Upload 2 files

app.py

@@ -60,7 +60,13 @@ def download_model(repo_id, filename, save_path):
 
 # Download the model if it doesn't exist
 if not os.path.exists(model_path):
-    download_model("PurpleAILAB/Llama3.2-3B-uncensored-SQLi-Q4_K_M-GGUF", filename, model_path)
+    download_model("adeptusnull/llama3.2-1b-wizardml-vicuna-uncensored-finetune-test", filename, model_path)
+
+# Function to truncate context to fit within the model's context window
+def truncate_context(context, max_tokens):
+    words = context.split()
+    truncated_context = ' '.join(words[-max_tokens:])
+    return truncated_context
 
 def respond(
     message,
@@ -80,14 +86,24 @@ def respond(
     # Load the model with the maximum context length and control the maximum tokens in the response
     llm = Llama(
         model_path=model_path,
-        n_ctx=5000,  # Set the maximum context length
+        n_ctx=500,  # Set the maximum context length
         max_tokens=512  # Control the maximum number of tokens generated in the response
     )
 
     agent = Agent(llm, db_path, system_prompt)
     user_id = str(uuid.uuid4())  # Generate a unique user ID for each session
 
-    response = agent.process_query(user_id, message)
+    try:
+        # Truncate the context to fit within the model's context window
+        max_context_tokens = 500  # Adjust this based on your model's context window
+        context = f"{system_prompt}\nUser: {message}\nAssistant: "
+        truncated_context = truncate_context(context, max_context_tokens)
+
+        response = agent.process_query(user_id, message, truncated_context)
+    except ValueError as e:
+        logging.error(f"Error during processing: {e}")
+        response = "Désolé, il y a eu une erreur lors du traitement de votre requête. Veuillez essayer à nouveau."
+
     return response
 
 """

main.py

@@ -0,0 +1,78 @@
+# src/main.py
+from src.agent import Agent
+from src.create_database import load_and_process_dataset  # Import from create_database.py
+import os
+import uuid
+import requests
+import logging
+from llama_cpp import Llama
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+
+# Create the directory if it doesn't exist
+local_dir = "models"
+os.makedirs(local_dir, exist_ok=True)
+
+# Specify the filename for the model
+filename = "unsloth.Q4_K_M.gguf"
+model_path = os.path.join(local_dir, filename)
+
+# Function to download the model file
+def download_model(repo_id, filename, save_path):
+    # Construct the URL for the model file
+    url = f"https://huggingface.co/{repo_id}/resolve/main/{filename}"
+
+    # Download the model file
+    response = requests.get(url)
+    if response.status_code == 200:
+        with open(save_path, 'wb') as f:
+            f.write(response.content)
+        print(f"Model downloaded to {save_path}")
+    else:
+        print(f"Failed to download model: {response.status_code}")
+
+# Download the model if it doesn't exist
+if not os.path.exists(model_path):
+    download_model("adeptusnull/llama3.2-1b-wizardml-vicuna-uncensored-finetune-test", filename, model_path)
+
+def main():
+    model_path = "models/unsloth.Q4_K_M.gguf"  # Path to the downloaded model
+    db_path = "agent.db"
+    system_prompt = "Vous êtes l'assistant intelligent de Les Chronique MTC. Votre rôle est d'aider les visiteurs en expliquant le contenu des Chroniques, Flash Infos et Chronique-FAQ de Michel Thomas. Utilisez le contexte fourni pour améliorer vos réponses et veillez à ce qu'elles soient précises et pertinentes."
+    max_tokens = 512
+    temperature = 0.7
+    top_p = 0.95
+
+    # Check if the database exists, if not, initialize it
+    if not os.path.exists(db_path):
+        data_update_path = "data-update.txt"
+        keyword_dir = "keyword"  # Updated keyword directory
+        load_and_process_dataset(data_update_path, keyword_dir, db_path)
+
+    # Load the model
+    llm = Llama(
+        model_path=model_path,
+        n_ctx=5072,  # Set the maximum context length
+        max_tokens=max_tokens  # Control the maximum number of tokens generated in the response
+    )
+
+    agent = Agent(llm, db_path, system_prompt, max_tokens, temperature, top_p)
+
+    while True:
+        user_id = str(uuid.uuid4())  # Generate a unique user ID for each session
+        user_query = input("Entrez votre requête: ")
+        if user_query.lower() == 'exit':
+            break
+
+        try:
+            response = agent.process_query(user_id, user_query)
+            print("Réponse:", response)
+        except Exception as e:
+            print(f"Erreur lors du traitement de la requête: {e}")
+
+        # Clean up expired interactions
+        agent.memory.cleanup_expired_interactions()
+
+if __name__ == "__main__":
+    main()