first commit

Makefile

@@ -1,13 +1,14 @@
-.PHONY: style format
-
+.PHONY: style format run
 
 style:
 	python -m black --line-length 119 .
 	python -m isort .
 	ruff check --fix .
 
-
 quality:
 	python -m black --check --line-length 119 .
 	python -m isort --check-only .
 	ruff check .
+
+run:
+	python -m src.main

app.py

@@ -1,204 +1,130 @@
 import gradio as gr
-from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns
+import bittensor as bt
+import requests
 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.")
-    return Leaderboard(
-        value=dataframe,
-        datatype=[c.type for c in fields(AutoEvalColumn)],
-        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:",
-        ),
-        search_columns=[AutoEvalColumn.model.name, AutoEvalColumn.license.name],
-        hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden],
-        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
-            ),
-        ],
-        bool_checkboxgroup_label="Hide models",
-        interactive=False,
-    )
-
 
+# Custom CSS for better appearance
+custom_css = """
+.gradio-container {
+    max-width: 1200px !important;
+    margin: auto;
+}
+.title {
+    text-align: center;
+    margin-bottom: 1rem;
+}
+.status-active { color: green; }
+.status-error { color: red; }
+"""
+
+# Initialize bittensor objects
+subtensor = bt.subtensor()
+metagraph = bt.metagraph(netuid=36)
+
+def get_validator_data() -> pd.DataFrame:
+    validator_ids = [i for i in range(len(metagraph.validator_permit)) if metagraph.validator_permit[i]]
+    
+    results = []
+    for uid in validator_ids:
+        try:
+            ip = metagraph.axons[uid].ip_str().split('/')[-1]
+            response = requests.get(f'http://{ip}/step', timeout=5)
+            response.raise_for_status()
+            validator_info = {
+                'UID': uid,
+                'IP': ip,
+                'Bits': response.json().get('bits', 0),
+                'Status': '✅ Active'
+            }
+        except Exception as e:
+            validator_info = {
+                'UID': uid,
+                'IP': metagraph.axons[uid].ip_str().split('/')[-1],
+                'Bits': 0,
+                'Status': f'❌ Error: {str(e)[:50]}...' if len(str(e)) > 50 else str(e)
+            }
+        results.append(validator_info)
+    
+    df = pd.DataFrame(results)
+    return df.sort_values('Bits', ascending=False)
+
+# Create the Gradio interface
 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):
-            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")
 
+with demo:
+    gr.HTML(
+        """
+        <div class="title">
+            <h1>🏆 Validator Bits Leaderboard</h1>
+            <p>Real-time tracking of validator performance and bits</p>
+        </div>
+        """
+    )
+    
+    with gr.Tabs() as tabs:
+        with gr.Tab("📊 Leaderboard"):
+            leaderboard = gr.DataFrame(
+                headers=['UID', 'IP', 'Bits', 'Status'],
+                datatype=['number', 'str', 'number', 'str'],
+                interactive=False
+            )
+            
             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,
+                refresh_button = gr.Button("🔄 Refresh Data", variant="primary")
+                auto_refresh = gr.Checkbox(
+                    label="Auto-refresh (5 min)",
+                    value=True,
+                    interactive=True
+                )
+            
+            status_message = gr.Markdown("Last updated: Never")
+            
+        with gr.Tab("ℹ️ About"):
+            gr.Markdown(
+                """
+                ## About this Leaderboard
+                
+                This dashboard shows real-time information about validators on the network:
+                
+                - **UID**: Unique identifier of the validator
+                - **IP**: Validator's IP address
+                - **Bits**: Current bits count
+                - **Status**: Active/Error status of the validator
+                
+                Data is automatically refreshed every 5 minutes, or you can manually refresh using the button.
+                """
             )
 
-    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 update_leaderboard():
+        df = get_validator_data()
+        timestamp = pd.Timestamp.now().strftime("%Y-%m-%d %H:%M:%S UTC")
+        return df, f"Last updated: {timestamp}"
+    
+    refresh_button.click(
+        fn=update_leaderboard,
+        outputs=[leaderboard, status_message]
+    )
+
+    # Auto-refresh logic
+    def setup_auto_refresh():
+        if demo.scheduler:
+            demo.scheduler.shutdown()
+        demo.scheduler = BackgroundScheduler()
+        demo.scheduler.add_job(
+            lambda: demo.queue(update_leaderboard), 
+            'interval', 
+            minutes=5
+        )
+        demo.scheduler.start()
+
+    # Initial data load
+    demo.load(
+        fn=update_leaderboard,
+        outputs=[leaderboard, status_message]
+    )
+    
+    setup_auto_refresh()
 
-scheduler = BackgroundScheduler()
-scheduler.add_job(restart_space, "interval", seconds=1800)
-scheduler.start()
-demo.queue(default_concurrency_limit=40).launch()
+# Launch the interface
+demo.queue(default_concurrency_limit=5).launch()

axoninfo.py

@@ -0,0 +1,41 @@
+import bittensor as bt
+import requests
+import json
+
+subtensor = bt.subtensor()
+metagraph = bt.metagraph(netuid=36)
+
+#print(metagraph.axons)
+
+#print(metagraph.validator_permit[48])
+
+
+def get_validator_ids():
+    return [i for i in range(len(metagraph.validator_permit)) if metagraph.validator_permit[i]]
+
+def get_validator_axons(list):
+    return [metagraph.axons[uid].ip_str().split('/')[-1] for uid in list]
+
+
+print(get_validator_axons(get_validator_ids()))
+
+response = requests.get('http://' + metagraph.axons[178].ip_str().split('/')[-1] + '/step')
+response.raise_for_status()
+print(response.json())
+def get_validator_bits(validator_ids, timeout=5, retries=3):
+    results = []
+    for uid in validator_ids:
+        for attempt in range(retries):
+            try:
+                ip = metagraph.axons[uid].ip_str().split('/')[-1]
+                response = requests.get(f'http://{ip}/step', timeout=timeout)
+                response.raise_for_status()
+                results.append(response.json())
+                break  # Success, exit retry loop
+            except requests.exceptions.RequestException as e:
+                if attempt == retries - 1:  # Last attempt
+                    print(f"Failed to get bits from validator {uid} after {retries} attempts: {str(e)}")
+                continue
+    return results
+
+print(get_validator_bits(get_validator_ids()))

requirements.txt

@@ -1,16 +1,5 @@
-APScheduler
-black
-datasets
-gradio
-gradio[oauth]
-gradio_leaderboard==0.0.9
-gradio_client
-huggingface-hub>=0.18.0
-matplotlib
-numpy
+gradio>=4.0.0
+bittensor
 pandas
-python-dateutil
-tqdm
-transformers
-tokenizers>=0.15.0
-sentencepiece
+apscheduler
+requests