v2

app.py

@@ -1,96 +1,443 @@
+import subprocess
 import gradio as gr
 import pandas as pd
+from apscheduler.schedulers.background import BackgroundScheduler
+from huggingface_hub import snapshot_download
 
-csv_filename = 'leaderboard.csv'
-# url = 'https://docs.google.com/spreadsheets/d/1Oh3nrbdWjKuh9twJsc9yJLppiJeD_BZyKgCTOxRkALM/export?format=csv'
+from src.about import (
+    CITATION_BUTTON_LABEL,
+    CITATION_BUTTON_TEXT,
+    EVALUATION_QUEUE_TEXT,
+    INTRODUCTION_TEXT,
+    LLM_BENCHMARKS_TEXT,
+    TITLE,
+)
+from src.display.css_html_js import custom_css
+from src.display.utils import (
+    BENCHMARK_COLS,
+    COLS,
+    EVAL_COLS,
+    EVAL_TYPES,
+    NUMERIC_INTERVALS,
+    TYPES,
+    AutoEvalColumn,
+    ModelType,
+    fields,
+    WeightType,
+    Precision
+)
+from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, REPO_ID, RESULTS_REPO, TOKEN
+from src.populate import get_evaluation_queue_df, get_leaderboard_df
+from src.submission.submit import add_new_eval
 
-def get_data_classifica():
-    dataset = pd.read_csv("leaderboard_general.csv", sep=',')
-    if 'model ' in dataset.columns:
-        dataset.rename(columns={'model ': 'model'}, inplace=True)
-    df_classifica = dataset[['model', 'helloswag_it acc norm', 'arc_it acc norm', 'm_mmlu_it acc shot 5']]
-    df_classifica['media'] = df_classifica[['helloswag_it acc norm', 'arc_it acc norm', 'm_mmlu_it acc shot 5']].mean(axis=1)
-    df_classifica['media'] = df_classifica['media'].round(3) 
-    df_classifica = df_classifica.sort_values(by='media', ascending=False) 
-    df_classifica = df_classifica[['model', 'media', 'helloswag_it acc norm', 'arc_it acc norm', 'm_mmlu_it acc shot 5']]
 
-    return df_classifica
+def restart_space():
+    API.restart_space(repo_id=REPO_ID)
 
-def get_data_totale():
-    dataset = pd.read_csv("leaderboard_general.csv", sep=',')
-    if 'model ' in dataset.columns:
-        dataset.rename(columns={'model ': 'model'}, inplace=True)
-    return dataset
+try:
+    print(EVAL_REQUESTS_PATH)
+    snapshot_download(
+        repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN
+    )
+except Exception:
+    restart_space()
+try:
+    print(EVAL_RESULTS_PATH)
+    snapshot_download(
+        repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN
+    )
+except Exception:
+    restart_space()
 
-with gr.Blocks() as demo:
 
-        with gr.Tab('Classifica Generale'):
+raw_data, original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
+leaderboard_df = original_df.copy()
 
