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v0 : Script de call vers les APIs de Mistral, GPT-4, Llama, Qwen, Deepseek et Claude. Choix de quelles APIs appeler (une, plusieurs ou toutes), passage d'un prompt, export des réponses au format Txt (concaténé si plusieurs API appelées).

.env_sample

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+REQUESTS_CA_BUNDLE=C:\ProgramData\Netskope\STAgent\data\nscacert.pem
+SSL_CERT_FILE=C:\ProgramData\Netskope\STAgent\data\nscacert.pem
+
+MISTRAL_API_KEY_static=
+LLAMA_API_KEY_static=
+ANTHROPIC_API_KEY_static=
+OPENAI_API_KEY_static=

Tests_API_GenAI.py

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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'")
+

requirements.txt

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-streamlit==1.10.0
-openai==0.27.0
-transformers==4.30.0
-tensorflow==2.12.0 
-torch==2.1.0
+annotated-types==0.7.0
+anyio==4.8.0
+certifi==2025.1.31
+charset-normalizer==3.4.1
+colorama==0.4.6
+distro==1.9.0
+eval_type_backport==0.2.2
+exceptiongroup==1.2.2
+filelock==3.17.0
+fsspec==2025.2.0
+h11==0.14.0
+httpcore==1.0.7
+httpx==0.28.1
+huggingface-hub==0.29.1
+idna==3.10
+jiter==0.8.2
+jsonpath-python==1.0.6
+mistralai==1.5.0
+mypy-extensions==1.0.0
+numpy==1.26.4
+openai==1.64.0
+packaging==24.2
+pandas==2.2.3
+pydantic==2.10.6
+pydantic_core==2.27.2
+python-dateutil==2.9.0.post0
+python-dotenv==1.0.1
+pytz==2025.1
+PyYAML==6.0.2
+regex==2024.11.6
+requests==2.32.3
+safetensors==0.5.2
+scipy==1.12.0
+six==1.17.0
+sniffio==1.3.1
+tokenizers==0.21.0
+tqdm==4.67.1
+transformers==4.49.0
+typing-inspect==0.9.0
+typing_extensions==4.12.2
+tzdata==2025.1
+urllib3==2.3.0