.env.example

@@ -5,6 +5,7 @@
 # https://makersuite.google.com/app/apikey
 GOOGLE_API_KEY=
 
+
 ###############################################################################
 # Configurazioni per gli agenti di mercato
 ###############################################################################
@@ -21,6 +22,7 @@ CRYPTOCOMPARE_API_KEY=
 BINANCE_API_KEY=
 BINANCE_API_SECRET=
 
+
 ###############################################################################
 # Configurazioni per gli agenti di notizie
 ###############################################################################
@@ -31,6 +33,7 @@ NEWS_API_KEY=
 # https://cryptopanic.com/developers/api/
 CRYPTOPANIC_API_KEY=
 
+
 ###############################################################################
 # Configurazioni per API di social media
 ###############################################################################
@@ -38,3 +41,11 @@ CRYPTOPANIC_API_KEY=
 # https://www.reddit.com/prefs/apps
 REDDIT_API_CLIENT_ID=
 REDDIT_API_CLIENT_SECRET=
+
+
+###############################################################################
+# Configurazioni per API di messaggistica
+###############################################################################
+
+# https://core.telegram.org/bots/features#creating-a-new-bot
+TELEGRAM_BOT_TOKEN=

.gitignore

@@ -173,8 +173,8 @@ cython_debug/
 # PyPI configuration file
 .pypirc
 
-# chroma db
-./chroma_db/
-
 # VS Code
-.vscode/
+.vscode/
+
+# Gradio
+.gradio/

README.md

@@ -91,13 +91,22 @@ uv run src/app
 
 # **Applicazione**
 
-***L'applicazione è attualmente in fase di sviluppo.***
+> [!CAUTION]\
+> ***L'applicazione è attualmente in fase di sviluppo.***
 
-Usando la libreria ``gradio`` è stata creata un'interfaccia web semplice per interagire con l'agente principale. Gli agenti secondari si trovano nella cartella `src/app/agents` e sono:
+L'applicazione viene fatta partire tramite il file [src/app/\_\_main\_\_.py](src/app/__main__.py) che inizializza l'agente principale e gli agenti secondari.
-- **Market Agent**: Agente unificato che supporta multiple fonti di dati con auto-retry e gestione degli errori.
+
-- **News Agent**: Recupera le notizie finanziarie più recenti sul mercato delle criptovalute.
+In esso viene creato il server `gradio` per l'interfaccia web e viene anche inizializzato il bot di Telegram (se è stata inserita la chiave nel file `.env` ottenuta da [BotFather](https://core.telegram.org/bots/features#creating-a-new-bot)).
-- **Social Agent**: Analizza i sentimenti sui social media riguardo alle criptovalute.
+
-- **Predictor Agent**: Utilizza i dati raccolti dagli altri agenti per fare previsioni.
+L'interazione è guidata, sia tramite l'interfaccia web che tramite il bot di Telegram; l'utente può scegliere prima di tutto delle opzioni generali (come il modello e la strategia di investimento), dopodiché può inviare un messaggio di testo libero per chiedere consigli o informazioni specifiche. Per esempio: "Qual è l'andamento attuale di Bitcoin?" o "Consigliami quali sono le migliori criptovalute in cui investire questo mese".
+
+L'applicazione, una volta ricevuta la richiesta, la passa al [Team](src/app/agents/team.py) di agenti che si occupano di raccogliere i dati necessari per rispondere in modo completo e ragionato.
+
+Gli agenti coinvolti nel Team sono:
+- **Leader**: Coordina gli altri agenti e fornisce la risposta finale all'utente.
+- **Market Agent**: Recupera i dati di mercato attuali delle criptovalute da Binance e Yahoo Finance.
+- **News Agent**: Recupera le ultime notizie sul mercato delle criptovalute da NewsAPI e GNews.
+- **Social Agent**: Recupera i dati dai social media (Reddit) per analizzare il sentiment del mercato.
 