-            gr.Markdown('''# Classifica generale degli LLM italiani''')
-            discord_link = 'https://discord.gg/m7sS3mduY2'
-            gr.Markdown('''
-            I modelli sottostanti sono stati testati con [lm_evaluation_harness](https://github.com/EleutherAI/lm-evaluation-harness) su task specifici per l'italiano introdotti con questa [PR](https://github.com/EleutherAI/lm-evaluation-harness/pull/1358).
-            L'intero progetto, i modelli e i dataset sono rigorosamente open source e tutti i risultati sono riproducibili lanciando i seguenti comandi:
+(
+    finished_eval_queue_df,
+    running_eval_queue_df,
+    pending_eval_queue_df,
+) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)
+
+
+# Searching and filtering
+def update_table(
+    hidden_df: pd.DataFrame,
+    columns: list,
+    type_query: list,
+    precision_query: str,
+    size_query: list,
+    show_deleted: bool,
+    query: str,
+):
+    filtered_df = filter_models(hidden_df, type_query, size_query, precision_query, show_deleted)
+    filtered_df = filter_queries(query, filtered_df)
+    df = select_columns(filtered_df, columns)
+    return df
+
+
+def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame:
+    return df[(df[AutoEvalColumn.model.name].str.contains(query, case=False))]
+
+
+def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:
+    always_here_cols = [
+        AutoEvalColumn.model_type_symbol.name,
+        AutoEvalColumn.model.name,
+    ]
+    # We use COLS to maintain sorting
+    filtered_df = df[
+        always_here_cols + [c for c in COLS if c in df.columns and c in columns] 
+    ]
+    return filtered_df
+
+
+def filter_queries(query: str, filtered_df: pd.DataFrame) -> pd.DataFrame:
+    final_df = []
+    if query != "":
+        queries = [q.strip() for q in query.split(";")]
+        for _q in queries:
+            _q = _q.strip()
+            if _q != "":
+                temp_filtered_df = search_table(filtered_df, _q)
+                if len(temp_filtered_df) > 0:
+                    final_df.append(temp_filtered_df)
+        if len(final_df) > 0:
+            filtered_df = pd.concat(final_df)
+            filtered_df = filtered_df.drop_duplicates(
+                subset=[AutoEvalColumn.model.name, AutoEvalColumn.precision.name, AutoEvalColumn.revision.name]
+            )
+
+    return filtered_df
+
+
+def filter_models(
+    df: pd.DataFrame, type_query: list, size_query: list, precision_query: list, show_deleted: bool
+) -> pd.DataFrame:
+    # Show all models
+    if show_deleted:
+        filtered_df = df
+    else:  # Show only still on the hub models
+        filtered_df = df[df[AutoEvalColumn.still_on_hub.name] == True]
+
+    type_emoji = [t[0] for t in type_query]
+    filtered_df = filtered_df.loc[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
+    filtered_df = filtered_df.loc[df[AutoEvalColumn.precision.name].isin(precision_query + ["None"])]
+
+    numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[s] for s in size_query]))
+    params_column = pd.to_numeric(df[AutoEvalColumn.params.name], errors="coerce")
+    mask = params_column.apply(lambda x: any(numeric_interval.contains(x)))
+    filtered_df = filtered_df.loc[mask]
+
+    return filtered_df
+
+
+demo = gr.Blocks(css=custom_css)
+with demo:
+    gr.HTML(TITLE)
+    gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
+
+    with gr.Tabs(elem_classes="tab-buttons") as tabs:
+        with gr.TabItem("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
+            with gr.Row():
+                with gr.Column():
+                    with gr.Row():
+                        search_bar = gr.Textbox(
+                            placeholder=" 🔍 Search for your model (separate multiple queries with `;`) and press ENTER...",
+                            show_label=False,
+                            elem_id="search-bar",
+                        )
+                    with gr.Row():
+                        shown_columns = gr.CheckboxGroup(
+                            choices=[
+                                c.name
+                                for c in fields(AutoEvalColumn)
+                                if not c.hidden and not c.never_hidden
+                            ],
+                            value=[
+                                c.name
+                                for c in fields(AutoEvalColumn)
+                                if c.displayed_by_default and not c.hidden and not c.never_hidden
+                            ],
+                            label="Select columns to show",
+                            elem_id="column-select",
+                            interactive=True,
+                        )
+                    with gr.Row():
+                        deleted_models_visibility = gr.Checkbox(
