import requests
import json
import os
from datetime import datetime
from typing import Dict, Any, Optional

# Configuration
OLLAMA_URL = "http://217.182.105.173:11434"
MODEL_NAME = "llama3.2-vision:90b-instruct-q8_0"

def get_ollama_info() -> Optional[Dict[str, Any]]:
    """
    Récupère toutes les informations disponibles sur le modèle depuis Ollama.
    
    Returns:
        Dict contenant les informations ou None si erreur
    """
    try:
        response = requests.post(
            f"{OLLAMA_URL}/api/show",
            json={"name": MODEL_NAME},
            headers={"Content-Type": "application/json"}
        )
        
        if response.status_code == 200:
            return response.json()
        else:
            print(f"❌ Erreur {response.status_code}: {response.text}")
            return None
            
    except requests.exceptions.RequestException as e:
        print(f"❌ Erreur de connexion: {str(e)}")
        return None

def save_raw_response(data: Dict[str, Any]) -> str:
    """
    Sauvegarde la réponse brute dans un fichier JSON.
    
    Args:
        data: Données à sauvegarder
        
    Returns:
        Nom du fichier créé
    """
    os.makedirs('settings', exist_ok=True)
    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
    filename = f"settings/ollama_raw_{timestamp}.json"
    
    with open(filename, 'w', encoding='utf-8') as f:
        json.dump(data, f, indent=2, ensure_ascii=False)
    
    print(f"✅ Réponse brute sauvegardée dans: {filename}")
    return filename

def extract_model_parameters(data: Dict[str, Any]) -> Dict[str, Any]:
    """
    Extrait et analyse les paramètres du modèle.
    
    Args:
        data: Données brutes d'Ollama
        
    Returns:
        Dict contenant les paramètres organisés
    """
    model_info = {
        "basic_info": {
            "name": MODEL_NAME,
            "family": data.get("details", {}).get("family", ""),
            "parameter_size": data.get("details", {}).get("parameter_size", ""),
            "quantization": data.get("details", {}).get("quantization_level", ""),
            "modified_at": data.get("modified_at", "")
        },
        "technical_info": {
            "architecture": data.get("model_info", {}).get("general.architecture", ""),
            "context_length": data.get("model_info", {}).get("mllama.context_length", 0),
            "vocab_size": data.get("model_info", {}).get("mllama.vocab_size", 0),
            "embedding_length": data.get("model_info", {}).get("mllama.embedding_length", 0)
        },
        "vision_info": {
            "image_size": data.get("projector_info", {}).get("mllama.vision.image_size", 0),
            "patch_size": data.get("projector_info", {}).get("mllama.vision.patch_size", 0),
            "max_num_tiles": data.get("projector_info", {}).get("mllama.vision.max_num_tiles", 0)
        },
        "default_parameters": {}
    }
    
    # Extraire les paramètres du modelfile
    if "modelfile" in data:
        for line in data["modelfile"].split('\n'):
            if line.startswith('PARAMETER'):
                _, key, value = line.split(None, 2)
                try:
                    model_info["default_parameters"][key] = float(value)
                except ValueError:
                    model_info["default_parameters"][key] = value.strip()
    
    # Ajouter les paramètres de la section parameters
    if "parameters" in data:
        for line in data["parameters"].split('\n'):
            if line.strip():
                key, value = line.split(None, 1)
                try:
                    model_info["default_parameters"][key] = float(value)
                except ValueError:
                    model_info["default_parameters"][key] = value.strip()
    
    return model_info

def main():
    # Récupérer les informations d'Ollama
    print(f"🔄 Récupération des informations pour le modèle {MODEL_NAME}...")
    ollama_data = get_ollama_info()
    
    if ollama_data:
        # Sauvegarder la réponse brute
        raw_file = save_raw_response(ollama_data)
        
        # Extraire et organiser les informations
        model_info = extract_model_parameters(ollama_data)
        
        # Sauvegarder les informations organisées
        info_file = raw_file.replace('raw', 'info')
        with open(info_file, 'w', encoding='utf-8') as f:
            json.dump(model_info, f, indent=2, ensure_ascii=False)
        print(f"✅ Informations organisées sauvegardées dans: {info_file}")
        
        # Afficher un résumé
        print("\n📊 Résumé des informations :")
        print(f"• Modèle: {model_info['basic_info']['name']}")
        print(f"• Famille: {model_info['basic_info']['family']}")
        print(f"• Taille: {model_info['basic_info']['parameter_size']}")
        print(f"• Quantization: {model_info['basic_info']['quantization']}")
        print("\n🔧 Paramètres par défaut:")
        for key, value in model_info['default_parameters'].items():
            print(f"• {key}: {value}")

if __name__ == "__main__":
    main()