158 lines
6.2 KiB
Python
158 lines
6.2 KiB
Python
import os
|
|
import requests
|
|
import json
|
|
import time
|
|
import re
|
|
from dotenv import load_dotenv
|
|
|
|
load_dotenv("../.env.global")
|
|
AIANALASYS_APIKEY = os.getenv("AIANALASYS_APIKEY")
|
|
|
|
def get_mammouth_models():
|
|
url = "https://api.mammouth.ai/public/models"
|
|
try:
|
|
requests.packages.urllib3.disable_warnings()
|
|
response = requests.get(url, verify=False)
|
|
response.raise_for_status()
|
|
return response.json().get('data', [])
|
|
except Exception as e:
|
|
print(f"Error fetching Mammouth 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 AA data: {e}")
|
|
return []
|
|
|
|
def clean_id(model_id):
|
|
id_clean = re.sub(r'-\d{4,8}', '', model_id.lower())
|
|
id_clean = id_clean.replace('-latest', '').replace('-preview', '').replace('-instruct', '')
|
|
return id_clean.strip()
|
|
|
|
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 = "# Dashboard des Modèles Mammouth.ai\n\n"
|
|
md += "*Analyse comparative basée sur le prix (Mammouth) et la performance (Artificial Analysis).*\n\n"
|
|
md += f"Dernière mise à jour : {time.strftime('%Y-%m-%d %H:%M:%S')}\n\n"
|
|
md += "### Légende :\n"
|
|
md += "- **Note Prix** : 10 = Le moins cher, 0 = Le plus cher.\n"
|
|
md += "- **Efficience** : Ratio Performance / Prix. Un score élevé indique un excellent rapport qualité/prix.\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:
|
|
md += f"## {cat}\n\n"
|
|
md += "| Modèle | Prix (In/Out 1M) | Score | Vitesse | Note Prix | **Efficience** |\n"
|
|
md += "| :--- | :--- | :--- | :--- | :--- | :--- |\n"
|
|
models = categories[cat]
|
|
# Tri par Efficience décroissante
|
|
models.sort(key=lambda x: x.get('efficiency_score', 0), reverse=True)
|
|
for m in models:
|
|
score_str = f"**{m['score']:.1f}**" if m['score'] else "N/A"
|
|
speed_str = f"{m['speed']:.1f}" if m['speed'] else "N/A"
|
|
p_score = f"{m['price_score']:.1f}" if m['price_score'] is not None else "N/A"
|
|
eff_score = f"**{m['efficiency_score']:.1f}**" if m['efficiency_score'] else "N/A"
|
|
md += f"| {m['name']} | ${m['price_in']:.2f}/${m['price_out']:.2f} | {score_str} | {speed_str} | {p_score} | {eff_score} |\n"
|
|
md += "\n"
|
|
return md
|
|
|
|
def main():
|
|
print("Calcul de l'efficience des modèles...")
|
|
m_models = get_mammouth_models()
|
|
aa_data = get_aa_data()
|
|
|
|
aa_map = {m.get('slug', '').lower(): m for m in aa_data}
|
|
for aa_m in aa_data:
|
|
aa_map[aa_m.get('name', '').lower()] = aa_m
|
|
|
|
enriched = []
|
|
# On calcule d'abord les prix pour déterminer les échelles de note
|
|
temp_list = []
|
|
for m in m_models:
|
|
m_id = m.get('id', '')
|
|
info = m.get('model_info', {})
|
|
if not m_id: continue
|
|
|
|
price_in = float(info.get('input_cost_per_token', 0)) * 1000000
|
|
price_out = float(info.get('output_cost_per_token', 0)) * 1000000
|
|
# Prix combiné (moyenne pondérée 3:1 comme AA)
|
|
blended_price = (price_in * 0.75) + (price_out * 0.25)
|
|
|
|
if blended_price > 0:
|
|
temp_list.append((m, blended_price, price_in, price_out))
|
|
|
|
if not temp_list: return
|
|
|
|
# Calcul des échelles de prix pour la note (Log scale pour mieux différencier)
|
|
min_p = min(x[1] for x in temp_list)
|
|
max_p = max(x[1] for x in temp_list)
|
|
|
|
for m_data, b_price, p_in, p_out in temp_list:
|
|
m_id = m_data.get('id', '')
|
|
m_id_clean = clean_id(m_id)
|
|
short_id = m_id_clean.split('/')[-1]
|
|
|
|
aa_info = aa_map.get(m_id_clean) or aa_map.get(short_id)
|
|
if not aa_info:
|
|
norm = m_id_clean.replace('-', '').replace('.', '')
|
|
for k, v in aa_map.items():
|
|
if k.replace('-', '').replace('.', '') == norm:
|
|
aa_info = v
|
|
break
|
|
|
|
# Note Prix : 10 pour le moins cher, 0 pour le plus cher
|
|
# Formule : 10 * (1 - (price - min) / (max - min))
|
|
price_score = 10 * (1 - (b_price - min_p) / (max_p - min_p)) if max_p > min_p else 10
|
|
|
|
score = None
|
|
speed = None
|
|
category = "General"
|
|
if any(x in m_id_clean for x in ['coding', 'code', 'starcoder', 'codestral', 'coder']): category = "Coding"
|
|
elif any(x in m_id_clean for x in ['agent', 'hermes', 'tool', 'function', 'sonar']): category = "Agents"
|
|
|
|
if aa_info:
|
|
evals = aa_info.get('evaluations', {})
|
|
score = evals.get('artificial_analysis_coding_index') if category == "Coding" else evals.get('artificial_analysis_intelligence_index')
|
|
if not score: score = evals.get('artificial_analysis_intelligence_index')
|
|
speed = aa_info.get('median_output_tokens_per_second')
|
|
|
|
# Efficience : On combine la performance et le prix
|
|
# Si pas de score AA, on base l'efficience uniquement sur le prix (avec un bonus de base)
|
|
efficiency_score = 0
|
|
if score:
|
|
# Score normalisé (0-100) * Note Prix (0-10) / 10
|
|
efficiency_score = (score * price_score) / 10
|
|
else:
|
|
# Modèle sans benchmark : on lui donne une efficience basée sur son prix seul
|
|
efficiency_score = price_score * 2 # Moins prioritaire que ceux avec score
|
|
|
|
enriched.append({
|
|
'name': m_id,
|
|
'price_in': p_in,
|
|
'price_out': p_out,
|
|
'score': score,
|
|
'speed': speed,
|
|
'price_score': price_score,
|
|
'efficiency_score': efficiency_score,
|
|
'category': category
|
|
})
|
|
|
|
with open("README.md", "w", encoding="utf-8") as f:
|
|
f.write(generate_markdown(enriched))
|
|
print(f"Success! Dashboard updated with {len(enriched)} models.")
|
|
|
|
if __name__ == "__main__":
|
|
main()
|