+                            value=False, label="Show gated/private/deleted models", interactive=True
+                        )
+                with gr.Column(min_width=320):
+                    #with gr.Box(elem_id="box-filter"):
+                    filter_columns_type = gr.CheckboxGroup(
+                        label="Model types",
+                        choices=[t.to_str() for t in ModelType],
+                        value=[t.to_str() for t in ModelType],
+                        interactive=True,
+                        elem_id="filter-columns-type",
+                    )
+                    filter_columns_precision = gr.CheckboxGroup(
+                        label="Precision",
+                        choices=[i.value.name for i in Precision],
+                        value=[i.value.name for i in Precision],
+                        interactive=True,
+                        elem_id="filter-columns-precision",
+                    )
+                    filter_columns_size = gr.CheckboxGroup(
+                        label="Model sizes (in billions of parameters)",
+                        choices=list(NUMERIC_INTERVALS.keys()),
+                        value=list(NUMERIC_INTERVALS.keys()),
+                        interactive=True,
+                        elem_id="filter-columns-size",
+                    )
+
+            leaderboard_table = gr.components.Dataframe(
+                value=leaderboard_df[
+                    [c.name for c in fields(AutoEvalColumn) if c.never_hidden]
+                    + shown_columns.value
+                ],
+                headers=[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value,
+                datatype=TYPES,
+                elem_id="leaderboard-table",
+                interactive=False,
+                visible=True,
+            )
+
+            # Dummy leaderboard for handling the case when the user uses backspace key
+            hidden_leaderboard_table_for_search = gr.components.Dataframe(
+                value=original_df[COLS],
+                headers=COLS,
+                datatype=TYPES,
+                visible=False,
+            )
+            search_bar.submit(
+                update_table,
+                [
+                    hidden_leaderboard_table_for_search,
+                    shown_columns,
+                    filter_columns_type,
+                    filter_columns_precision,
+                    filter_columns_size,
+                    deleted_models_visibility,
+                    search_bar,
+                ],
+                leaderboard_table,
+            )
+            for selector in [shown_columns, filter_columns_type, filter_columns_precision, filter_columns_size, deleted_models_visibility]:
+                selector.change(
+                    update_table,
+                    [
+                        hidden_leaderboard_table_for_search,
+                        shown_columns,
+                        filter_columns_type,
+                        filter_columns_precision,
+                        filter_columns_size,
+                        deleted_models_visibility,
+                        search_bar,
+                    ],
+                    leaderboard_table,
+                    queue=True,
+                )
+
+        with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2):
+            gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
+
+        with gr.TabItem("🚀 Submit here! ", elem_id="llm-benchmark-tab-table", id=3):
+            with gr.Column():
+                with gr.Row():
+                    gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text")
+
+                with gr.Column():
+                    with gr.Accordion(
+                        f"✅ Finished Evaluations ({len(finished_eval_queue_df)})",
+                        open=False,
+                    ):
+                        with gr.Row():
+                            finished_eval_table = gr.components.Dataframe(
+                                value=finished_eval_queue_df,
+                                headers=EVAL_COLS,
+                                datatype=EVAL_TYPES,
+                                row_count=5,
+                            )
+                    with gr.Accordion(
+                        f"🔄 Running Evaluation Queue ({len(running_eval_queue_df)})",
+                        open=False,
+                    ):
+                        with gr.Row():
+                            running_eval_table = gr.components.Dataframe(
+                                value=running_eval_queue_df,
+                                headers=EVAL_COLS,
+                                datatype=EVAL_TYPES,
+                                row_count=5,
+                            )
+
+                    with gr.Accordion(
