Formatting

app.py

@@ -9,40 +9,47 @@ 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)
+    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']}
+    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)
+        all_list.append(json_format_data)
 
+    df_all_list = (pd.DataFrame(all_list))
 
-  df_all_list = (pd.DataFrame(all_list))
+    return df_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]
+    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]
+    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']}}
 
-  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())
+    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}
 
-  return {"Downloads": sum_downloads, "Likes": sum_likes}
 
 def get_openllm_leaderboard():
     url = 'https://huggingfaceh4-open-llm-leaderboard.hf.space/'
@@ -67,12 +74,14 @@ def get_openllm_leaderboard():
         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]
+        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
@@ -80,64 +89,60 @@ def make_leaderboard(orgs, which_one):
     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
+            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"]
-          })
+            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"]
-          })
+            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"]
-          })
+            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()]
+    org_names_in_list = [i.rstrip("\n") for i in f.readlines()]
 
-
-INTRODUCTION_TEXT = f"""
+markdown_main_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:
@@ -158,53 +163,67 @@ INTRODUCTION_TEXT = f"""
 
 """
 
+
 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"
+        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"))
-        else:
-          df[column] = df[column].apply(lambda x: clickable(x, which_one))
+            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")
+    gr.Markdown("""<h1 align="center" id="space-title">🤗 Organization Leaderboard</h1>""")
+    gr.Markdown(markdown_main_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"]
 
-      with gr.TabItem("🏛️ Models", id=1):
+        gr.Dataframe(models_df, headers=headers, interactive=True,
+                     datatype=["str", "markdown", "str", "str", "str", "markdown", "str", "str", "str", "markdown",
+                               "str", "markdown", "str"])
 
-          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")
+    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 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"])
+        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"]
 
-      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")
+        gr.Dataframe(dataset_df, headers=headers, interactive=False,
+                     datatype=["str", "markdown", "str", "str", "str", "str", "str", "markdown", "str", "markdown",
+                               "str"])
 
-          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"]
 
-      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")
 
-          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"]
 
-          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"])
+        gr.Dataframe(spaces_df, headers=headers, interactive=False,
+                     datatype=["str", "markdown", "str", "str", "str", "markdown", "str"])
 
-demo.launch()
+demo.launch()