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# -*- 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.client import MistralClient
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')
# 📌 Choix des modèles à utiliser
USE_MODELS = {
"mistral": False,
"gpt-4": True,
"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"]:
mistral_client = MistralClient(api_key=MISTRAL_API_KEY)
messages = [{"role": "user", "content": question}]
response = mistral_client.chat(model="mistral-medium", messages=messages)
text_response = response.choices[0].message.content
responses["mistral"] = {"response": text_response, "entropy": calculate_entropy(text_response)}
# 🔹 GPT-4 (OpenAI)
if USE_MODELS["gpt-4"]:
# openai>=1.0.0
client = openai.OpenAI(api_key=OPENAI_API_KEY)
response = client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": question}]
)
text_response = response.choices[0].message.content
responses["gpt-4"] = {"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)
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["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)
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["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'")
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