feat: migliora gestione errori con suggerimenti contextuali

- Aggiunge config_manager a load_model_from_config() per usare config attive
- Migliora error handling con tipo eccezione e suggerimenti
- Aggiunge pyproject.toml e setup.py per installazione pip

main.py

@@ -54,18 +54,49 @@ import json
 def load_model_from_config():
     """
     Load the LLM model based on configuration.
-    Configuration can come from environment variables or a config file.
-    For simplicity, we'll use environment variables.
+    Priority: 1) config_manager active config, 2) environment variables
     
-    Expected environment variables:
+    Expected environment variables (fallback):
     - OPENAI_API_KEY: For OpenAI models
     - OLLAMA_BASE_URL: For Ollama (e.g., https://ollama.solarpi.net)
     - MODEL_PROVIDER: One of "openai", "ollama", "openrouter", etc.
     - MODEL_NAME: The specific model name (e.g., "gpt-4o", "gemma2:9b")
     """
+    # Try to use config_manager first
+    if CONFIG_AVAILABLE:
+        active_name = config_manager.get_active_configuration()
+        if active_name:
+            config = config_manager.get_configuration(active_name)
+            if config:
+                provider = config.get("provider", "ollama").lower()
+                model_name = config.get("model_name", "gemma2:9b")
+                base_url = config.get("base_url", "http://localhost:11434")
+                api_key = config.get("api_key")
+                
+                print(f"🔗 Caricamento modello da config_manager: {active_name}")
+                print(f"   Provider: {provider}")
+                print(f"   Modello: {model_name}")
+                print(f"   URL: {base_url}")
+                
+                if provider == "ollama":
+                    return init_chat_model(f"ollama:{model_name}", base_url=base_url)
+                elif provider == "openai":
+                    return init_chat_model(f"openai:{model_name}", api_key=api_key)
+                elif provider == "openrouter":
+                    return init_chat_model(
+                        f"openai:{model_name}", 
+                        base_url=base_url, 
+                        api_key=api_key
+                    )
+    
+    # Fallback to environment variables
     provider = os.getenv("MODEL_PROVIDER", "openai").lower()
     model_name = os.getenv("MODEL_NAME", "gpt-4o")
     
+    print(f"⚠️  Usando variabili d'ambiente (nessuna config attiva)")
+    print(f"   Provider: {provider}")
+    print(f"   Modello: {model_name}")
+    
     # Configure based on provider
     if provider == "openai":
         # Uses OPENAI_API_KEY from environment
@@ -999,7 +1030,21 @@ def main():
                 ai_message = result["messages"][-1]
                 print(f"\n🤖 LangCode: {ai_message.content}")
             except Exception as e:
-                print(f"\n❌ Error: {str(e)}")
+                error_msg = str(e)
+                if not error_msg:
+                    error_msg = f"{type(e).__name__} (nessun messaggio)"
+                print(f"\n❌ Errore: {error_msg}")
+                
+                # Suggerimenti basati sul tipo di errore
+                exc_type = type(e).__name__
+                if "Connection" in exc_type or "Connect" in str(e):
+                    print("💡 Suggerimento: Verifica che il server Ollama sia raggiungibile.")
+                    print(f"   URL: {os.getenv('OLLAMA_BASE_URL', 'http://localhost:11434')}")
+                elif "Timeout" in exc_type or "timeout" in str(e).lower():
+                    print("💡 Suggerimento: Il server è lento. Prova con un modello più piccolo.")
+                elif "model" in str(e).lower() or "Model" in exc_type:
+                    print("💡 Suggerimento: Il modello specificato potrebbe non esistere.")
+                    print(f"   Modello: {os.getenv('MODEL_NAME', 'non impostato')}")
                 
     except Exception as e:
         print(f"❌ Failed to initialize agent: {str(e)}")