Better logging

2025-10-09 12:43:27 +02:00
parent e7c32cc227
commit 2642b0a221
7 changed files with 111 additions and 57 deletions
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -13,6 +13,7 @@ dependencies = [
    "pytest", # Test
    "dotenv", # Gestire variabili d'ambiente (generalmente API keys od opzioni)
    "gradio", # UI web semplice con user_input e output
+    "colorlog", # Log colorati in console

    # Per costruire agenti (ovvero modelli che possono fare più cose tramite tool) https://github.com/agno-agi/agno
    # altamente consigliata dato che ha anche tools integrati per fare scraping, calcoli e molto altro
--- a/src/app/main.py
+++ b/src/app/main.py
@@ -3,10 +3,37 @@ from dotenv import load_dotenv
 load_dotenv()


+# Modifico il comportamento del logging (dato che ci sono molte librerie che lo usano)
+import logging.config
+logging.config.dictConfig({
+    'version': 1,
+    'disable_existing_loggers': False, # Mantiene i logger esistenti (es. di terze parti)
+    'formatters': {
+        'colored': {
+            '()': 'colorlog.ColoredFormatter',
+            'format': '%(log_color)s%(levelname)s%(reset)s [%(asctime)s] (%(name)s) - %(message)s'
+        },
+    },
+    'handlers': {
+        'console': {
+            'class': 'logging.StreamHandler',
+            'formatter': 'colored',
+            'level': 'INFO'
+        },
+    },
+    'root': {  # Configura il logger root
+        'handlers': ['console'],
+        'level': 'INFO',
+    },
+    'loggers': {
+        'httpx': {'level': 'WARNING'}, # Troppo spam per INFO
+    }
+})
+
+

 # IMPORTARE LIBRERIE DA QUI IN POI
 from app.utils import ChatManager, BotFunctions
-from agno.utils.log import log_info #type: ignore



@@ -17,7 +44,7 @@ if __name__ == "__main__":
    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}")
+    logging.info(f"UPO AppAI Chat is running on {local_url} and {share_url}")

    telegram = BotFunctions.create_bot(share_url)
    telegram.run_polling()
--- a/src/app/agents/models.py
+++ b/src/app/agents/models.py
@@ -1,14 +1,16 @@
 import os
 import ollama
+import logging
 from enum import Enum
 from agno.agent import Agent
 from agno.models.base import Model
 from agno.models.google import Gemini
 from agno.models.ollama import Ollama
 from agno.tools import Toolkit
-from agno.utils.log import log_warning #type: ignore
 from pydantic import BaseModel

+logging = logging.getLogger(__name__)
+

 class AppModels(Enum):
    """
@@ -36,7 +38,7 @@ class AppModels(Enum):
            app_models = [model for model in AppModels if model.name.startswith("OLLAMA")]
            return [model for model in app_models if model.value in availables]
        except Exception as e:
-            log_warning(f"Ollama is not running or not reachable: {e}")
+            logging.warning(f"Ollama is not running or not reachable: {e}")
            return []

    @staticmethod
@@ -46,7 +48,7 @@ class AppModels(Enum):
        come variabili d'ambiente e ritorna una lista di provider disponibili.
        """
        if not os.getenv("GOOGLE_API_KEY"):
-            log_warning("No GOOGLE_API_KEY set in environment variables.")
+            logging.warning("No GOOGLE_API_KEY set in environment variables.")
            return []
        availables = [AppModels.GEMINI, AppModels.GEMINI_PRO]
        return availables
--- a/src/app/agents/pipeline.py
+++ b/src/app/agents/pipeline.py
@@ -1,9 +1,12 @@
+import logging
 from agno.run.agent import RunOutput
 from app.agents.models import AppModels
 from app.agents.team import create_team_with
 from app.agents.predictor import PREDICTOR_INSTRUCTIONS, PredictorInput, PredictorOutput, PredictorStyle
 from app.base.markets import ProductInfo

+logging = logging.getLogger(__name__)
+

 class Pipeline:
    """
@@ -65,42 +68,51 @@ class Pipeline:
        3. Invoca Predictor
        4. Restituisce la strategia finale
        """
+
        # Step 1: raccolta output dai membri del Team
+        logging.info(f"Pipeline received query: {query}")
        team_outputs = self.team.run(query) # type: ignore

