src/app/__main__.py

@@ -1,95 +1,24 @@
-import gradio as gr
+# IMPORTANTE: Carichiamo le variabili d'ambiente PRIMA di qualsiasi altra cosa
 from dotenv import load_dotenv
+load_dotenv()
+
+
+
+# IMPORTARE LIBRERIE DA QUI IN POI
+from app.utils import ChatManager, BotFunctions
 from agno.utils.log import log_info #type: ignore
-from app.utils import ChatManager
-from app.agents import Pipeline
-from app.utils.telegram_app import BotFunctions
 
 
-# Disabilita TUTTI i log di livello inferiore a WARNING
-# La maggior parte arrivano da httpx
-import logging
-logging.getLogger().setLevel(logging.WARNING)
-
-
-
-def gradio_app(pipeline: Pipeline, server: str = "0.0.0.0", port: int = 8000) -> str:
-    chat = ChatManager()
-
-    ########################################
-    # Funzioni Gradio
-    ########################################
-    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_style, 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])
-
-    _app, local, share = demo.launch(server_name=server, server_port=port, quiet=True, prevent_thread_lock=True)
-    log_info(f"UPO AppAI Chat is running on {local} and {share}")
-    return share
-
 
 
 if __name__ == "__main__":
-    load_dotenv()  # Carica le variabili d'ambiente dal file .env
+    server, port, share = ("0.0.0.0", 8000, False) # TODO Temp configs, maybe read from env/yaml/ini file later
 
-    pipeline = Pipeline()
-    url = gradio_app(pipeline)
+    chat = ChatManager()
+    gradio = chat.gradio_build_interface()
+    _app, local_url, share_url = gradio.launch(server_name=server, server_port=port, quiet=True, prevent_thread_lock=True, share=share)
+    log_info(f"UPO AppAI Chat is running on {local_url} and {share_url}")
 
-    telegram = BotFunctions.create_bot(pipeline, url)
+    telegram = BotFunctions.create_bot(share_url)
     telegram.run_polling()
 

src/app/agents/pipeline.py

@@ -12,11 +12,12 @@ class Pipeline:
     e scelto dall'utente tramite i dropdown dell'interfaccia grafica.
     """
 
-    def __init__(self):
-        self.available_models = AppModels.availables()
-        self.all_styles = list(PredictorStyle)
+    # Variabili statiche
+    available_models = AppModels.availables()
+    all_styles = list(PredictorStyle)
 
-        self.style = self.all_styles[0]
+    def __init__(self):
+        self.style = Pipeline.all_styles[0]
         self.team = create_team_with(AppModels.OLLAMA_QWEN_1B)
         self.choose_predictor(0)  # Modello di default
 
@@ -27,7 +28,7 @@ class Pipeline:
         """
         Sceglie il modello LLM da usare per il Predictor.
         """
-        model = self.available_models[index]
+        model = Pipeline.available_models[index]
         self.predictor = model.get_agent(
             PREDICTOR_INSTRUCTIONS,
             output_schema=PredictorOutput,
@@ -37,7 +38,7 @@ class Pipeline:
         """
         Sceglie lo stile (conservativo/aggressivo) da usare per il Predictor.
         """
-        self.style = self.all_styles[index]
+        self.style = Pipeline.all_styles[index]
 
     # ======================
     # Helpers
@@ -46,13 +47,13 @@ class Pipeline:
         """
         Restituisce la lista dei nomi dei modelli disponibili.
         """
-        return [model.name for model in self.available_models]
+        return [model.name for model in Pipeline.available_models]
 
     def list_styles(self) -> list[str]:
         """
         Restituisce la lista degli stili di previsione disponibili.
         """
-        return [style.value for style in self.all_styles]
+        return [style.value for style in Pipeline.all_styles]
 
     # ======================
     # Core interaction

src/app/utils/chat_manager.py

@@ -1,5 +1,8 @@
-import json
 import os
+import json
+import gradio as gr
+from app.agents.pipeline import Pipeline
+
 
 class ChatManager:
     """
@@ -11,6 +14,7 @@ class ChatManager:
 
     def __init__(self):
         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_predictor, inputs=provider, outputs=None)
+
+                style = gr.Dropdown(
+                    choices=self.pipeline.list_styles(),
+                    type="index",
+                    label="Stile di investimento"
+                )
+                style.change(fn=self.pipeline.choose_style, 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/utils/telegram_app.py

@@ -1,15 +1,22 @@
+import io
 import os
 import json
 import httpx
+import warnings
 from enum import Enum
 from typing import Any
 from agno.utils.log import log_info  # type: ignore
+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, ExtBot, JobQueue, MessageHandler, filters
 from app.agents import AppModels, PredictorStyle
 from app.agents.pipeline import Pipeline
 
