llm 3

app/llm_handling.py

@@ -18,17 +18,35 @@ from app.configs.prompts import SYSTEM_PROMPTS
 logging.basicConfig(level=logging.INFO)
 
 class LLMType(Enum):
-    OPENAI = "openai"
-    LOCAL = "local"
-
-# Client OpenAI standard
-openai_client = OpenAI(api_key=OPENAI_API_KEY)
-
-# Client LM Studio locale
-local_client = OpenAI(
-    base_url="http://192.168.140.5:1234/v1",
-    api_key="not-needed"
-)
+    OPENAI_GPT_4O_MINI = "openai - GPT-4o-mini"
+    LOCAL_QWEN = "local - Qwen 7B"
+    LOCAL_PHI = "local - Phi-3 Mini"
+
+# Configurazione modelli
+LLM_CONFIGS = {
+    LLMType.OPENAI_GPT_4O_MINI: {
+        "client": lambda: OpenAI(api_key=OPENAI_API_KEY),
+        "model": "gpt-4-mini",
+        "base_url": None
+    },
+    LLMType.LOCAL_QWEN: {
+        "client": lambda: OpenAI(base_url="http://192.168.140.5:1234/v1", api_key="not-needed"),
+        "model": "qwen2.5-coder-7b-instruct",
+        "base_url": "http://192.168.140.5:1234/v1"
+    },
+    LLMType.LOCAL_PHI: {
+        "client": lambda: OpenAI(base_url="http://192.168.140.5:1234/v1", api_key="not-needed"),
+        "model": "phi-3.5-mini-ita",
+        "base_url": "http://192.168.140.5:1234/v1"
+    }
+}
+
+def get_llm_client(llm_type: LLMType):
+    """Ottiene il client appropriato per il modello selezionato"""
+    config = LLM_CONFIGS.get(llm_type)
+    if not config:
+        raise ValueError(f"Modello {llm_type} non supportato")
+    return config["client"](), config["model"]
 
 # Voci italiane edge-tts
 VOICE_USER = "it-IT-DiegoNeural"      # Voce maschile utente
@@ -112,7 +130,7 @@ def get_system_prompt(prompt_type="tutor"):
     """Seleziona il prompt di sistema appropriato"""
     return SYSTEM_PROMPTS.get(prompt_type, SYSTEM_PROMPTS["tutor"])
 
-def answer_question(question, db_name, prompt_type="tutor", chat_history=None, llm_type=LLMType.OPENAI):
+def answer_question(question, db_name, prompt_type="tutor", chat_history=None, llm_type=LLMType.OPENAI_GPT_4O_MINI):
     """
     Risponde alla domanda 'question' usando i documenti del database 'db_name'.
     Restituisce una lista di 2 messaggi in formato:
@@ -172,22 +190,14 @@ def answer_question(question, db_name, prompt_type="tutor", chat_history=None, l
             {"role": "user", "content": question}
         ]
         
-        if llm_type == LLMType.OPENAI:
-            response = openai_client.chat.completions.create(
-                model="gpt-4o-mini",
-                messages=messages,
-                temperature=0.7,
-                max_tokens=2048  # Aumenta token per gestire conversazioni lunghe
-            )
-            answer = response.choices[0].message.content
-            
-        else:  # LOCAL
-            response = local_client.chat.completions.create(
-                model="qwen2.5-coder-7b-instruct",
-                messages=messages,
-                temperature=0.7
-            )
-            answer = response.choices[0].message.content
+        client, model = get_llm_client(llm_type)
+        response = client.chat.completions.create(
+            model=model,
+            messages=messages,
+            temperature=0.7,
+            max_tokens=2048  # Aumenta token per gestire conversazioni lunghe
+        )
+        answer = response.choices[0].message.content
 
         # Genera audio per domanda e risposta
         user_audio = generate_speech(question, is_user=True)

ui/chatbot_tab.py

@@ -9,6 +9,9 @@ from utils.helpers import extract_text_from_files
 
 logging.basicConfig(level=logging.INFO)
 
+
+
+
 def create_chatbot_tab():
     """Crea il tab 'Chatbot' dell'interfaccia Gradio."""
     