+                        f"⏳ Pending Evaluation Queue ({len(pending_eval_queue_df)})",
+                        open=False,
+                    ):
+                        with gr.Row():
+                            pending_eval_table = gr.components.Dataframe(
+                                value=pending_eval_queue_df,
+                                headers=EVAL_COLS,
+                                datatype=EVAL_TYPES,
+                                row_count=5,
+                            )
+            with gr.Row():
+                gr.Markdown("# ✉️✨ Submit your model here!", elem_classes="markdown-text")
+
+            with gr.Row():
+                with gr.Column():
+                    model_name_textbox = gr.Textbox(label="Model name")
+                    revision_name_textbox = gr.Textbox(label="Revision commit", placeholder="main")
+                    model_type = gr.Dropdown(
+                        choices=[t.to_str(" : ") for t in ModelType if t != ModelType.Unknown],
+                        label="Model type",
+                        multiselect=False,
+                        value=None,
+                        interactive=True,
+                    )
+
+                with gr.Column():
+                    precision = gr.Dropdown(
+                        choices=[i.value.name for i in Precision if i != Precision.Unknown],
+                        label="Precision",
+                        multiselect=False,
+                        value="float16",
+                        interactive=True,
+                    )
+                    weight_type = gr.Dropdown(
+                        choices=[i.value.name for i in WeightType],
+                        label="Weights type",
+                        multiselect=False,
+                        value="Original",
+                        interactive=True,
+                    )
+                    base_model_name_textbox = gr.Textbox(label="Base model (for delta or adapter weights)")
+
+            submit_button = gr.Button("Submit Eval")
+            submission_result = gr.Markdown()
+            submit_button.click(
+                add_new_eval,
+                [
+                    model_name_textbox,
+                    base_model_name_textbox,
+                    revision_name_textbox,
+                    precision,
+                    weight_type,
+                    model_type,
+                ],
+                submission_result,
+            )
+
+    with gr.Row():
+        with gr.Accordion("📙 Citation", open=False):
+            citation_button = gr.Textbox(
+                value=CITATION_BUTTON_TEXT,
+                label=CITATION_BUTTON_LABEL,
+                lines=20,
+                elem_id="citation-button",
+                show_copy_button=True,
+            )
+
+scheduler = BackgroundScheduler()
+scheduler.add_job(restart_space, "interval", seconds=1800)
+scheduler.start()
+demo.queue(default_concurrency_limit=40).launch()
+
+
+# import gradio as gr
+# import pandas as pd
+
+# csv_filename = 'leaderboard.csv'
+# # url = 'https://docs.google.com/spreadsheets/d/1Oh3nrbdWjKuh9twJsc9yJLppiJeD_BZyKgCTOxRkALM/export?format=csv'
+
+# def get_data_classifica():
+#     dataset = pd.read_csv("leaderboard_general.csv", sep=',')
+#     if 'model ' in dataset.columns:
+#         dataset.rename(columns={'model ': 'model'}, inplace=True)
+#     df_classifica = dataset[['model', 'helloswag_it acc norm', 'arc_it acc norm', 'm_mmlu_it acc shot 5']]
+#     df_classifica['media'] = df_classifica[['helloswag_it acc norm', 'arc_it acc norm', 'm_mmlu_it acc shot 5']].mean(axis=1)
+#     df_classifica['media'] = df_classifica['media'].round(3) 
+#     df_classifica = df_classifica.sort_values(by='media', ascending=False) 
+#     df_classifica = df_classifica[['model', 'media', 'helloswag_it acc norm', 'arc_it acc norm', 'm_mmlu_it acc shot 5']]
+
+#     return df_classifica
+
+# def get_data_totale():
+#     dataset = pd.read_csv("leaderboard_general.csv", sep=',')
+#     if 'model ' in dataset.columns:
+#         dataset.rename(columns={'model ': 'model'}, inplace=True)
+#     return dataset
+
+# with gr.Blocks() as demo:
+
+#         with gr.Tab('Classifica Generale'):
+
+#             gr.Markdown('''# Classifica generale degli LLM italiani''')
+#             discord_link = 'https://discord.gg/m7sS3mduY2'
+#             gr.Markdown('''
+#             I modelli sottostanti sono stati testati con [lm_evaluation_harness](https://github.com/EleutherAI/lm-evaluation-harness) su task specifici per l'italiano introdotti con questa [PR](https://github.com/EleutherAI/lm-evaluation-harness/pull/1358).
+#             L'intero progetto, i modelli e i dataset sono rigorosamente open source e tutti i risultati sono riproducibili lanciando i seguenti comandi:
             