 ## Struttura del codice del Progetto
 

configs.yaml

@@ -17,8 +17,8 @@ models:
   gemini:
     - name: gemini-2.0-flash
       label: Gemini
-    - name: gemini-2.0-pro
+    # - name: gemini-2.0-pro # TODO Non funziona, ha un nome diverso
-      label: Gemini Pro
+    #   label: Gemini Pro
   ollama:
     - name: gpt-oss:latest
       label: Ollama GPT

demos/telegram_bot_demo.py

@@ -0,0 +1,59 @@
+import os
+from dotenv import load_dotenv
+from telegram import InlineKeyboardButton, InlineKeyboardMarkup, Update
+from telegram.ext import Application, CommandHandler, CallbackQueryHandler, MessageHandler, filters, ContextTypes
+
+# Esempio di funzione per gestire il comando /start
+async def start(update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
+    if not update.message: return
+    await update.message.reply_text('Ciao! Inviami un messaggio e ti risponderò!')
+
+
+# Esempio di funzione per fare echo del messaggio ricevuto
+async def echo(update: Update,  context: ContextTypes.DEFAULT_TYPE):
+    message = update.message
+    if not message: return
+
+    print(f"Ricevuto messaggio: {message.text} da chat id: {message.chat.id}")
+    await message.reply_text(text=f"Hai detto: {message.text}")
+
+
+# Esempio di funzione per far partire una inline keyboard (comando /keyboard)
+async def inline_keyboard(update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
+    if not update.message: return
+    keyboard = [
+        [
+            InlineKeyboardButton("Option 1", callback_data='1'),
+            InlineKeyboardButton("Option 2", callback_data='2'),
+        ]
+    ]
+    reply_markup = InlineKeyboardMarkup(keyboard)
+    await update.message.reply_text('Please choose:', reply_markup=reply_markup)
+
+
+async def button_handler(update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
+    query = update.callback_query
+    if not query: return
+    await query.answer()
+    await query.edit_message_text(text=f"Selected option: {query.data}")
+
+
+
+
+
+def main():
+    print("Bot in ascolto...")
+
+    load_dotenv()
+    token = os.getenv("TELEGRAM_BOT_TOKEN", '')
+    app = Application.builder().token(token).build()
+
+    app.add_handler(CommandHandler("start", start))
+    app.add_handler(CommandHandler("keyboard", inline_keyboard))
+    app.add_handler(MessageHandler(filters=filters.TEXT, callback=echo))
+    app.add_handler(CallbackQueryHandler(button_handler))
+
+    app.run_polling(allowed_updates=Update.ALL_TYPES)
+
+if __name__ == "__main__":
+    main()

pyproject.toml

@@ -36,6 +36,10 @@ dependencies = [
 
     # API di social media
     "praw", # Reddit
+
+    # Per telegram bot
+    "python-telegram-bot", # Interfaccia Telegram Bot
+    "markdown-pdf", # Per convertire markdown in pdf
 ]
 
 [tool.pytest.ini_options]

src/app/__main__.py

@@ -1,86 +1,31 @@
 import asyncio
-import gradio as gr
+import logging
 from dotenv import load_dotenv
-from agno.utils.log import log_info #type: ignore
 from app.configs import AppConfig
-from app.interface import ChatManager
+from app.interface import *
 from app.agents import Pipeline
 
 
 if __name__ == "__main__":
-    # Inizializzazioni
     load_dotenv()
 
     configs = AppConfig.load()
     pipeline = Pipeline(configs)
 
-    chat = ChatManager()
+    chat = ChatManager(pipeline)
-
+    gradio = chat.gradio_build_interface()
-    ########################################
+    _app, local_url, share_url = gradio.launch(server_name="0.0.0.0", server_port=configs.port, quiet=True, prevent_thread_lock=True, share=configs.gradio_share)
-    # Funzioni Gradio
+    logging.info(f"UPO AppAI Chat is running on {share_url or local_url}")
-    ########################################
-    def respond(message: str, history: list[dict[str, str]]) -> tuple[list[dict[str, str]], list[dict[str, str]], str]:
-        chat.send_message(message)
-        response = pipeline.interact(message)
-        chat.receive_message(response)
-        history.append({"role": "user", "content": message})
-        history.append({"role": "assistant", "content": response})
-        return history, history, ""
-
-    def save_current_chat() -> str:
-        chat.save_chat("chat.json")
-        return "💾 Chat salvata in chat.json"
-
-    def load_previous_chat() -> tuple[list[dict[str, str]], list[dict[str, str]]]:
-        chat.load_chat("chat.json")
-        history: list[dict[str, str]] = []
-        for m in chat.get_history():
-            history.append({"role": m["role"], "content": m["content"]})
-        return history, history
-
-    def reset_chat() -> tuple[list[dict[str, str]], list[dict[str, str]]]:
-        chat.reset_chat()
-        return [], []
-
-    ########################################
-    # Interfaccia Gradio
-    ########################################
-    with gr.Blocks() as demo:
-        gr.Markdown("# 🤖 Agente di Analisi e Consulenza Crypto (Chat)")
-
-        # Dropdown provider e stile
-        with gr.Row():
-            provider = gr.Dropdown(
-                choices=pipeline.list_providers(),
-                type="index",
-                label="Modello da usare"
-            )
-            provider.change(fn=pipeline.choose_predictor, inputs=provider, outputs=None)
-
-            style = gr.Dropdown(
-                choices=pipeline.list_styles(),
-                type="index",
-                label="Stile di investimento"
-            )
-            style.change(fn=pipeline.choose_strategy, inputs=style, outputs=None)
-
-        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?")
-
-        with gr.Row():
-            clear_btn = gr.Button("🗑️ Reset Chat")
-            save_btn = gr.Button("💾 Salva Chat")
-            load_btn = gr.Button("📂 Carica Chat")
-
-        # Eventi e interazioni
-        msg.submit(respond, inputs=[msg, chatbot], outputs=[chatbot, chatbot, msg])
-        clear_btn.click(reset_chat, inputs=None, outputs=[chatbot, chatbot])
-        save_btn.click(save_current_chat, inputs=None, outputs=None)
-        load_btn.click(load_previous_chat, inputs=None, outputs=[chatbot, chatbot])
 
