Create 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
+
+api = HfApi()
+fs = HfFileSystem()
+
+
+text = f"""
+🎯 The Leaderboard aims to track TheBloke's quantisied models.
+
+## 📚 Type Of Models
+
+- GPTQ
+
+- GGUF
+
+- AWQ
+
+- GGML
+
+## 🛠️ 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 spesific 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 quantisied models for a model.
+
+If you find any incorrectly quantisied model, please report it to me.
+"""
+
+quant_models = [i.__dict__['id'] for i in api.list_models(author="TheBloke") if "GPTQ" in i.__dict__['id'] or "GGUF" in i.__dict__['id'] or "AWQ" in i.__dict__['id'] or "GGML" 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, gptq, gguf, awq, ggml = [], [], [], [], [], []
+
+
+for model, values in liste.items():
+    models.append(model)
+
+    gptq_value, gguf_value, awq_value, ggml_value = None, None, None, None
+
+    for value in values:
+        if "-GPTQ" in value:
+            gptq_value = value
+        elif "-GGUF" in value:
+            gguf_value = value
+        elif "-AWQ" in value:
+            awq_value = value
+        elif "-GGML" in value:
+            ggml_value = value
+
+    authors.append(model.split('/')[0])
+    gptq.append(gptq_value)
+    gguf.append(gguf_value)
+    awq.append(awq_value)
+    ggml.append(ggml_value)
+
+
+df = pd.DataFrame({'👤 Author Name': authors, '🤖 Model Name': models, '👍 GPTQ': gptq, '📥 GGUF': gguf, '🤷‍♂️ AWQ': awq, '😭 GGML': ggml})
+
+
+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://cdn-uploads.huggingface.co/production/uploads/6426d3f3a7723d62b53c259b/tvPikpAzKTKGN5wrpadOJ.jpeg" width=200 height=200></center>""")
+    gr.Markdown("""<h1 align="center" id="space-title">The Bloke Quantisied Models</h1>""")
+    gr.Markdown(text)
+
+    with gr.Column(min_width=320):
+        search_bar = gr.Textbox(placeholder="🔍 Search for a author or a spesific 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)
+
+
+demo.launch()