-                ```
-                   lm_eval --model hf --model_args pretrained=HUGGINGFACE_MODEL_ID  --tasks hellaswag_it,arc_it  --device cuda:0 --batch_size auto:2
-                ```
+#                 ```
+#                    lm_eval --model hf --model_args pretrained=HUGGINGFACE_MODEL_ID  --tasks hellaswag_it,arc_it  --device cuda:0 --batch_size auto:2
+#                 ```
     
-                ```
-                   lm_eval --model hf --model_args pretrained=HUGGINGFACE_MODEL_ID  --tasks m_mmlu_it --num_fewshot 5  --device cuda:0 --batch_size auto:2 
-                ```
-            ''')
-            gr.DataFrame(get_data_classifica, every=3600)            
-            gr.Markdown(f"Contributore principale: @giux78")
-            gr.Markdown('''
-            ### Risultati su modelli "internazionali" (instruct)
-
-            | Model | Arc-c  | HellaS | MMUL | AVG |
-            | --- | --- | --- | --- | --- |
-            | Mixtral 8x22b | 55.3 | 77.1 | 75.8 | 69.4 |
-            | LLama3 70b | 52.9 | 70.3 | 74.8 | 66.0 |
-            | command-r-plus | 49.5 | 74.9 | 67.6 | 64.0 |
-            | Mixtral 8x7b | 51.1 | 72.9 | 65.9 | 63.3 |
-            | LLama2 70b | 49.4 | 70.9 | 65.1 | 61.8 |
-            | command-r-v01 | 50.8 | 72.3 | 60.0 | 61.0 |
-            | Phi-3-mini | 43.46 | 61.44 | 56.55 | 53.8 |
-            | LLama3 8b | 44.3 | 59.9 | 55.7 | 53.3 |
-            | LLama1 34b | 42.9 | 65.4 | 49.0 | 52.4 |
-            | Mistral 7b | 41.49 | 61.22 | 52.53 | 51.7 |
-            | Gemma 1.1 7b | 41.75 | 54.07 | 49.45 | 48.4 |
-
-            ''')
-
-
-        with gr.Tab('Classifica RAG'):
-
-            gr.Markdown('''# Classifica RAG degli LLM italiani''')
-            gr.Markdown(f'''In questa sezione i modelli sono valutati su dei task di Q&A e ordinati per F1 Score e EM (Exact Match). La repo di riferimento è [questa](https://github.com/C080/open-llm-ita-leaderboard).
-                        I modelli in cima alla classifica sono ritenuti preferibili per i task di Retrieval Augmented Generation.''')
-            gr.Dataframe(pd.read_csv(csv_filename, sep=';'))
-            gr.Markdown(f"Si ringrazia il @galatolo per il codice dell'eval.")
+#                 ```
+#                    lm_eval --model hf --model_args pretrained=HUGGINGFACE_MODEL_ID  --tasks m_mmlu_it --num_fewshot 5  --device cuda:0 --batch_size auto:2 
+#                 ```
+#             ''')
+#             gr.DataFrame(get_data_classifica, every=3600)            
+#             gr.Markdown(f"Contributore principale: @giux78")
+#             gr.Markdown('''
+#             ### Risultati su modelli "internazionali" (instruct)
+
+#             | Model | Arc-c  | HellaS | MMUL | AVG |
+#             | --- | --- | --- | --- | --- |
+#             | Mixtral 8x22b | 55.3 | 77.1 | 75.8 | 69.4 |
+#             | LLama3 70b | 52.9 | 70.3 | 74.8 | 66.0 |
+#             | command-r-plus | 49.5 | 74.9 | 67.6 | 64.0 |
+#             | Mixtral 8x7b | 51.1 | 72.9 | 65.9 | 63.3 |
+#             | LLama2 70b | 49.4 | 70.9 | 65.1 | 61.8 |
+#             | command-r-v01 | 50.8 | 72.3 | 60.0 | 61.0 |
+#             | Phi-3-mini | 43.46 | 61.44 | 56.55 | 53.8 |
+#             | LLama3 8b | 44.3 | 59.9 | 55.7 | 53.3 |
+#             | LLama1 34b | 42.9 | 65.4 | 49.0 | 52.4 |
+#             | Mistral 7b | 41.49 | 61.22 | 52.53 | 51.7 |
+#             | Gemma 1.1 7b | 41.75 | 54.07 | 49.45 | 48.4 |
+
+#             ''')
+
+
+#         with gr.Tab('Classifica RAG'):
+
+#             gr.Markdown('''# Classifica RAG degli LLM italiani''')
+#             gr.Markdown(f'''In questa sezione i modelli sono valutati su dei task di Q&A e ordinati per F1 Score e EM (Exact Match). La repo di riferimento è [questa](https://github.com/C080/open-llm-ita-leaderboard).
+#                         I modelli in cima alla classifica sono ritenuti preferibili per i task di Retrieval Augmented Generation.''')
+#             gr.Dataframe(pd.read_csv(csv_filename, sep=';'))
+#             gr.Markdown(f"Si ringrazia il @galatolo per il codice dell'eval.")
             
