value"]>=summary["repos_model_total"]].iloc[0]["top_perc"]
+    summary["repos_model_fav_type"] = Counter(df_user["model_type"].dropna()).most_common(1)[0][0]
+    
+    summary["repos_datasets_total"] = len(list(iter(user_datasets)))
+    summary["repos_spaces_total"] = len(list(iter(user_spaces)))
+    summary["repos_spaces_fav_sdk"] = Counter([info.sdk for info in user_spaces]).most_common(1)[0][0]
+
+    return dict_to_html(summary)
+
+
+def dict_to_html(summary):
+
+    report = template
+
+    for key in summary:
+        report = report.replace("{{" + key +  "}}", str(summary[key]))
+    return report
+
+
+demo = gr.Blocks(
+    css=".gradio-container {background-color: #000000}"
+)
+with demo:
+    with gr.Row():
+        gr.HTML(value="""<p style="text-align: center; color: rgb(255, 210, 31); font-family: 'Consolas', monospace; font-size: 24px;"> <b>Enter your HF user name:</b></p>""")
+    with gr.Row():
+        username = gr.Textbox(lines=1, max_lines=1, label="User name")
+    with gr.Row():
+        run = gr.Button()
+    with gr.Row():
+        output = gr.HTML(label="Generated code")
+    event = run.click(create_user_summary, [username], output)
+demo.launch()

html_template.html

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+<br>
+<br>
+<p style="text-align: center; color: rgb(255, 210, 31); font-family: 'Consolas', monospace; font-size: 48px;"> <b>Your Personal <img src="https://huggingface.co/front/assets/huggingface_logo.svg" alt="image" style="vertical-align: middle; display: inline-block; height: 72px;"> Report</b></p>
+<br>
+<br>
+<br>
+<p style="color: rgb(255, 210, 31);font-family: 'Consolas', monospace; font-size: 36px;">♥︎ Likes received: {{likes_user_total}}   (top {{likes_user_top}}%)</p>
+<br>
+<p style="color: rgb(255, 210, 31);font-family: 'Consolas', monospace; font-size: 16px;">Your model that got most likes ({{likes_repo_most_n}}): {{likes_repo_most}}</p>
+<br>
+<br>
+<p style="color: rgb(255, 210, 31);font-family: 'Consolas', monospace; font-size: 36px;">⤓ Total downloads: {{downloads_user_total}}   (top {{downloads_user_top}} %)</p>
+<br>
+<p style="color: rgb(255, 210, 31);font-family: 'Consolas', monospace; font-size: 16px;">Your model that was downloaded most ({{downlods_repo_most_n}}): {{downlods_repo_most}}</p>
+<br>
+<br>
+<p style="color: rgb(255, 210, 31);font-family: 'Consolas', monospace; font-size: 36px;">⚅ Number of models: {{repos_model_total}}   (top {{repos_model_top}} %)</p>
+<br>
+<p style="color: rgb(255, 210, 31);font-family: 'Consolas', monospace; font-size: 16px;">Your favourite model type is: {{repos_model_fav_type}}</p>
+<br>
+<br>
+<p style="color: rgb(255, 210, 31);font-family: 'Consolas', monospace; font-size: 36px;">⌂ You also have {{repos_datasets_total}} datasets and {{repos_spaces_total}} spaces!</p>
+<br>
+<p style="color: rgb(255, 210, 31);font-family: 'Consolas', monospace; font-size: 16px;">Your favourite spaces SDK is {{repos_spaces_fav_sdk}}.</p>