     try:
-        _app, local, shared = demo.launch(server_name="0.0.0.0", server_port=configs.port, quiet=True, prevent_thread_lock=True, share=configs.gradio_share)
+        telegram = TelegramApp(pipeline)
-        log_info(f"Starting UPO AppAI Chat on {shared or local}")
+        telegram.add_miniapp_url(share_url)
-        asyncio.get_event_loop().run_forever()
+        telegram.run()
-    except KeyboardInterrupt:
+    except AssertionError as e:
-        demo.close()
+        try:
+            logging.warning(f"Telegram bot could not be started: {e}")
+            asyncio.get_event_loop().run_forever()
+        except KeyboardInterrupt:
+            logging.info("Shutting down due to KeyboardInterrupt")
+    finally:
+        gradio.close()

src/app/agents/pipeline.py

@@ -1,10 +1,10 @@
-from agno.run.agent import RunOutput
+import logging
 from app.agents.team import create_team_with
-from app.agents.predictor import PredictorInput, PredictorOutput
 from app.agents.prompts import *
-from app.api.core.markets import ProductInfo
 from app.configs import AppConfig
 
+logging = logging.getLogger("pipeline")
+
 
 class Pipeline:
     """
@@ -17,27 +17,30 @@ class Pipeline:
         self.configs = configs
 
         # Stato iniziale
-        self.choose_strategy(0)
+        self.leader_model = self.configs.get_model_by_name(self.configs.agents.team_leader_model)
-        self.choose_predictor(0)
+        self.team_model = self.configs.get_model_by_name(self.configs.agents.team_model)
+        self.strategy = self.configs.get_strategy_by_name(self.configs.agents.strategy)
 
     # ======================
     # Dropdown handlers
     # ======================
-    def choose_predictor(self, index: int):
+    def choose_leader(self, index: int):
         """
-        Sceglie il modello LLM da usare per il Predictor.
+        Sceglie il modello LLM da usare per il Team.
         """
-        model = self.configs.models.all_models[index]
+        self.leader_model = self.configs.models.all_models[index]
-        self.predictor = model.get_agent(
+
-            PREDICTOR_INSTRUCTIONS,
+    def choose_team(self, index: int):
-            output_schema=PredictorOutput,
+        """
-        )
+        Sceglie il modello LLM da usare per il Team.
+        """
+        self.team_model = self.configs.models.all_models[index]
 
     def choose_strategy(self, index: int):
         """
         Sceglie la strategia da usare per il Predictor.
         """
-        self.strat = self.configs.strategies[index].description
+        self.strategy = self.configs.strategies[index]
 
     # ======================
     # Helpers
@@ -64,46 +67,18 @@ class Pipeline:
         3. Invoca Predictor
         4. Restituisce la strategia finale
         """
-        # Step 1: raccolta output dai membri del Team
+        # Step 1: Creazione Team
-        team_model = self.configs.get_model_by_name(self.configs.agents.team_model)
+        team = create_team_with(self.configs, self.team_model, self.leader_model)
-        leader_model = self.configs.get_model_by_name(self.configs.agents.team_leader_model)
 