 
-        with gr.Tab('Eval aggiuntive'):
+#         with gr.Tab('Eval aggiuntive'):
 
-            gr.Markdown('''# Altre evaluation''')
-            gr.Markdown('''Qui ci sono altri test di altri modelli, che non sono ancora stati integrati nella classifica generale.''')
-            gr.DataFrame(get_data_totale, every=3600) 
+#             gr.Markdown('''# Altre evaluation''')
+#             gr.Markdown('''Qui ci sono altri test di altri modelli, che non sono ancora stati integrati nella classifica generale.''')
+#             gr.DataFrame(get_data_totale, every=3600) 
 
-        with gr.Tab('Informazioni'):
+#         with gr.Tab('Informazioni'):
             
-            form_link = "https://forms.gle/Gc9Dfu52xSBhQPpAA"
-            gr.Markdown('''# Community discord
-            Se vuoi contribuire al progetto o semplicemente unirti alla community di LLM italiani unisciti al nostro [discord!](https://discord.gg/m7sS3mduY2)
-            # Aggiungi il tuo modello
-            Se hai sviluppato un tuo modello che vuoi far valutare, compila il form [qui](https://forms.gle/Gc9Dfu52xSBhQPpAA) è tutto gratuito!         
-            ''') 
+#             form_link = "https://forms.gle/Gc9Dfu52xSBhQPpAA"
+#             gr.Markdown('''# Community discord
+#             Se vuoi contribuire al progetto o semplicemente unirti alla community di LLM italiani unisciti al nostro [discord!](https://discord.gg/m7sS3mduY2)
+#             # Aggiungi il tuo modello
+#             Se hai sviluppato un tuo modello che vuoi far valutare, compila il form [qui](https://forms.gle/Gc9Dfu52xSBhQPpAA) è tutto gratuito!         
+#             ''') 
         
-        with gr.Tab('Sponsor'):
+#         with gr.Tab('Sponsor'):
 
-            gr.Markdown('''
-            # Sponsor
-            Le evaluation della classifica generale sono state gentilmente offerte da un provider cloud italiano [seeweb.it](https://www.seeweb.it/) specializzato in servizi di GPU cloud e AI.
-            ''')
+#             gr.Markdown('''
+#             # Sponsor
+#             Le evaluation della classifica generale sono state gentilmente offerte da un provider cloud italiano [seeweb.it](https://www.seeweb.it/) specializzato in servizi di GPU cloud e AI.
+#             ''')
             
-demo.launch()
+# demo.launch()