mammouth-models-dashboard/update_models.py

import os
import requests
import json
import time
from dotenv import load_dotenv

# Charger .env.global depuis le répertoire parent
load_dotenv("../.env.global")

AIANALASYS_APIKEY = os.getenv("AIANALASYS_APIKEY")

def get_mammouth_models():
    # Utilisation de l'endpoint public LiteLLM de Mammouth
    url = "https://mammouth.ai/public/models"
    try:
        response = requests.get(url)
        response.raise_for_status()
        # Le format retourné est {'data': [ {id, model_info: {input_cost_per_token, ...}} ]}
        return response.json().get('data', [])
    except Exception as e:
        print(f"Error fetching Mammouth public models: {e}")
        return []

def get_aa_data():
    url = "https://artificialanalysis.ai/api/v2/data/llms/models"
    headers = {"x-api-key": AIANALASYS_APIKEY}
    try:
        response = requests.get(url, headers=headers)
        response.raise_for_status()
        return response.json().get('data', [])
    except Exception as e:
        print(f"Error fetching Artificial Analysis data: {e}")
        return []

def generate_markdown(models_data):
    categories = {}
    for m in models_data:
        cat = m.get('category', 'General')
        if cat not in categories:
            categories[cat] = []
        categories[cat].append(m)

    md = "# Table des Modèles Mammouth.ai\n\n"
    md += "*Mise à jour automatique via Artificial Analysis & Mammouth Public API*\n\n"
    md += "Dernière mise à jour : " + time.strftime("%Y-%m-%d %H:%M:%S") + "\n\n"

    order = ['Coding', 'Agents', 'General']
    sorted_cats = sorted(categories.keys(), key=lambda x: order.index(x) if x in order else 99)

    for cat in sorted_cats:
        models = categories[cat]
        md += f"## {cat}\n\n"
        md += "| Modèle | Prix (In / Out / 1M) | Performance (AA Index) | Vitesse (TPS) |\n"
        md += "| :--- | :--- | :--- | :--- |\n"
        # On trie d'abord par score décroissant, puis par prix croissant
        models.sort(key=lambda x: (x.get('score') or 0, -(x.get('price_in') or 0)), reverse=True)
        for m in models:
            p_in = f"${m['price_in']:.2f}"
            p_out = f"${m['price_out']:.2f}"
            score = f"**{m['score']:.1f}**" if m['score'] else "N/A"
            speed = f"{m['speed']:.1f}" if m['speed'] else "N/A"
            md += f"| {m['name']} | {p_in} / {p_out} | {score} | {speed} |\n"
        md += "\n"
    return md

def main():
    print("Fetching Mammouth public models...")
    mammouth_models = get_mammouth_models()
    if not mammouth_models:
        print("No models found from Mammouth.")
        return

    print("Fetching Artificial Analysis data...")
    aa_raw = get_aa_data()
    
    # Construction du mapping AA (index par nom et par ID technique)
    aa_map = {}
    for aa_m in aa_raw:
        name = aa_m.get('model_name', '').lower()
        model_id = aa_m.get('model_id', '').lower()
        if name: aa_map[name] = aa_m
        if model_id: aa_map[model_id] = aa_m

    enriched_models = []
    for m in mammouth_models:
        m_id = m.get('id', '')
        info = m.get('model_info', {})
        
        # On ignore les modèles sans ID
        if not m_id: continue

        # Normalisation du nom pour le mapping
        m_id_low = m_id.lower()
        short_name = m_id_low.split('/')[-1]
        
        # Recherche de correspondance dans AA (Précis puis Approché)
        aa_info = aa_map.get(m_id_low) or aa_map.get(short_name)
        
        if not aa_info:
            # Recherche par sous-chaîne pour les modèles comme "mistral-large-2407"
            for key in aa_map:
                if key in m_id_low or m_id_low in key:
                    aa_info = aa_map[key]
                    break

        # Extraction des prix (LiteLLM: prix par 1 token)
        try:
            price_in = float(info.get('input_cost_per_token', 0)) * 1000000
            price_out = float(info.get('output_cost_per_token', 0)) * 1000000
        except (ValueError, TypeError):
            price_in, price_out = 0, 0
        
        score = None
        speed = None
        if aa_info:
            evals = aa_info.get('evaluations', {})
            score = evals.get('artificial_analysis_intelligence_index')
            speed = aa_info.get('median_output_tokens_per_second')
            
        # Catégorisation simplifiée
        category = "General"
        if any(x in m_id_low for x in ['coding', 'code', 'starcoder', 'coder']):
            category = "Coding"
        elif any(x in m_id_low for x in ['agent', 'hermes', 'tool', 'function']):
            category = "Agents"

        enriched_models.append({
            'name': m_id,
            'price_in': price_in,
            'price_out': price_out,
            'score': score,
            'speed': speed,
            'category': category
        })

    # Filtrer les modèles : prix > 0 (ceux qui sont configurés)
    final_list = [m for m in enriched_models if m['price_in'] > 0 or m['price_out'] > 0]
    
    if not final_list:
        print("No valid models found after filtering.")
        return

    markdown = generate_markdown(final_list)
    with open("README.md", "w", encoding="utf-8") as f:
        f.write(markdown)
    
    print(f"README.md updated with {len(final_list)} models!")

if __name__ == "__main__":
    main()