import streamlit as st
import pandas as pd
import wandb
import time
from datetime 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 ID": 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({
                    "Created At": run.created_at,

                    "Miner 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"),

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