-        team = create_team_with(self.configs, team_model, leader_model)
+        # Step 2: raccolta output dai membri del Team
+        logging.info(f"Pipeline received query: {query}")
+        # TODO migliorare prompt (?)
+        query = f"The user query is: {query}\n\n They requested a {self.strategy.label} investment strategy."
         team_outputs = team.run(query) # type: ignore
 
-        # Step 2: aggregazione output strutturati
+        # Step 3: recupero ouput
-        all_products: list[ProductInfo] = []
+        if not isinstance(team_outputs.content, str):
-        sentiments: list[str] = []
+            logging.error(f"Team output is not a string: {team_outputs.content}")
-
+            raise ValueError("Team output is not a string")
-        for agent_output in team_outputs.member_responses:
+        logging.info(f"Team finished")
-            if isinstance(agent_output, RunOutput) and agent_output.metadata is not None:
+        return team_outputs.content
-                keys = agent_output.metadata.keys()
-                if "products" in keys:
-                    all_products.extend(agent_output.metadata["products"])
-                if "sentiment_news" in keys:
-                    sentiments.append(agent_output.metadata["sentiment_news"])
-                if "sentiment_social" in keys:
-                    sentiments.append(agent_output.metadata["sentiment_social"])
-
-        aggregated_sentiment = "\n".join(sentiments)
-
-        # Step 3: invocazione Predictor
-        predictor_input = PredictorInput(
-            data=all_products,
-            style=self.strat,
-            sentiment=aggregated_sentiment
-        )
-
-        result = self.predictor.run(predictor_input) # type: ignore
-        if not isinstance(result.content, PredictorOutput):
-            return "❌ Errore: il modello non ha restituito un output valido."
-        prediction: PredictorOutput = result.content
-
-        # Step 4: restituzione strategia finale
-        portfolio_lines = "\n".join(
-            [f"{item.asset} ({item.percentage}%): {item.motivation}" for item in prediction.portfolio]
-        )
-        return (
-            f"📊 Strategia ({self.strat}): {prediction.strategy}\n\n"
-            f"💼 Portafoglio consigliato:\n{portfolio_lines}"
-        )

src/app/api/social/reddit.py

@@ -59,6 +59,7 @@ class RedditWrapper(SocialWrapper):
             client_id=client_id,
             client_secret=client_secret,
             user_agent="upo-appAI",
+            check_for_async=False,
         )
         self.subreddits = self.tool.subreddit("+".join(SUBREDDITS))
 

src/app/api/wrapper_handler.py

@@ -1,9 +1,10 @@
 import inspect
+import logging
 import time
 import traceback
 from typing import Any, Callable, Generic, TypeVar
-from agno.utils.log import log_info, log_warning #type: ignore
 
+logging = logging.getLogger("wrapper_handler")
 WrapperType = TypeVar("WrapperType")
 WrapperClassType = TypeVar("WrapperClassType")
 OutputType = TypeVar("OutputType")
@@ -86,7 +87,7 @@ class WrapperHandler(Generic[WrapperType]):
             Exception: If all wrappers fail after retries.
         """
 
-        log_info(f"{inspect.getsource(func).strip()} {inspect.getclosurevars(func).nonlocals}")
+        logging.info(f"{inspect.getsource(func).strip()} {inspect.getclosurevars(func).nonlocals}")
         results: dict[str, OutputType] = {}
         starting_index = self.index
 
@@ -96,18 +97,18 @@ class WrapperHandler(Generic[WrapperType]):
             wrapper_name = wrapper.__class__.__name__
 
             if not try_all:
-                log_info(f"try_call {wrapper_name}")
+                logging.info(f"try_call {wrapper_name}")
 
             for try_count in range(1, self.retry_per_wrapper + 1):
                 try:
                     result = func(wrapper)
-                    log_info(f"{wrapper_name} succeeded")
+                    logging.info(f"{wrapper_name} succeeded")
                     results[wrapper_name] = result
                     break
 
                 except Exception as e:
                     error = WrapperHandler.__concise_error(e)
-                    log_warning(f"{wrapper_name} failed {try_count}/{self.retry_per_wrapper}: {error}")
+                    logging.warning(f"{wrapper_name} failed {try_count}/{self.retry_per_wrapper}: {error}")
                     time.sleep(self.retry_delay)
 
             if not try_all and results:
@@ -153,6 +154,6 @@ class WrapperHandler(Generic[WrapperType]):
                 wrapper = wrapper_class(**(kwargs or {}))
                 result.append(wrapper)
             except Exception as e:
-                log_warning(f"{wrapper_class} cannot be initialized: {e}")
+                logging.warning(f"'{wrapper_class.__name__}' cannot be initialized: {e}")
 
         return WrapperHandler(result, try_per_wrapper, retry_delay)

src/app/configs.py

@@ -145,6 +145,17 @@ class AppConfig(BaseModel):
                 return strat
         raise ValueError(f"Strategy with name '{name}' not found.")
 
