1. Correzione del modello base del Team: inizializzato con qwen3:latest

2. Modifica dell'interfaccia e inserimento di un ChatManager per gestire interazione, salvataggio e caricamento della chat.

src/app.py

@@ -1,47 +1,82 @@
 import gradio as gr
-
-from dotenv import load_dotenv
-from app.pipeline import Pipeline
 from agno.utils.log import log_info
+from dotenv import load_dotenv
+
+from app.chat_manager import ChatManager
 
 ########################################
-# MAIN APP & GRADIO INTERFACE
+# MAIN APP & GRADIO CHAT INTERFACE
 ########################################
 if __name__ == "__main__":
-    ######################################
-    # DA FARE PRIMA DI ESEGUIRE L'APP
-    # qui carichiamo le variabili d'ambiente dal file .env
-    # una volta fatto, possiamo usare le API keys senza problemi
-    # quindi non è necessario richiamare load_dotenv() altrove
+    # Carica variabili d’ambiente (.env)
     load_dotenv()
-    ######################################
 
-    pipeline = Pipeline()
+    # Inizializza ChatManager
+    chat = ChatManager()
 
+    ########################################
+    # Funzioni Gradio
+    ########################################
+    def respond(message, history):
+        response = chat.send_message(message)
+        history.append({"role": "user", "content": message})
+        history.append({"role": "assistant", "content": response})
+        return history, history
+
+    def save_current_chat():
+        chat.save_chat("chat.json")
+        return "💾 Chat salvata in chat.json"
+
+    def load_previous_chat():
+        chat.load_chat("chat.json")
+        history = []
+        for m in chat.get_history():
+            history.append({"role": m["role"], "content": m["content"]})
+        return history, history
+
+    def reset_chat():
+        chat.reset_chat()
+        return [], []
+
+    ########################################
+    # Interfaccia Gradio
+    ########################################
     with gr.Blocks() as demo:
-        gr.Markdown("# 🤖 Agente di Analisi e Consulenza Crypto")
+        gr.Markdown("# 🤖 Agente di Analisi e Consulenza Crypto (Chat)")
 
+        # Dropdown provider e stile
         with gr.Row():
             provider = gr.Dropdown(
-                choices=pipeline.list_providers(),
+                choices=chat.list_providers(),
                 type="index",
                 label="Modello da usare"
             )
-            provider.change(fn=pipeline.choose_provider, inputs=provider, outputs=None)
+            provider.change(fn=chat.choose_provider, inputs=provider, outputs=None)
 
             style = gr.Dropdown(
-                choices=pipeline.list_styles(),
+                choices=chat.list_styles(),
                 type="index",
                 label="Stile di investimento"
             )
-            style.change(fn=pipeline.choose_style, inputs=style, outputs=None)
+            style.change(fn=chat.choose_style, inputs=style, outputs=None)
 
-        user_input = gr.Textbox(label="Richiesta utente")
-        output = gr.Textbox(label="Risultato analisi", lines=12)
+        chatbot = gr.Chatbot(label="Conversazione", height=500, type="messages")
+        msg = gr.Textbox(label="Scrivi la tua richiesta", placeholder="Es: Quali sono le crypto interessanti oggi?")
 
-        analyze_btn = gr.Button("🔎 Analizza")
-        analyze_btn.click(fn=pipeline.interact, inputs=[user_input], outputs=output)
+        with gr.Row():
+            clear_btn = gr.Button("🗑️ Reset Chat")
+            save_btn = gr.Button("💾 Salva Chat")
+            load_btn = gr.Button("📂 Carica Chat")
 
-    server, port = ("0.0.0.0", 8000)
-    log_info(f"Starting UPO AppAI on http://{server}:{port}")
+        # Invio messaggio
+        msg.submit(respond, inputs=[msg, chatbot], outputs=[chatbot, chatbot])
+        # Reset
+        clear_btn.click(reset_chat, inputs=None, outputs=[chatbot, chatbot])
+        # Salvataggio
+        save_btn.click(save_current_chat, inputs=None, outputs=None)
+        # Caricamento
+        load_btn.click(load_previous_chat, inputs=None, outputs=[chatbot, chatbot])
+
+    server, port = ("127.0.0.1", 8000)
+    log_info(f"Starting UPO AppAI Chat on http://{server}:{port}")
     demo.launch(server_name=server, server_port=port, quiet=True)