+# per per_message di ConversationHandler che rompe sempre qualunque input tu metta
+warnings.filterwarnings("ignore")
+
+
 # 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:
@@ -32,9 +39,9 @@ class ConfigsChat(Enum):
 
 class ConfigsRun:
     def __init__(self):
-        self.model_team = BotFunctions.pipeline.available_models[0]
-        self.model_output = BotFunctions.pipeline.available_models[0]
-        self.strategy = BotFunctions.pipeline.all_styles[0]
+        self.model_team = Pipeline.available_models[0]
+        self.model_output = Pipeline.available_models[0]
+        self.strategy = Pipeline.all_styles[0]
         self.user_query = ""
 
 
@@ -43,11 +50,10 @@ class BotFunctions:
 
     # In theory this is already thread-safe if run with CPython
     users_req: dict[User, ConfigsRun]
-    pipeline: Pipeline
 
     # che incubo di typing
     @staticmethod
-    def create_bot(pipeline: Pipeline, miniapp_url: str | None = None) -> Application[ExtBot[None], ContextTypes.DEFAULT_TYPE, dict[str, Any], dict[str, Any], dict[str, Any], JobQueue[ContextTypes.DEFAULT_TYPE]]:
+    def create_bot(miniapp_url: str | None = None) -> Application[ExtBot[None], ContextTypes.DEFAULT_TYPE, dict[str, Any], dict[str, Any], dict[str, Any], JobQueue[ContextTypes.DEFAULT_TYPE]]:
         """
         Create a Telegram bot application instance.
         Assumes the TELEGRAM_BOT_TOKEN environment variable is set.
@@ -57,7 +63,6 @@ class BotFunctions:
             AssertionError: If the TELEGRAM_BOT_TOKEN environment variable is not set.
         """
         BotFunctions.users_req = {}
-        BotFunctions.pipeline = pipeline
 
         token = os.getenv("TELEGRAM_BOT_TOKEN", '')
         assert token, "TELEGRAM_BOT_TOKEN environment variable not set"
@@ -118,7 +123,7 @@ class BotFunctions:
     async def handle_configs(update: Update, state: ConfigsChat, msg: str | None = None) -> int:
         query, _ = await BotFunctions.handle_callbackquery(update)
 
-        models = [(m.name, f"__select_config:{state}:{m.name}") for m in BotFunctions.pipeline.available_models]
+        models = [(m.name, f"__select_config:{state}:{m.name}") for m in Pipeline.available_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))
@@ -142,7 +147,7 @@ class BotFunctions:
             endpoint = f"https://api.telegram.org/bot{token}/setChatMenuButton"
             payload = {"menu_button": json.dumps({
                 "type": "web_app",
-                "text": "Apri Mini App", # Il testo che appare sul pulsante
+                "text": "MiniApp",
                 "web_app": {
                     "url": url
                 }
@@ -173,7 +178,7 @@ class BotFunctions:
     async def __strategy(update: Update, context: ContextTypes.DEFAULT_TYPE) -> int:
         query, _ = await BotFunctions.handle_callbackquery(update)
 
-        strategies = [(s.name, f"__select_config:{ConfigsChat.STRATEGY}:{s.name}") for s in BotFunctions.pipeline.all_styles]
+        strategies = [(s.name, f"__select_config:{ConfigsChat.STRATEGY}:{s.name}") for s in Pipeline.all_styles]
         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))
@@ -227,37 +232,35 @@ class BotFunctions:
         msg_id = update.message.message_id - 1
         chat_id = update.message.chat_id
 
-        configs = [
+        configs_str = [
             'Running with configurations:   ',
             f'Team:      {confs.model_team.name}',
             f'Output:    {confs.model_output.name}',
             f'Strategy:  {confs.strategy.name}',
             f'Query:     "{confs.user_query}"'
         ]
-        full_message = f"""```\n{'\n'.join(configs)}\n```\n\n"""
+        full_message = f"""```\n{'\n'.join(configs_str)}\n```\n\n"""
         msg = await bot.edit_message_text(chat_id=chat_id, message_id=msg_id, text=full_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)
 
-        # TODO fare il run effettivo del team
-        # Simulate a long-running task
-        n_simulations = 3
-        import asyncio
+        # Start TEAM
+        # TODO migliorare messaggi di attesa
+        pipeline = Pipeline()
+        pipeline.choose_predictor(Pipeline.available_models.index(confs.model_team))
+        pipeline.choose_style(Pipeline.all_styles.index(confs.strategy))
+
         await bot.send_chat_action(chat_id=chat_id, action=ChatAction.TYPING)
-        for i in range(n_simulations):
-            await msg.edit_text(f"{full_message}Working {i+1}/{n_simulations}", parse_mode='MarkdownV2')
-            await asyncio.sleep(2)
+        report_content = pipeline.interact(confs.user_query)
         await msg.delete()
 
         # attach report file to the message
-        import io
-        from markdown_pdf import MarkdownPdf, Section
-        report_content = f"# Report\n\nThis is a sample report generated by the team."
         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)