+    def get_defaults(self) -> tuple[AppModel, AppModel, Strategy]:
+        """
+        Retrieve the default team model, leader model, and strategy.
+        Returns:
+            A tuple containing the default team model (AppModel), leader model (AppModel), and strategy (Strategy).
+        """
+        team_model = self.get_model_by_name(self.agents.team_model)
+        leader_model = self.get_model_by_name(self.agents.team_leader_model)
+        strategy = self.get_strategy_by_name(self.agents.strategy)
+        return team_model, leader_model, strategy
+
     def set_logging_level(self) -> None:
         """
         Set the logging level based on the configuration.

src/app/interface/__init__.py

@@ -1,3 +1,4 @@
 from app.interface.chat import ChatManager
+from app.interface.telegram_app import TelegramApp
 
-__all__ = ["ChatManager"]
+__all__ = ["ChatManager", "TelegramApp"]

src/app/interface/chat.py

@@ -1,5 +1,8 @@
-import json
 import os
+import json
+import gradio as gr
+from app.agents.pipeline import Pipeline
+
 
 class ChatManager:
     """
@@ -9,8 +12,9 @@ class ChatManager:
     - salva e ricarica le chat
     """
 
-    def __init__(self):
+    def __init__(self, pipeline: Pipeline):
         self.history: list[dict[str, str]] = []  # [{"role": "user"/"assistant", "content": "..."}]
+        self.pipeline = pipeline
 
     def send_message(self, message: str) -> None:
         """
@@ -56,3 +60,66 @@ class ChatManager:
         Restituisce lo storico completo della chat.
         """
         return self.history
+
+
+    ########################################
+    # Funzioni Gradio
+    ########################################
+    def gradio_respond(self, message: str, history: list[dict[str, str]]) -> tuple[list[dict[str, str]], list[dict[str, str]], str]:
+        self.send_message(message)
+        response = self.pipeline.interact(message)
+        self.receive_message(response)
+        history.append({"role": "user", "content": message})
+        history.append({"role": "assistant", "content": response})
+        return history, history, ""
+
+    def gradio_save(self) -> str:
+        self.save_chat("chat.json")
+        return "💾 Chat salvata in chat.json"
+
+    def gradio_load(self) -> tuple[list[dict[str, str]], list[dict[str, str]]]:
+        self.load_chat("chat.json")
+        history: list[dict[str, str]] = []
+        for m in self.get_history():
+            history.append({"role": m["role"], "content": m["content"]})
+        return history, history
+
+    def gradio_clear(self) -> tuple[list[dict[str, str]], list[dict[str, str]]]:
+        self.reset_chat()
+        return [], []
+
+    def gradio_build_interface(self) -> gr.Blocks:
+        with gr.Blocks() as interface:
+            gr.Markdown("# 🤖 Agente di Analisi e Consulenza Crypto (Chat)")
+
+            # Dropdown provider e stile
+            with gr.Row():
+                provider = gr.Dropdown(
+                    choices=self.pipeline.list_providers(),
+                    type="index",
+                    label="Modello da usare"
+                )
+                provider.change(fn=self.pipeline.choose_leader, inputs=provider, outputs=None)
+
+                style = gr.Dropdown(
+                    choices=self.pipeline.list_styles(),
+                    type="index",
+                    label="Stile di investimento"
+                )
+                style.change(fn=self.pipeline.choose_strategy, inputs=style, outputs=None)
+
+            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?")
+
+            with gr.Row():
+                clear_btn = gr.Button("🗑️ Reset Chat")
+                save_btn = gr.Button("💾 Salva Chat")
+                load_btn = gr.Button("📂 Carica Chat")
+
+            # Eventi e interazioni
+            msg.submit(self.gradio_respond, inputs=[msg, chatbot], outputs=[chatbot, chatbot, msg])
+            clear_btn.click(self.gradio_clear, inputs=None, outputs=[chatbot, chatbot])
+            save_btn.click(self.gradio_save, inputs=None, outputs=None)
+            load_btn.click(self.gradio_load, inputs=None, outputs=[chatbot, chatbot])
+
+        return interface

