# -*- coding: utf-8 -*-
"""
Created on Mon Feb 24 12:03:11 2025

@author: MIPO10053340
"""
#JWT
from dotenv import load_dotenv
load_dotenv()

import os
import numpy as np
import pandas as pd
from scipy.stats import entropy

# API Clients
from mistralai import Mistral
from openai import OpenAI

# from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline


# ⚙️ Configurations API (remplace par tes clés API)
MISTRAL_API_KEY = os.getenv('MISTRAL_API_KEY_static')
OPENAI_API_KEY = os.getenv('OPENAI_API_KEY_static')
# ANTHROPIC_API_KEY = os.getenv('ANTHROPIC_API_KEY_static')
# LLAMA_API_KEY = os.getenv('LLAMA_API_KEY_static')
# HUGGINGFACE_TOKEN =os.getenv('HUGGINGFACE_TOKEN_static')

# 📌 Choix des modèles à utiliser
USE_MODELS = {
    "mistral": True,
    "gpt-4": False,
    "llama": False,  # Active si tu veux l'utiliser
    "qwen": False, 
    "deepseek": False
}


# 📊 Fonction pour calculer l'entropie des réponses
def calculate_entropy(text):
    tokens = text.split()
    probas = np.array([tokens.count(word) / len(tokens) for word in set(tokens)])
    return entropy(probas)

# 🚀 Fonction pour interroger les modèles
def get_model_responses(question):
    responses = {}

    # # 🔹 MISTRAL
    if USE_MODELS["mistral"]:
        # Initialisation du client Mistral
        client = Mistral(api_key=MISTRAL_API_KEY)
        
        # Créer une complétion de chat
        response = client.chat.complete(
            model="mistral-medium",
            messages=[
                {"role": "user", "content": question}
            ]
        )
        # Extraire et afficher la réponse
        text_response = response.choices[0].message.content    
        responses["mistral-medium"] = {"response": text_response, "entropy": calculate_entropy(text_response)}

    # 🔹 GPT-4 (OpenAI)
    if USE_MODELS["gpt-4"]:
        # Initialisation du client OpenAI
        client = OpenAI(api_key=OPENAI_API_KEY)
        
        # Créer une complétion de chat
        response = client.chat.completions.create(
            model="gpt-4-turbo",
            messages=[
                {"role": "user", "content": question}
            ]
        )
        # Extraire et afficher la réponse
        text_response = response.choices[0].message.content    
        responses["gpt-4-turbo"] = {"response": text_response, "entropy": calculate_entropy(text_response)}

    # # 🔹 LLAMA (Hugging Face)
    # if USE_MODELS["llama"]:
    #     model_id = "meta-llama/Llama-2-7b-chat-hf"
    #     tokenizer = AutoTokenizer.from_pretrained(model_id, use_auth_token=HUGGINGFACE_TOKEN)
    #     model = AutoModelForCausalLM.from_pretrained(model_id, use_auth_token=HUGGINGFACE_TOKEN)
    #     pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
    
    #     text_response = pipe(question, max_length=300)[0]["generated_text"]
    #     responses["llama"] = {"response": text_response, "entropy": calculate_entropy(text_response)}
    # # 🔹 QWEN (Hugging Face)
    # if USE_MODELS["qwen"]:
    #     model_id = "Qwen/Qwen-7B-Chat"
    #     tokenizer = AutoTokenizer.from_pretrained(model_id, token=HUGGINGFACE_TOKEN, trust_remote_code=True)
    #     model = AutoModelForCausalLM.from_pretrained(model_id, token=HUGGINGFACE_TOKEN, trust_remote_code=True)
    #     pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
    #     text_response = pipe(question, max_length=300)[0]["generated_text"]
    #     responses["qwen"] = {"response": text_response, "entropy": calculate_entropy(text_response)}

    # # 🔹 DEEPSEEK (Hugging Face)
    # if USE_MODELS["deepseek"]:
    #     model_id = "deepseek-ai/deepseek-7b-chat"
    #     tokenizer = AutoTokenizer.from_pretrained(model_id)
    #     model = AutoModelForCausalLM.from_pretrained(model_id)
    #     pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
    #     text_response = pipe(question, max_length=300)[0]["generated_text"]
    #     responses["deepseek"] = {"response": text_response, "entropy": calculate_entropy(text_response)}

    return responses

# 📌 Question de test
question = "Quels sont les besoins en protéines des poulets de chair en phase de croissance ?"

# 🔥 Exécuter le test
results = get_model_responses(question)

# 📊 Afficher les résultats
df = pd.DataFrame.from_dict(results, orient="index")
print(df)

# 💾 Sauvegarde en .txt
with open("model_responses.txt", "w", encoding="utf-8") as f:
    for model, data in results.items():
        f.write(f"🔹 Modèle : {model.upper()}\n")
        f.write(f"Réponse :\n{data['response']}\n")
        f.write(f"📊 Entropie : {data['entropy']:.4f}\n")
        f.write("=" * 50 + "\n\n")

print("\n✅ Réponses enregistrées dans 'model_responses.txt'")