utils.py

import streamlit as st
import pandas as pd
import wandb
import datetime


def get_all_competition_summary(api, projects):
    
    number_of_competitions = 0
    number_of_runs = 0
    
    for project in projects:
        entity = projects[project]["entity"]
        project_name = projects[project]["project"]
        runs = api.runs(f"{entity}/{project_name}")
        number_of_competitions += 1
        number_of_runs += len(runs)
        
    return number_of_competitions,number_of_runs

def fetch_competition_summary(api, projects, selected_project):
    data = []
        
    entity = projects[selected_project]["entity"]
    project = projects[selected_project]["project"]
    
    runs = api.runs(f"{entity}/{project}")
    
    for run in runs:
        try:
            summary = run.summary
            if summary.get("validator_hotkey") and summary.get("winning_hotkey"):
                data.append({
                    "ID": run.id,
                    "Validator Hotkey": summary.get("validator_hotkey"),
                    "Winning Hotkey": summary.get("winning_hotkey"),
                    "Run Time (s)": summary.get("run_time_s"),
                    "Created At": run.created_at,
                })
        except Exception as e:
            st.write(f"Error processing run {run.id}: {str(e)}")
    
    df = pd.DataFrame(data)
    if not df.empty:
        df['Created At'] = pd.to_datetime(df['Created At'])
        df = df.sort_values(by="Created At", ascending=False)
    
    return df

def fetch_models_evaluation(api, projects, selected_project):
    data = []
    
    entity = projects[selected_project]["entity"]
    project = projects[selected_project]["project"]
    
    runs = api.runs(f"{entity}/{project}")
    
    for run in runs:
        try:
            summary = run.summary
            if summary.get("score") is not None:  # Assuming runs with score are model evaluations
                data.append({
                    "ID": run.id,
                    "Username": run.user.username,
                    "Hotkey": summary.get("miner_hotkey", "N/A"),
                    "Score": summary.get("score"),

                    "F1-beta": summary.get("fbeta"),
                    "Accuracy": summary.get("accuracy"),
                    "Recall": summary.get("recall"),
                    "Precision": summary.get("precision"),

                    "Tested entries": summary.get("tested_entries"),
                    # "Run Time (s)": summary.get("run_time_s"),

                    "ROC AUC": summary.get("roc_auc"),
                    "Confusion Matrix": summary.get("confusion_matrix"),

                    "Created At": run.created_at,
                    #TODO link to huggingface model
                })
        except Exception as e:
            st.write(f"Error processing run {run.id}: {str(e)}")
    
    df = pd.DataFrame(data)
    if not df.empty:
        df['Created At'] = pd.to_datetime(df['Created At'])
        df = df.sort_values(by="Created At", ascending=False)
    
    return df

def update_leader_info(leader_info, competition, best_model):
    current_date = datetime.now().strftime("%Y-%m-%d")
    if leader_info.get(competition) is None:
        leader_info[competition] = {
            "Username": best_model["Username"],
            "Hotkey": best_model["Hotkey"],
            "Date": current_date,
            "Days on Top": 1
        }
    else:
        if leader_info[competition]["UID"] == best_model["ID"]:
            leader_info[competition]["Days on Top"] += 1
        else:
            leader_info[competition] = {
                "Username": best_model["Username"],
                "Hotkey": best_model["Hotkey"],
                "Date": current_date,
                "Days on Top": 1
            }
    return leader_info[competition]

def highlight_score_column(s):
    """
    Highlight the 'Score' column with a custom background color.
    """
    return ['background-color: yellow' if s.name == 'Score' else '' for _ in s]

def set_custom_table_style():
    st.markdown("""
    <style>
    .dataframe, .summary-table {
        width: 100%;
        border-collapse: collapse;
        margin: 20px 0;
        font-size: 16px;
        min-width: 400px;
    }
    .dataframe th,
    .dataframe td,
    .summary-table th,
    .summary-table td {
        padding: 12px 15px;
        border: 1px solid #CCCCCC;
        text-align: left;
    }
    .dataframe thead th,
    .summary-table th {
        background-color: #333333;
        color: #FFFFFF;
    }
    .dataframe tbody tr:nth-child(even),
    .summary-table tr:nth-child(even) {
        background-color: #F0F0F0;
    }
    .dataframe tbody tr:nth-child(odd),
    .summary-table tr:nth-child(odd) {
        background-color: #E0E0E0;
    }
    .dataframe tbody td,
    .summary-table td {
        color: #333333;
    }
    .dataframe tbody tr:hover,
    .summary-table tr:hover {
        background-color: #D1D1D1;
    }
    .table-container {
        overflow-x: auto;
    }
    </style>
    """, unsafe_allow_html=True)