Upload app.py

app.py

@@ -0,0 +1,209 @@
+import re
+import json
+import requests
+import pandas as pd
+from tqdm import tqdm
+from bs4 import BeautifulSoup
+from huggingface_hub import HfApi, list_models, list_datasets, list_spaces
+import gradio as gr
+
+api = HfApi()
+
+def get_models(org_name, which_one):
+  all_list = []
+  if which_one == "models":
+    things = api.list_models(author=org_name)
+  elif which_one == "datasets":
+    things = api.list_datasets(author=org_name)
+  elif which_one == "spaces":
+     things = api.list_spaces(author=org_name)
+
+  for i in things:
+    i = i.__dict__
+    json_format_data = {"id": i['id'], "downloads": i['downloads'], "likes": i['likes']} if which_one != "spaces" else {"id": i['id'], "downloads": 0, "likes": i['likes']}
+
+    all_list.append(json_format_data)
+
+
+  df_all_list = (pd.DataFrame(all_list))
+
+  return df_all_list
+
+def get_most(df_for_most_function):
+  download_sorted_df = df_for_most_function.sort_values(by=['downloads'], ascending=False)
+  most_downloaded = download_sorted_df.iloc[0]
+
+  like_sorted_df = df_for_most_function.sort_values(by=['likes'], ascending=False)
+  most_liked = like_sorted_df.iloc[0]
+
+  return {"Most Download": {"id": most_downloaded['id'], "downloads": most_downloaded['downloads'], "likes": most_downloaded['likes']}, "Most Likes": {"id": most_liked['id'], "downloads": most_liked['downloads'], "likes": most_liked['likes']}}
+
+def get_sum(df_for_sum_function):
+  sum_downloads = sum(df_for_sum_function['downloads'].tolist())
+  sum_likes = sum(df_for_sum_function['likes'].tolist())
+
+  return {"Downloads": sum_downloads, "Likes": sum_likes}
+
+def get_openllm_leaderboard():
+    url = 'https://huggingfaceh4-open-llm-leaderboard.hf.space/'
+    response = requests.get(url)
+    soup = BeautifulSoup(response.content, 'html.parser')
+    script_elements = soup.find_all('script')
+    data = json.loads(str(script_elements[1])[31:-10])
+
+    component_index = 11
+    pattern = r'href="([^"]*)"'
+    zero_or_one = 1
+
+    result_list = []
+    i = 0
+    while True:
+        try:
+            unfiltered = data['components'][component_index]['props']['value']['data'][i][zero_or_one].rstrip("\n")
+            normal_name = re.search(pattern, unfiltered).group(1)
+            normal_name = "/".join(normal_name.split("/")[-2:])
+            result_list.append(normal_name)
+            i += 1
+        except (IndexError, AttributeError):
+            return result_list
+
+def get_ranking(model_list, target_org):
+    for index, model in enumerate(model_list):
+      if model.split("/")[0].lower() == target_org.lower():
+          return [index+1, model]
+    return "Not Found"
+
+def make_leaderboard(orgs, which_one):
+    data_rows = []
+    open_llm_leaderboard = get_openllm_leaderboard() if which_one == "models" else None
+
+    for org in tqdm(orgs, desc=f"Scraping Organizations ({which_one})", position=0, leave=True):
+        df = get_models(org, which_one)
+        if len(df) == 0:
+          continue
+        num_things = len(df)
+        sum_info = get_sum(df)
+        most_info = get_most(df)
+
+        if which_one == "models":
+          open_llm_leaderboard_get_org = get_ranking(open_llm_leaderboard, org)
+          data_rows.append({
+              "Organization Name": org,
+              "Total Downloads": sum_info["Downloads"],
+              "Total Likes": sum_info["Likes"],
+              "Number of Models": num_things,
+              "Best Model On Open LLM Leaderboard": open_llm_leaderboard_get_org[1] if open_llm_leaderboard_get_org != "Not Found" else open_llm_leaderboard_get_org,
+              "Best Rank On Open LLM Leaderboard": open_llm_leaderboard_get_org[0] if open_llm_leaderboard_get_org != "Not Found" else open_llm_leaderboard_get_org,
+              "Average Downloads per Model": int(sum_info["Downloads"] / num_things) if num_things != 0 else 0,
+              "Average Likes per Model": int(sum_info["Likes"] / num_things) if num_things != 0 else 0,
+              "Most Downloaded Model": most_info["Most Download"]["id"],
+              "Most Download Count": most_info["Most Download"]["downloads"],
+              "Most Liked Model": most_info["Most Likes"]["id"],
+              "Most Like Count": most_info["Most Likes"]["likes"]
+          })
+        elif which_one == "datasets":
+          data_rows.append({
+              "Organization Name": org,
+              "Total Downloads": sum_info["Downloads"],
+              "Total Likes": sum_info["Likes"],
+              "Number of Datasets": num_things,
+              "Average Downloads per Dataset": int(sum_info["Downloads"] / num_things) if num_things != 0 else 0,
+              "Average Likes per Dataset": int(sum_info["Likes"] / num_things) if num_things != 0 else 0,
+              "Most Downloaded Dataset": most_info["Most Download"]["id"],
+              "Most Download Count": most_info["Most Download"]["downloads"],
+              "Most Liked Dataset": most_info["Most Likes"]["id"],
+              "Most Like Count": most_info["Most Likes"]["likes"]
+          })
+
+        elif which_one == "spaces":
+          data_rows.append({
+              "Organization Name": org,
+              "Total Likes": sum_info["Likes"],
+              "Number of Spaces": num_things,
+              "Average Likes per Space": int(sum_info["Likes"] / num_things) if num_things != 0 else 0,
+              "Most Liked Space": most_info["Most Likes"]["id"],
+              "Most Like Count": most_info["Most Likes"]["likes"]
+          })
+
+    leaderboard = pd.DataFrame(data_rows)
+    leaderboard.insert(0, "Serial Number", range(1, len(leaderboard) + 1))
+    return leaderboard
+
+"""# Gradio başlasın
+
+"""
+
+with open("org_names.txt", "r") as f:
+  org_names_in_list = [i.rstrip("\n") for i in f.readlines()]
+
+
+INTRODUCTION_TEXT = f"""
+🎯 The Organization Leaderboard aims to track organizations ranking. This space is inspired by [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+
+## Dataframes Available:
+
+- 🏛️ Models
+
+- 📊 Datasets
+
+- 🚀 Spaces
+
+## Backend
+
+🛠️ The leaderboard's backend mainly runs the [Hugging Face Hub API](https://huggingface.co/docs/huggingface_hub/v0.5.1/en/package_reference/hf_api).