-        # Step 2: aggregazione output strutturati
-        all_products: list[ProductInfo] = []
-        sentiments: list[str] = []
+        # Step 2: recupero ouput
+        if not isinstance(team_outputs.content, str):
+            logging.error(f"Team output is not a string: {team_outputs.content}")
+            raise ValueError("Team output is not a string")
+        logging.info(f"Team finished")
+        return team_outputs.content

-        for agent_output in team_outputs.member_responses:
-            if isinstance(agent_output, RunOutput) and agent_output.metadata is not None:
-                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"])
+        # # Step 2: aggregazione output strutturati
+        # all_products: list[ProductInfo] = []
+        # sentiments: list[str] = []

-        aggregated_sentiment = "\n".join(sentiments)
+        # for agent_output in team_outputs.member_responses:
+        #     if isinstance(agent_output, RunOutput) and agent_output.metadata is not None:
+        #         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"])

-        # Step 3: invocazione Predictor
-        predictor_input = PredictorInput(
-            data=all_products,
-            style=self.style,
-            sentiment=aggregated_sentiment
-        )
+        # aggregated_sentiment = "\n".join(sentiments)

-        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 3: invocazione Predictor
+        # predictor_input = PredictorInput(
+        #     data=all_products,
+        #     style=self.style,
+        #     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.style.value}): {prediction.strategy}\n\n"
-            f"💼 Portafoglio consigliato:\n{portfolio_lines}"
-        )
+        # portfolio_lines = "\n".join(
+        #     [f"{item.asset} ({item.percentage}%): {item.motivation}" for item in prediction.portfolio]
+        # )
+        # return (
+        #     f"📊 Strategia ({self.style.value}): {prediction.strategy}\n\n"
+        #     f"💼 Portafoglio consigliato:\n{portfolio_lines}"
+        # )
--- a/src/app/utils/telegram_app.py
+++ b/src/app/utils/telegram_app.py
@@ -2,10 +2,10 @@ import io
 import os
 import json
 import httpx
+import logging
 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
@@ -15,6 +15,7 @@ from app.agents.pipeline import Pipeline

 # per per_message di ConversationHandler che rompe sempre qualunque input tu metta
 warnings.filterwarnings("ignore")
+logging = logging.getLogger(__name__)


 # Lo stato cambia in base al valore di ritorno delle funzioni async
@@ -70,7 +71,7 @@ class BotFunctions:
        if miniapp_url: BotFunctions.update_miniapp_url(miniapp_url, token)
        app = Application.builder().token(token).build()

-        conv_handler = ConversationHandler(
+        app.add_handler(ConversationHandler(
            per_message=False, # capire a cosa serve perchè da un warning quando parte il server
            entry_points=[CommandHandler('start', BotFunctions.__start)],
            states={
@@ -86,11 +87,7 @@ class BotFunctions:
                ]
            },
            fallbacks=[CommandHandler('start', BotFunctions.__start)],
-        )
-
-        app.add_handler(conv_handler)
-
-        log_info("Telegram bot application created successfully.")
+        ))
        return app

    ########################################
@@ -154,7 +151,7 @@ class BotFunctions:
            })}
            httpx.post(endpoint, data=payload)
        except httpx.HTTPError as e:
-            log_info(f"Failed to update mini app URL: {e}")
+            logging.info(f"Failed to update mini app URL: {e}")

    #########################################
    # Funzioni async per i comandi e messaggi
@@ -162,7 +159,7 @@ class BotFunctions:
    @staticmethod
    async def __start(update: Update, context: ContextTypes.DEFAULT_TYPE) -> int:
        message, user = await BotFunctions.handle_message(update)
-        log_info(f"@{user.username} started the conversation.")
+        logging.info(f"@{user.username} started the conversation.")
        await BotFunctions.start_message(user, message)
        return CONFIGS