@@ -27,24 +30,26 @@ def create_chatbot_tab():
         return chat_history
 
     def respond(message, chat_history, db_name, prompt_type, llm_type):
-        """Genera una risposta alla domanda dell'utente e aggiorna la chat."""
         if chat_history is None:
             chat_history = []
 
-        # Converti stringa in enum
-        selected_llm = LLMType.LOCAL if llm_type == "local" else LLMType.OPENAI
+        # Mappatura dei modelli
+        llm_mapping = {
+            "openai - GPT-4o-Mini": LLMType.OPENAI_GPT_4O_MINI,
+            "local - Qwen 7B": LLMType.LOCAL_QWEN,
+            "local - Phi-3 Mini": LLMType.LOCAL_PHI
+        }
+        
+        selected_llm = llm_mapping.get(llm_type, LLMType.OPENAI_GPT_4O_MINI)
         
-        # Ottieni risposta con audio
         messages = answer_question(
             message, 
             db_name, 
-            prompt_type, 
-            chat_history=chat_history,  # Passa la cronologia
+            prompt_type=prompt_type.split(" - ")[0],
             llm_type=selected_llm
         )
         
         chat_history.extend(messages)
-
         return "", chat_history
 
     def clear_chat():
@@ -151,23 +156,27 @@ def create_chatbot_tab():
         with gr.Row():
             with gr.Column(scale=1):
                 db_name_chat = gr.Dropdown(
-                    choices=databases,
-                    label="Seleziona conoscenze",
-                    value="default_db"
+                    choices=list_databases(),  # Lista dinamica dei database
+                    label="Seleziona Database",
+                    value=list_databases()[0] if list_databases() else None
                 )
             
             with gr.Column(scale=1):
                 prompt_selector = gr.Dropdown(
-                    choices=list(SYSTEM_PROMPTS.keys()),
-                    label="Seleziona istruzioni LLM",
+                    choices=list(SYSTEM_PROMPTS.keys()),  # Usa le chiavi da SYSTEM_PROMPTS
+                    label="Seleziona Stile Risposta",
                     value="tutor"
                 )
             
             with gr.Column(scale=1):
                 llm_selector = gr.Dropdown(
-                    choices=["openai", "local"],
+                    choices=[
+                        "openai - GPT-4o-Mini",
+                        "local - Qwen 7B",
+                        "local - Phi-3 Mini"
+                    ],
                     label="Seleziona Modello",
-                    value="openai"
+                    value="openai - GPT-4o-Mini"
                 )
         
         # Chatbot e input
@@ -181,33 +190,28 @@ def create_chatbot_tab():
         # Bottoni per azioni
         with gr.Row():
             ask_button = gr.Button("Invia")
-            clear_button = gr.Button("Pulisci Chat") 
-       
-        # Upload file con dimensioni ridotte
+            upload_button = gr.Button("Carica Documenti")
+            download_button = gr.Button("💾 Scarica Conversazione")
+            clear_button = gr.Button("Pulisci Chat")       
+            
+        # box
         with gr.Row():
             file_input = gr.File(
                 label="Carica PDF/Docx/TXT per la conversazione", 
                 file_types=[".pdf", ".docx", ".txt"], 
                 file_count="multiple",
-                height="100px",
-                scale=3
-            )
-            upload_button = gr.Button("Carica Documenti", scale=1)
+                height="10px"
+                          )
+            download_file = gr.File(
+                label="Download Conversazione",
+                visible=True,
+                interactive=False
+                )
         
         # Stato della chat
         chat_state = gr.State([])
 
- 
-        # Download e Audio in due righe separate
-        with gr.Row():
-            with gr.Column(scale=1):
-                download_button = gr.Button("💾 Scarica Conversazione")
-                download_file = gr.File(
-                    label="Download Conversazione",
-                    visible=True,
-                    interactive=False
-                )
-            
+             
         with gr.Row():
             with gr.Column(scale=1):
                 audio_button = gr.Button("🎤 Genera Audio Chat")