133 lines
4.7 KiB
Python
133 lines
4.7 KiB
Python
import os
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import requests
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import json
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import time
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from dotenv import load_dotenv
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load_dotenv("../.env.global")
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MAMMOUTH_APIKEY = os.getenv("MAMMOUTH_APIKEY")
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AIANALASYS_APIKEY = os.getenv("AIANALASYS_APIKEY")
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def get_mammouth_models():
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# Retour au point d'accès OpenRouter compatible qui est plus stable
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url = "https://openrouter.ai/api/v1/models"
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headers = {"Authorization": f"Bearer {MAMMOUTH_APIKEY}"}
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try:
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response = requests.get(url, headers=headers)
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response.raise_for_status()
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return response.json().get('data', [])
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except Exception as e:
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print(f"Error fetching Mammouth models: {e}")
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return []
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def get_aa_data():
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url = "https://artificialanalysis.ai/api/v2/data/llms/models"
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headers = {"x-api-key": AIANALASYS_APIKEY}
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try:
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response = requests.get(url, headers=headers)
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response.raise_for_status()
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return response.json().get('data', [])
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except Exception as e:
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print(f"Error fetching Artificial Analysis data: {e}")
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return []
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def generate_markdown(models_data):
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categories = {}
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for m in models_data:
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cat = m.get('category', 'General')
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if cat not in categories:
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categories[cat] = []
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categories[cat].append(m)
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md = "# Table des Modèles Mammouth.ai\n\n"
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md += "*Mise à jour automatique via Artificial Analysis & Mammouth API*\n\n"
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md += f"Dernière mise à jour : {time.strftime('%Y-%m-%d %H:%M:%S')}\n\n"
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order = ['Coding', 'Agents', 'General']
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sorted_cats = sorted(categories.keys(), key=lambda x: order.index(x) if x in order else 99)
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for cat in sorted_cats:
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models = categories[cat]
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# Tri par score (desc) puis prix (asc)
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models.sort(key=lambda x: (x.get('score') or 0, -(x.get('price_in') or 999)), reverse=True)
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md += f"## {cat}\n\n"
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md += "| Modèle | Prix (In / Out / 1M) | Intelligence Index | Vitesse (TPS) |\n"
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md += "| :--- | :--- | :--- | :--- |\n"
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for m in models:
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p_in = f"${m['price_in']:.2f}"
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p_out = f"${m['price_out']:.2f}"
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score = f"**{m['score']:.1f}**" if m['score'] else "N/A"
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speed = f"{m['speed']:.1f}" if m['speed'] else "N/A"
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md += f"| {m['name']} | {p_in} / {p_out} | {score} | {speed} |\n"
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md += "\n"
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return md
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def main():
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print("Fetching models...")
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mammouth_models = get_mammouth_models()
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aa_raw = get_aa_data()
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# Mapping AA : Priorité aux noms exacts
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aa_map_exact = {m.get('model_name', '').lower(): m for m in aa_raw}
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aa_map_id = {m.get('model_id', '').lower(): m for m in aa_raw}
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enriched_models = []
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for m in mammouth_models:
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m_id = m.get('id', '')
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m_name = m.get('name', '').lower()
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short_id = m_id.split('/')[-1].lower()
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# Mapping plus strict pour éviter les scores identiques
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aa_info = aa_map_id.get(m_id.lower()) or aa_map_exact.get(m_name) or aa_map_id.get(short_id)
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# Si toujours pas de match, on ne fait PAS de recherche par sous-chaîne floue
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# pour éviter de polluer les données. On ne match que si le nom est très proche.
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if not aa_info:
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for name, info in aa_map_exact.items():
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if name in m_name and len(name) > 0.8 * len(m_name):
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aa_info = info
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break
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pricing = m.get('pricing', {})
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try:
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price_in = float(pricing.get('prompt', 0)) * 1000000
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price_out = float(pricing.get('completion', 0)) * 1000000
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except:
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price_in, price_out = 0, 0
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score = None
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speed = None
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if aa_info:
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evals = aa_info.get('evaluations', {})
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score = evals.get('artificial_analysis_intelligence_index')
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speed = aa_info.get('median_output_tokens_per_second')
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category = "General"
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m_lower = (m_id + m_name).lower()
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if any(x in m_lower for x in ['coding', 'code', 'starcoder', 'coder']):
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category = "Coding"
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elif any(x in m_lower for x in ['agent', 'hermes', 'tool', 'function']):
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category = "Agents"
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enriched_models.append({
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'name': m.get('name', m_id),
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'price_in': price_in,
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'price_out': price_out,
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'score': score,
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'speed': speed,
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'category': category
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})
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# Filtrer les modèles inutiles (prix nul et pas de score)
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final_list = [m for m in enriched_models if m['price_in'] > 0 or m['score'] is not None]
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with open("README.md", "w", encoding="utf-8") as f:
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f.write(generate_markdown(final_list))
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print(f"Done! {len(final_list)} models processed.")
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if __name__ == "__main__":
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main()
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