+
+🛠️ Organization names are being retrieved using web scrabing ([HUggingface Organizations](https://huggingface.co/organizations))
+
+**🌐 Note:** In model's dataframe there is some columns related to [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). These datas are also being retrieved with web scrabing.
+
+"""
+
+def clickable(x, which_one):
+    if which_one == "models":
+      if x != "Not Found":
+          return f'<a target="_blank" href="https://huggingface.co/{x}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{x}</a>'
+      else:
+          return "Not Found"
+    else:
+        return f'<a target="_blank" href="https://huggingface.co/{which_one}/{x}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{x}</a>'
+
+def models_df_to_clickable(df, columns, which_one):
+    for column in columns:
+        if column == "Organization Name":
+          df[column] = df[column].apply(lambda x: clickable(x, "models"))
+        df[column] = df[column].apply(lambda x: clickable(x, which_one))
+    return df
+
+demo = gr.Blocks()
+
+with gr.Blocks() as demo:
+      gr.Markdown("""<h1 align="center" id="space-title">🤗 Organization Leaderboard</h1>""")
+      gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
+
+      with gr.TabItem("🏛️ Models", id=1):
+
+          columns_to_convert = ["Organization Name", "Best Model On Open LLM Leaderboard", "Most Downloaded Model", "Most Liked Model"]
+          models_df = make_leaderboard(org_names_in_list, "models")
+          models_df = models_df_to_clickable(models_df, columns_to_convert, "models")
+
+          headers = ["🔢 Serial Number", "🏢 Organization Name", "📥 Total Downloads", "👍 Total Likes", "🤖 Number of Models", "🏆 Best Model On Open LLM Leaderboard", "🥇 Best Rank On Open LLM Leaderboard", "📊 Average Downloads per Model", "📈 Average Likes per Model", "🚀 Most Downloaded Model", "📈 Most Download Count", "❤️ Most Liked Model", "👍 Most Like Count"]
+          gr.Dataframe(models_df, headers=headers, interactive=True, datatype=["str", "markdown", "str", "str", "str", "markdown", "str", "str", "str", "markdown", "str", "markdown", "str"])
+
+      with gr.TabItem("📊 Dataset", id=2):
+          columns_to_convert = ["Organization Name", "Most Downloaded Dataset", "Most Liked Dataset"]
+          dataset_df = make_leaderboard(org_names_in_list, "datasets")
+          dataset_df = models_df_to_clickable(dataset_df, columns_to_convert, "datasets")
+
+          headers = ["🔢 Serial Number", "🏢 Organization Name", "📥 Total Downloads", "👍 Total Likes", "📊 Number of Datasets", "📊 Average Downloads per Dataset", "📈 Average Likes per Dataset", "🚀 Most Downloaded Dataset", "📈 Most Download Count", "❤️ Most Liked Dataset", "👍 Most Like Count"]
+          gr.Dataframe(dataset_df, headers=headers, interactive=False, datatype=["str", "markdown", "str", "str", "str", "str", "str", "markdown", "str", "markdown", "str"])
+
+      with gr.TabItem("🚀 Spaces", id=3):
+          columns_to_convert = ["Organization Name", "Most Liked Space"]
+
+          spaces_df = make_leaderboard(org_names_in_list, "spaces")
+          spaces_df = models_df_to_clickable(spaces_df, columns_to_convert, "spaces")
+
+          headers = ["🔢 Serial Number", "🏢 Organization Name", "👍 Total Likes", "🚀 Number of Spaces", "📈 Average Likes per Space", "❤️ Most Liked Space", "👍 Most Like Count"]
+          gr.Dataframe(spaces_df, headers=headers, interactive=False,  datatype=["str", "markdown", "str", "str", "str", "markdown", "str"])
+
+demo.launch()