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MAJ code Mistral AI pour pouvoir passer un dict à l'API. Ajouts des librairies pour Llama et Deepseek, mais Llama, Deepseek et Qwen non fonctionnels

Tests_API_GenAI.py

@@ -15,9 +15,14 @@ from scipy.stats import entropy
 
 # API Clients
 from mistralai.client import MistralClient
+from mistralai.models.chat_completion import ChatMessage
+
 import openai
 from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+import transformers_stream_generator
+import einops 
 
+HF_TOKEN = "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"
 
 # ⚙️ Configurations API (remplace par tes clés API)
 MISTRAL_API_KEY = os.getenv('MISTRAL_API_KEY_static')
@@ -27,10 +32,10 @@ LLAMA_API_KEY = os.getenv('LLAMA_API_KEY_static')
 
 # 📌 Choix des modèles à utiliser
 USE_MODELS = {
-    "mistral": False,
-    "gpt-4": True,
+    "mistral": True,
+    "gpt-4": False,
     "llama": False,  # Active si tu veux l'utiliser
-    "qwen": False,
+    "qwen": False, 
     "deepseek": False
 }
 
@@ -47,7 +52,7 @@ def get_model_responses(question):
     # 🔹 MISTRAL
     if USE_MODELS["mistral"]:
         mistral_client = MistralClient(api_key=MISTRAL_API_KEY)
-        messages = [{"role": "user", "content": question}]
+        messages = [ChatMessage(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)}
@@ -77,8 +82,8 @@ def get_model_responses(question):
     # 🔹 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)
+        tokenizer = AutoTokenizer.from_pretrained(model_id, token=HF_TOKEN, trust_remote_code=True)
+        model = AutoModelForCausalLM.from_pretrained(model_id, token=HF_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)}

requirements.txt

@@ -1,45 +1,44 @@
-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
-streamlit==1.10.0
-tensorflow==2.18.0
-tf-keras==2.18.0
+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
+einops==0.8.1
+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
+transformers-stream-generator==0.0.5
+typing-inspect==0.9.0
+typing_extensions==4.12.2
+tzdata==2025.1
+urllib3==2.3.0