src/app/interface/telegram_app.py

@@ -0,0 +1,264 @@
+import io
+import os
+import json
+import httpx
+import logging
+import warnings
+from enum import Enum
+from markdown_pdf import MarkdownPdf, Section
+from telegram import CallbackQuery, InlineKeyboardButton, InlineKeyboardMarkup, Message, Update, User
+from telegram.constants import ChatAction
+from telegram.ext import Application, CallbackQueryHandler, CommandHandler, ContextTypes, ConversationHandler, MessageHandler, filters
+from app.agents.pipeline import Pipeline
+from app.configs import AppConfig
+
+# per per_message di ConversationHandler che rompe sempre qualunque input tu metta
+warnings.filterwarnings("ignore")
+logging = logging.getLogger("telegram")
+
+
+# Lo stato cambia in base al valore di ritorno delle funzioni async
+# END state è già definito in telegram.ext.ConversationHandler
+# Un semplice schema delle interazioni:
+#      /start
+#         ║
+#         V
+#  ╔══ CONFIGS <═════╗
+#  ║      ║ ╚══> SELECT_CONFIG
+#  ║      V
+#  ║  start_team (polling for updates)
+#  ║      ║
+#  ║      V
+#  ╚═══> END
+CONFIGS, SELECT_CONFIG = range(2)
+
+# Usato per separare la query arrivata da Telegram
+QUERY_SEP = "|==|"
+
+class ConfigsChat(Enum):
+    MODEL_TEAM = "Team Model"
+    MODEL_OUTPUT = "Output Model"
+    STRATEGY = "Strategy"
+
+class ConfigsRun:
+    def __init__(self, configs: AppConfig):
+        team, leader, strategy = configs.get_defaults()
+        self.team_model = team
+        self.leader_model = leader
+        self.strategy = strategy
+        self.user_query = ""
+
+
+class TelegramApp:
+    def __init__(self, pipeline: Pipeline):
+        token = os.getenv("TELEGRAM_BOT_TOKEN")
+        assert token, "TELEGRAM_BOT_TOKEN environment variable not set"
+
+        self.user_requests: dict[User, ConfigsRun] = {}
+        self.pipeline = pipeline
+        self.token = token
+        self.create_bot()
+
+    def add_miniapp_url(self, url: str) -> None:
+        try:
+            endpoint = f"https://api.telegram.org/bot{self.token}/setChatMenuButton"
+            payload = {"menu_button": json.dumps({
+                "type": "web_app",
+                "text": "MiniApp",
+                "web_app": { "url": url }
+            })}
+            httpx.post(endpoint, data=payload)
+        except httpx.HTTPError as e:
+            logging.warning(f"Failed to update mini app URL: {e}")
+
+    def create_bot(self) -> None:
+        """
+        Initialize the Telegram bot and set up the conversation handler.
+        """
+        app = Application.builder().token(self.token).build()
+
+        app.add_error_handler(self.__error_handler)
+        app.add_handler(ConversationHandler(
+            per_message=False, # capire a cosa serve perchè da un warning quando parte il server
+            entry_points=[CommandHandler('start', self.__start)],
+            states={
+                CONFIGS: [
+                    CallbackQueryHandler(self.__model_team, pattern=ConfigsChat.MODEL_TEAM.name),
+                    CallbackQueryHandler(self.__model_output, pattern=ConfigsChat.MODEL_OUTPUT.name),
+                    CallbackQueryHandler(self.__strategy, pattern=ConfigsChat.STRATEGY.name),
+                    CallbackQueryHandler(self.__cancel, pattern='^cancel$'),
+                    MessageHandler(filters.TEXT, self.__start_team)  # Any text message
+                ],
+                SELECT_CONFIG: [
+                    CallbackQueryHandler(self.__select_config, pattern=f"^__select_config{QUERY_SEP}.*$"),
+                ]
+            },
+            fallbacks=[CommandHandler('start', self.__start)],
+        ))
+        self.app = app
+
+    def run(self) -> None:
+        self.app.run_polling()
+
+    ########################################
+    # Funzioni di utilità
+    ########################################
+    async def start_message(self, user: User, query: CallbackQuery | Message) -> None:
+        confs = self.user_requests.setdefault(user, ConfigsRun(self.pipeline.configs))
+
+        str_model_team = f"{ConfigsChat.MODEL_TEAM.value}:   {confs.team_model.label}"
+        str_model_output = f"{ConfigsChat.MODEL_OUTPUT.value}:   {confs.leader_model.label}"
+        str_strategy = f"{ConfigsChat.STRATEGY.value}:   {confs.strategy.label}"
+
+        msg, keyboard = (
+            "Please choose an option or write your query",
+            InlineKeyboardMarkup([
