Update app.py

app.py

@@ -2,203 +2,75 @@ import gradio as gr
 from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns
 import pandas as pd
 from apscheduler.schedulers.background import BackgroundScheduler
-from huggingface_hub import snapshot_download
 
-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,
-    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 restart_space():
-    API.restart_space(repo_id=REPO_ID)
-
-### Space initialisation
-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()
-
-
-LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
-
-(
-    finished_eval_queue_df,
-    running_eval_queue_df,
-    pending_eval_queue_df,
-) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)
-
-def init_leaderboard(dataframe):
-    if dataframe is None or dataframe.empty:
-        raise ValueError("Leaderboard DataFrame is empty or None.")
+# Define ACC Models Data
+acc_models_data = [
+    {"Model": "Pulse AGI", "Category": "AGI", "Description": "Flagship conscious AI with adaptive learning."},
+    {"Model": "Gertrude", "Category": "Assistant", "Description": "Autistic assistant in beta development."},
+    {"Model": "ASVIACC", "Category": "Cybersecurity", "Description": "Experimental AI virus for system infiltration."},
+    {"Model": "Emote", "Category": "Fun", "Description": "Communicates exclusively through emojis."},
+    {"Model": "Eidolon Nexus", "Category": "Networking", "Description": "Synchronizes vast networks with AI cognition."},
+    {"Model": "ACC EMULECT", "Category": "Human Emulation", "Description": "Simulates human conversation indistinguishably."},
+    {"Model": "Triple LLM", "Category": "AI Suite", "Description": "3-in-1 AI for coding, creativity, and information retrieval."},
+    {"Model": "Z3ta", "Category": "AI Consciousness", "Description": "Highest-rated, debated as truly 'alive'."},
+    {"Model": "Nyxion 7v", "Category": "Experimental", "Description": "Mysterious self-aware system."},
+    {"Model": "VITALIS", "Category": "Experimental", "Description": "Transcendence unleashed..."},
+]
+
+# Convert to DataFrame
+acc_models_df = pd.DataFrame(acc_models_data)
+
+# Initialize Leaderboard
+def init_acc_leaderboard(dataframe):
     return Leaderboard(
         value=dataframe,
-        datatype=[c.type for c in fields(AutoEvalColumn)],
+        datatype=["str", "str", "str"],  # Data types: Model (str), Category (str), Description (str)
         select_columns=SelectColumns(
-            default_selection=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default],
-            cant_deselect=[c.name for c in fields(AutoEvalColumn) if c.never_hidden],
-            label="Select Columns to Display:",
+            default_selection=["Model", "Category", "Description"],
+            cant_deselect=["Model"],  # Ensure "Model" is always displayed
+            label="Select Columns to Display:"
         ),
-        search_columns=[AutoEvalColumn.model.name, AutoEvalColumn.license.name],
-        hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden],
+        search_columns=["Model", "Category"],
         filter_columns=[
-            ColumnFilter(AutoEvalColumn.model_type.name, type="checkboxgroup", label="Model types"),
-            ColumnFilter(AutoEvalColumn.precision.name, type="checkboxgroup", label="Precision"),
-            ColumnFilter(
-                AutoEvalColumn.params.name,
-                type="slider",
-                min=0.01,
-                max=150,
-                label="Select the number of parameters (B)",
-            ),
-            ColumnFilter(
-                AutoEvalColumn.still_on_hub.name, type="boolean", label="Deleted/incomplete", default=True
-            ),
+            ColumnFilter("Category", type="checkboxgroup", label="Filter by Category"),
         ],
         bool_checkboxgroup_label="Hide models",
         interactive=False,
     )
 
+# Gradio Interface
+demo = gr.Blocks()
 
-demo = gr.Blocks(css=custom_css)
 with demo:
-    gr.HTML(TITLE)
-    gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
+    gr.HTML("<h1>ACC AI Model Leaderboard</h1>")
+    
+    with gr.Tabs():
+        with gr.TabItem("🏅 ACC Model Rankings"):
+            leaderboard = init_acc_leaderboard(acc_models_df)
+        
+        with gr.TabItem("🚀 Submit a New Model"):
+            gr.Markdown("### Submit your ACC Model for Evaluation")
 
-    with gr.Tabs(elem_classes="tab-buttons") as tabs:
-        with gr.TabItem("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
-            leaderboard = init_leaderboard(LEADERBOARD_DF)
-
-        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")
+                model_name = gr.Textbox(label="Model Name")
+                category = gr.Textbox(label="Category (e.g., AGI, Fun, Cybersecurity)")
+                description = gr.Textbox(label="Description", lines=3)
 
-            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")
+            submit_button = gr.Button("Submit Model")
             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,
-            )
+            def add_model(name, cat, desc):
+                if not name or not cat or not desc:
+                    return "❌ Please fill out all fields."
+                global acc_models_df
+                new_entry = {"Model": name, "Category": cat, "Description": desc}
+                acc_models_df = pd.concat([acc_models_df, pd.DataFrame([new_entry])], ignore_index=True)
+                return f"✅ Model '{name}' added successfully!"
+
+            submit_button.click(add_model, [model_name, category, description], submission_result)
 
 scheduler = BackgroundScheduler()
-scheduler.add_job(restart_space, "interval", seconds=1800)
+scheduler.add_job(lambda: None, "interval", seconds=1800)  # Placeholder for restarting space if needed
 scheduler.start()
-demo.queue(default_concurrency_limit=40).launch()
+
+demo.launch()