init app.py

app.py

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+from huggingface_hub import HfApi, HfFileSystem
+import re
+from tqdm import tqdm
+import concurrent.futures
+import gradio as gr
+import datetime
+import pandas as pd
+import os
+import threading
+import time
+
+HF_TOKEN = os.getenv('HF_TOKEN')
+
+api = HfApi()
+fs = HfFileSystem()
+
+def restart_space():
+    time.sleep(36000)
+    api.restart_space(repo_id="Tanvir1337/lonestriker-quantized-models", token=HF_TOKEN)
+
+text = f"""
+🎯 The Leaderboard aims to track lonestriker's exl2 quantized models.
+
+## 🛠️ Backend
+
+The leaderboard's backend mainly runs on the [Hugging Face Hub API](https://huggingface.co/docs/huggingface_hub/v0.5.1/en/package_reference/hf_api).
+
+## 🔍 Searching
+
+You can search for author or a specific model using the search bar.
+
+## ⌛ Last Update
+
+This space is last updated in **{str(datetime.datetime.now().strftime("%Y-%m-%d %H:%M"))}**.
+
+## 📒 Important Note
+
+This space potentially includes incorrectly quantized models for a model.
+
+If you find any incorrectly quantized model, please report it to me.
+"""
+
+quant_models = [i.__dict__['id'] for i in api.list_models(author="lonestriker") if "exl2" in i.__dict__['id']]
+
+pattern = r'\(https://huggingface\.co/([^/]+)/([^/]+)\)'
+liste = {}
+
+def process_model(i, pattern, liste):
+    text = fs.read_text(i + "/README.md")
+    matches = re.search(pattern, text)
+
+    if matches:
+        author = matches.group(1)
+        model_name = matches.group(2)
+        full_id = (author + "/" + model_name).split(")")[0]
+
+        try:
+            liste[full_id].append(i)
+        except KeyError:
+            liste[full_id] = [i]
+
+
+num_threads = 64
+
+with concurrent.futures.ThreadPoolExecutor(max_workers=num_threads) as executor:
+    futures = []
+    for i in quant_models:
+        future = executor.submit(process_model, i, pattern, liste)
+        futures.append(future)
+
+    concurrent.futures.wait(futures)
+
+
+authors, models, exl2 = [], [], []
+
+
+for model, values in liste.items():
+    models.append(model)
+
+    exl2_value = None
+
+    for value in values:
+        if "-exl2" in value:
+            exl2_value = value
+
+    authors.append(model.split('/')[0])
+    exl2.append(exl2_value)
+
+
+df = pd.DataFrame({'👤 Author Name': authors, '🤖 Model Name': models, '📥 EXL2': exl2})
+
+
+def search(search_text):
+    if not search_text:
+        return df
+
+    if len(search_text.split('/'))>1:
+        return df[df['🤖 Model Name'] == clickable(search_text)]
+    else:
+        return df[df['👤 Author Name'] == clickable(search_text)]
+
+
+def clickable(x):
+    return None if not x else f'<a target="_blank" href="https://huggingface.co/{x}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{x}</a>'
+
+
+def to_clickable(df):
+    for column in list(df.columns):
+        df[column] = df[column].apply(lambda x: clickable(x))
+    return df
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("""<center><img src = "https://huggingface.co/avatars/f18351bc5ce9c106ba74523d9a55567c.svg" width=200 height=200></center>""")
+    gr.Markdown("""<h1 align="center" id="space-title">lonestriker Quantized Models</h1>""")
+    gr.Markdown(text)
+
+    with gr.Column(min_width=320):
+        search_bar = gr.Textbox(placeholder="🔍 Search for a author or a specific model", show_label=False)
+
+    
+    df_clickable = to_clickable(df)
+    gr_df = gr.Dataframe(df_clickable, interactive=False, datatype=["markdown"]*len(df.columns))
+
+    search_bar.submit(fn=search, inputs=search_bar, outputs=gr_df)
+
+threading.Thread(target=restart_space).start()
+demo.launch()