+                [InlineKeyboardButton(str_model_team, callback_data=ConfigsChat.MODEL_TEAM.name)],
+                [InlineKeyboardButton(str_model_output, callback_data=ConfigsChat.MODEL_OUTPUT.name)],
+                [InlineKeyboardButton(str_strategy, callback_data=ConfigsChat.STRATEGY.name)],
+                [InlineKeyboardButton("Cancel", callback_data='cancel')]
+            ])
+        )
+
+        if isinstance(query, CallbackQuery):
+            await query.edit_message_text(msg, reply_markup=keyboard, parse_mode='MarkdownV2')
+        else:
+            await query.reply_text(msg, reply_markup=keyboard, parse_mode='MarkdownV2')
+
+    async def handle_callbackquery(self, update: Update) -> tuple[CallbackQuery, User]:
+        assert update.callback_query and update.callback_query.from_user, "Update callback_query or user is None"
+        query = update.callback_query
+        await query.answer()  # Acknowledge the callback query
+        return query, query.from_user
+
+    async def handle_message(self, update: Update) -> tuple[Message, User]:
+        assert update.message and update.message.from_user, "Update message or user is None"
+        return update.message, update.message.from_user
+
+    def callback_data(self, strings: list[str]) -> str:
+        return QUERY_SEP.join(strings)
+
+    async def __error_handler(self, update: object, context: ContextTypes.DEFAULT_TYPE) -> None:
+        try:
+            logging.error(f"Unhandled exception in Telegram handler: {context.error}")
+
+            # Try to notify the user in chat if possible
+            if isinstance(update, Update) and update.effective_chat:
+                chat_id = update.effective_chat.id
+                msg = "An error occurred while processing your request."
+                await context.bot.send_message(chat_id=chat_id, text=msg)
+
+        except Exception:
+            # Ensure we never raise from the error handler itself
+            logging.exception("Exception in the error handler")
+
+    #########################################
+    # Funzioni async per i comandi e messaggi
+    #########################################
+    async def __start(self, update: Update, context: ContextTypes.DEFAULT_TYPE) -> int:
+        message, user = await self.handle_message(update)
+        logging.info(f"@{user.username} started the conversation.")
+        await self.start_message(user, message)
+        return CONFIGS
+
+    async def __model_team(self, update: Update, context: ContextTypes.DEFAULT_TYPE) -> int:
+        return await self._model_select(update, ConfigsChat.MODEL_TEAM)
+
+    async def __model_output(self, update: Update, context: ContextTypes.DEFAULT_TYPE) -> int:
+        return await self._model_select(update, ConfigsChat.MODEL_OUTPUT)
+
+    async def _model_select(self, update: Update, state: ConfigsChat, msg: str | None = None) -> int:
+        query, _ = await self.handle_callbackquery(update)
+
+        models = [(m.label, self.callback_data([f"__select_config", str(state), m.name])) for m in self.pipeline.configs.models.all_models]
+        inline_btns = [[InlineKeyboardButton(name, callback_data=callback_data)] for name, callback_data in models]
+
+        await query.edit_message_text(msg or state.value, reply_markup=InlineKeyboardMarkup(inline_btns))
+        return SELECT_CONFIG
+
+    async def __strategy(self, update: Update, context: ContextTypes.DEFAULT_TYPE) -> int:
+        query, _ = await self.handle_callbackquery(update)
+
+        strategies = [(s.label, self.callback_data([f"__select_config", str(ConfigsChat.STRATEGY), s.name])) for s in self.pipeline.configs.strategies]
+        inline_btns = [[InlineKeyboardButton(name, callback_data=callback_data)] for name, callback_data in strategies]
+
+        await query.edit_message_text("Select a strategy", reply_markup=InlineKeyboardMarkup(inline_btns))
+        return SELECT_CONFIG
+
+    async def __select_config(self, update: Update, context: ContextTypes.DEFAULT_TYPE) -> int:
+        query, user = await self.handle_callbackquery(update)
+        logging.debug(f"@{user.username} --> {query.data}")
+
+        req = self.user_requests[user]
+        _, state, model_name = str(query.data).split(QUERY_SEP)
+        if state == str(ConfigsChat.MODEL_TEAM):
+            req.team_model = self.pipeline.configs.get_model_by_name(model_name)
