Update app

app.py

@@ -5,7 +5,11 @@ import os
 from dotenv import load_dotenv
 from huggingface_hub import login
 from transformers import BitsAndBytesConfig
-from functools import lru_cache
+import logging
+
+# Configuration du logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
 
 load_dotenv()
 
@@ -16,38 +20,56 @@ login(hf_token)
 # Configuration du modèle
 model_path = "mistralai/Mistral-Large-Instruct-2411"
 
+# Détermination automatique du dtype optimal
+dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] == 8 else torch.float16
+logger.info(f"Using dtype: {dtype}")
+
 # Configuration de la quantification 4-bits
+logger.info("Configuring 4-bit quantization")
 quantization_config = BitsAndBytesConfig(
     load_in_4bit=True,
-    bnb_4bit_compute_dtype=torch.float16,
+    bnb_4bit_compute_dtype=dtype,  # Utilisation du dtype optimal
     bnb_4bit_quant_type="nf4",
     bnb_4bit_use_double_quant=True
 )
 
-# Initialisation du modèle avec quantification
+# Initialisation du modèle
+logger.info(f"Loading tokenizer from {model_path}")
 tokenizer = AutoTokenizer.from_pretrained(model_path)
+logger.info("Tokenizer loaded successfully")
+
+logger.info(f"Loading model from {model_path} with 4-bit quantization")
 model = AutoModelForCausalLM.from_pretrained(
     model_path,
     device_map="auto",
     quantization_config=quantization_config
 )
+logger.info("Model loaded successfully")
+
+logger.info("Creating inference pipeline")
 pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
+logger.info("Inference pipeline created successfully")
 
 def generate_response(message, temperature=0.7, max_new_tokens=256):
     try:
-        response = pipe(
-            message,
-            temperature=temperature,
-            max_new_tokens=max_new_tokens,
-            do_sample=True,
-            top_k=50,
-            top_p=0.9,
-            pad_token_id=tokenizer.pad_token_id,
-            eos_token_id=tokenizer.eos_token_id,
-            batch_size=1
-        )
+        logger.info(f"Generating response for message: {message[:50]}...")
+        parameters = {
+            "temperature": temperature,
+            "max_new_tokens": max_new_tokens,
+            # "do_sample": True,
+            # "top_k": 50,
+            # "top_p": 0.9,
+            # "pad_token_id": tokenizer.pad_token_id,
+            # "eos_token_id": tokenizer.eos_token_id,
+            # "batch_size": 1
+        }
+        logger.info(f"Parameters: {parameters}")
+        
+        response = pipe(message, **parameters)
+        logger.info("Response generated successfully")
         return response[0]['generated_text']
     except Exception as e:
+        logger.error(f"Error during generation: {str(e)}")
         return f"Une erreur s'est produite : {str(e)}"
 
 # Interface Gradio
@@ -64,4 +86,5 @@ demo = gr.Interface(
 )
 
 if __name__ == "__main__":
+    logger.info("Starting Gradio interface")
     demo.launch(share=True)