@@ -187,7 +184,7 @@ class BotFunctions:
    @staticmethod
    async def __select_config(update: Update, context: ContextTypes.DEFAULT_TYPE) -> int:
        query, user = await BotFunctions.handle_callbackquery(update)
-        log_info(f"@{user.username} --> {query.data}")
+        logging.info(f"@{user.username} --> {query.data}")

        req = BotFunctions.users_req[user]

@@ -209,16 +206,16 @@ class BotFunctions:
        confs = BotFunctions.users_req[user]
        confs.user_query = message.text or ""

-        log_info(f"@{user.username} started the team with [{confs.model_team}, {confs.model_output}, {confs.strategy}]")
+        logging.info(f"@{user.username} started the team with [{confs.model_team}, {confs.model_output}, {confs.strategy}]")
        await BotFunctions.__run_team(update, confs)

-        log_info(f"@{user.username} team finished.")
+        logging.info(f"@{user.username} team finished.")
        return ConversationHandler.END

    @staticmethod
    async def __cancel(update: Update, context: ContextTypes.DEFAULT_TYPE) -> int:
        query, user = await BotFunctions.handle_callbackquery(update)
-        log_info(f"@{user.username} canceled the conversation.")
+        logging.info(f"@{user.username} canceled the conversation.")
        if user in BotFunctions.users_req:
            del BotFunctions.users_req[user]
        await query.edit_message_text("Conversation canceled. Use /start to begin again.")
@@ -246,12 +243,12 @@ class BotFunctions:
        # Remove user query and bot message
        await bot.delete_message(chat_id=chat_id, message_id=update.message.id)

-        # Start TEAM
-        # TODO migliorare messaggi di attesa
+        # TODO settare correttamente i modelli
        pipeline = Pipeline()
-        pipeline.choose_predictor(Pipeline.available_models.index(confs.model_team))
+        #pipeline.choose_predictor(Pipeline.available_models.index(confs.model_team))
        pipeline.choose_style(Pipeline.all_styles.index(confs.strategy))

+        # TODO migliorare messaggi di attesa
        await bot.send_chat_action(chat_id=chat_id, action=ChatAction.TYPING)
        report_content = pipeline.interact(confs.user_query)
        await msg.delete()
--- a/src/app/utils/wrapper_handler.py
+++ b/src/app/utils/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(__name__)
 WrapperType = TypeVar("WrapperType")
 WrapperClassType = TypeVar("WrapperClassType")
 OutputType = TypeVar("OutputType")
@@ -76,7 +77,7 @@ 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

@@ -86,18 +87,18 @@ 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:
@@ -143,6 +144,6 @@ 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)
--- a/uv.lock
+++ b/uv.lock
@@ -285,6 +285,18 @@ wheels = [
    { url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335, upload-time = "2022-10-25T02:36:20.889Z" },
 ]

+[[package]]
+name = "colorlog"
+version = "6.9.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "colorama", marker = "sys_platform == 'win32'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/d3/7a/359f4d5df2353f26172b3cc39ea32daa39af8de522205f512f458923e677/colorlog-6.9.0.tar.gz", hash = "sha256:bfba54a1b93b94f54e1f4fe48395725a3d92fd2a4af702f6bd70946bdc0c6ac2", size = 16624, upload-time = "2024-10-29T18:34:51.011Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e3/51/9b208e85196941db2f0654ad0357ca6388ab3ed67efdbfc799f35d1f83aa/colorlog-6.9.0-py3-none-any.whl", hash = "sha256:5906e71acd67cb07a71e779c47c4bcb45fb8c2993eebe9e5adcd6a6f1b283eff", size = 11424, upload-time = "2024-10-29T18:34:49.815Z" },
+]
+
 [[package]]
 name = "cryptography"
 version = "46.0.2"
@@ -1644,6 +1656,7 @@ source = { virtual = "." }
 dependencies = [
    { name = "agno" },
    { name = "coinbase-advanced-py" },
+    { name = "colorlog" },
    { name = "ddgs" },
    { name = "dotenv" },
    { name = "gnews" },
@@ -1663,6 +1676,7 @@ dependencies = [
 requires-dist = [
    { name = "agno" },
    { name = "coinbase-advanced-py" },
+    { name = "colorlog" },
    { name = "ddgs" },
    { name = "dotenv" },
    { name = "gnews" },