+        if state == str(ConfigsChat.MODEL_OUTPUT):
+            req.leader_model = self.pipeline.configs.get_model_by_name(model_name)
+        if state == str(ConfigsChat.STRATEGY):
+            req.strategy = self.pipeline.configs.get_strategy_by_name(model_name)
+
+        await self.start_message(user, query)
+        return CONFIGS
+
+    async def __start_team(self, update: Update, context: ContextTypes.DEFAULT_TYPE) -> int:
+        message, user = await self.handle_message(update)
+
+        confs = self.user_requests[user]
+        confs.user_query = message.text or ""
+
+        logging.info(f"@{user.username} started the team with [{confs.team_model.label}, {confs.leader_model.label}, {confs.strategy.label}]")
+        await self.__run_team(update, confs)
+
+        logging.info(f"@{user.username} team finished.")
+        return ConversationHandler.END
+
+    async def __cancel(self, update: Update, context: ContextTypes.DEFAULT_TYPE) -> int:
+        query, user = await self.handle_callbackquery(update)
+        logging.info(f"@{user.username} canceled the conversation.")
+        if user in self.user_requests:
+            del self.user_requests[user]
+        await query.edit_message_text("Conversation canceled. Use /start to begin again.")
+        return ConversationHandler.END
+
+    async def __run_team(self, update: Update, confs: ConfigsRun) -> None:
+        if not update.message: return
+
+        bot = update.get_bot()
+        msg_id = update.message.message_id - 1
+        chat_id = update.message.chat_id
+
+        configs_str = [
+            'Running with configurations:   ',
+            f'Team:      {confs.team_model.label}',
+            f'Output:    {confs.leader_model.label}',
+            f'Strategy:  {confs.strategy.label}',
+            f'Query:     "{confs.user_query}"'
+        ]
+        full_message = f"""```\n{'\n'.join(configs_str)}\n```\n\n"""
+        first_message = full_message + "Generating report, please wait"
+        msg = await bot.edit_message_text(chat_id=chat_id, message_id=msg_id, text=first_message, parse_mode='MarkdownV2')
+        if isinstance(msg, bool): return
+
+        # Remove user query and bot message
+        await bot.delete_message(chat_id=chat_id, message_id=update.message.id)
+
+        self.pipeline.leader_model = confs.leader_model
+        self.pipeline.team_model = confs.team_model
+        self.pipeline.strategy = confs.strategy
+
+        # TODO migliorare messaggi di attesa
+        await bot.send_chat_action(chat_id=chat_id, action=ChatAction.TYPING)
+        report_content = self.pipeline.interact(confs.user_query)
+        await msg.delete()
+
+        # attach report file to the message
+        pdf = MarkdownPdf(toc_level=2, optimize=True)
+        pdf.add_section(Section(report_content, toc=False))
+
+        # TODO vedere se ha senso dare il pdf o solo il messaggio
+        document = io.BytesIO()
+        pdf.save_bytes(document)
+        document.seek(0)
+        await bot.send_document(chat_id=chat_id, document=document, filename="report.pdf", parse_mode='MarkdownV2', caption=full_message)
+

uv.lock

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+
+[[package]]
+name = "markdown-pdf"
+version = "1.10"
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+dependencies = [
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 version = "8.4.2"
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+[[package]]
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+version = "22.5"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "httpx" },
+]
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+]
+
 [[package]]
 name = "pytz"
 version = "2025.2"
@@ -1622,11 +1662,13 @@ dependencies = [
     { name = "gnews" },
     { name = "google-genai" },
     { name = "gradio" },
+    { name = "markdown-pdf" },
     { name = "newsapi-python" },
     { name = "ollama" },
     { name = "praw" },
     { name = "pytest" },
     { name = "python-binance" },
+    { name = "python-telegram-bot" },
     { name = "yfinance" },
 ]
 
@@ -1640,11 +1682,13 @@ requires-dist = [
     { name = "gnews" },
     { name = "google-genai" },
     { name = "gradio" },
+    { name = "markdown-pdf" },
     { name = "newsapi-python" },
     { name = "ollama" },
     { name = "praw" },
     { name = "pytest" },
     { name = "python-binance" },
+    { name = "python-telegram-bot" },
     { name = "yfinance" },
 ]