Update chat interface (#70)

* Update chat interface to fill height and width in Gradio blocks
* Implement asynchronous streaming for Gradio responses and enhance pipeline event handling
* Refactor tool event handling to provide user-friendly messages and add utility function for descriptive tool actions

src/app/agents/action_registry.py

@@ -0,0 +1,37 @@
+from typing import Any, Callable
+
+
+# Registro centrale popolato da tutti i file Toolkit all'avvio.
+ACTION_DESCRIPTIONS: dict[str, str] = {}
+
+def get_user_friendly_action(tool_name: str) -> str:
+    """
+    Restituisce un messaggio leggibile e descrittivo per l'utente
+    leggendo dal registro globale.
+    """
+    # Usa il dizionario ACTION_DESCRIPTIONS importato
+    return ACTION_DESCRIPTIONS.get(tool_name, f"⚙️ Eseguo l'operazione: {tool_name}...")
+
+def friendly_action(description: str) -> Callable[..., Any]:
+    """
+    Decoratore che registra automaticamente la descrizione "user-friendly"
+    di un metodo nel registro globale.
+
+    Questo decoratore viene eseguito all'avvio dell'app (quando i file
+    vengono importati) e popola il dizionario ACTION_DESCRIPTIONS.
+
+    Restituisce la funzione originale non modificata.
+    """
+
+    def decorator(func: Callable[..., Any]) -> Callable[..., Any]:
+        # Registra l'azione
+        tool_name = func.__name__
+        if tool_name in ACTION_DESCRIPTIONS:
+            print(f"Attenzione: Azione '{tool_name}' registrata più volte.")
+
+        ACTION_DESCRIPTIONS[tool_name] = description
+
+        # Restituisce la funzione originale
+        return func
+
+    return decorator

src/app/agents/core.py

@@ -45,28 +45,28 @@ class PipelineInputs:
         """
         Sceglie il modello LLM da usare per l'analizzatore di query.
         """
-        assert index >= 0 and index < len(self.configs.models.all_models), "Index out of range for models list."
+        assert 0 <= index < len(self.configs.models.all_models), "Index out of range for models list."
         self.query_analyzer_model = self.configs.models.all_models[index]
 
     def choose_team_leader(self, index: int):
         """
         Sceglie il modello LLM da usare per il Team Leader.
         """
-        assert index >= 0 and index < len(self.configs.models.all_models), "Index out of range for models list."
+        assert 0 <= index < len(self.configs.models.all_models), "Index out of range for models list."
         self.team_leader_model = self.configs.models.all_models[index]
 
     def choose_team(self, index: int):
         """
         Sceglie il modello LLM da usare per il Team.
         """
-        assert index >= 0 and index < len(self.configs.models.all_models), "Index out of range for models list."
+        assert 0 <= index < len(self.configs.models.all_models), "Index out of range for models list."
         self.team_model = self.configs.models.all_models[index]
 
     def choose_report_generator(self, index: int):
         """
         Sceglie il modello LLM da usare per il generatore di report.
         """
-        assert index >= 0 and index < len(self.configs.models.all_models), "Index out of range for models list."
+        assert 0 <= index < len(self.configs.models.all_models), "Index out of range for models list."
         self.report_generation_model = self.configs.models.all_models[index]
 
     def choose_strategy(self, index: int):
@@ -145,21 +145,31 @@ class RunMessage:
         - In esecuzione (➡️)
         - Completato (✅)
 
-        Lo stato di esecuzione può essere assegnato solo ad uno step alla volta.
+        Lo stato di esecuzione può essere assegnato solo a uno step alla volta.
         Args:
-            inputs (PipelineInputs): Input della pipeline per mostrare la configurazione.
-            prefix (str, optional): Prefisso del messaggio. Defaults to "".
-            suffix (str, optional): Suffisso del messaggio. Defaults to "".
+            inputs (PipelineInputs): Input della pipeline per mostrare la configurazione
+            prefix (str, optional): Prefisso del messaggio. Defaults to ""
+            suffix (str, optional): Suffisso del messaggio. Defaults to ""
         """
         self.base_message = f"Running configurations: \n{prefix}{inputs}{suffix}\n\n"
         self.emojis = ['🔳', '➡️', '✅']
         self.placeholder = '<<<>>>'
         self.current = 0
-        self.steps_total = [
-            (f"{self.placeholder} Query Check", 1),
-            (f"{self.placeholder} Info Recovery", 0),
-            (f"{self.placeholder} Report Generation", 0),
-        ]
+        self.steps_total: list[tuple[str, int]] = []
+        self.set_steps(["Query Check", "Info Recovery", "Report Generation"])
+
+    def set_steps(self, steps: list[str]) -> 'RunMessage':
+        """
+        Inizializza gli step di esecuzione con lo stato iniziale.
+        Args:
+            steps (list[str]): Lista degli step da includere nel messaggio.
+        Returns:
+            RunMessage: L'istanza aggiornata di RunMessage.
+        """
+        self.steps_total = [(f"{self.placeholder} {step}", 0) for step in steps]
+        self.steps_total[0] = (self.steps_total[0][0], 1)  # Primo step in esecuzione
+        self.current = 0
+        return self
 
     def update(self) -> 'RunMessage':
         """
@@ -176,15 +186,15 @@ class RunMessage:
             self.steps_total[self.current] = (text_curr, state_curr + 1)
         return self
 
-    def update_step(self, text_extra: str = "") -> 'RunMessage':
+    def update_step_with_tool(self, tool_used: str = "") -> 'RunMessage':
         """
         Aggiorna il messaggio per lo step corrente.
         Args:
-            text_extra (str, optional): Testo aggiuntivo da includere nello step. Defaults to "".
+            tool_used (str, optional): Testo aggiuntivo da includere nello step. Defaults to "".
         """
         text_curr, state_curr = self.steps_total[self.current]
-        if text_extra:
-            text_curr = f"{text_curr.replace('╚', '╠')}\n╚═ {text_extra}"
+        if tool_used:
+            text_curr = f"{text_curr.replace('╚', '╠')}\n╚═ {tool_used}"
         self.steps_total[self.current] = (text_curr, state_curr)
         return self
 
@@ -196,3 +206,4 @@ class RunMessage:
         """
         steps = [msg.replace(self.placeholder, self.emojis[state]) for msg, state in self.steps_total]
         return self.base_message + "\n".join(steps)
+

src/app/agents/pipeline.py

@@ -1,8 +1,7 @@
-import asyncio
 from enum import Enum
 import logging
 import random
-from typing import Any, Callable
+from typing import Any, AsyncGenerator, Callable
 from agno.agent import RunEvent
 from agno.run.workflow import WorkflowRunEvent
 from agno.workflow.types import StepInput, StepOutput
@@ -13,28 +12,34 @@ from app.agents.core import *
 logging = logging.getLogger("pipeline")
 
 
-
 class PipelineEvent(str, Enum):
     QUERY_CHECK = "Query Check"
-    QUERY_ANALYZER = "Query Analyzer"
+    QUERY_CHECK_END = "Query Check End"
     INFO_RECOVERY = "Info Recovery"
+    INFO_RECOVERY_END = "Info Recovery End"
     REPORT_GENERATION = "Report Generation"
-    REPORT_TRANSLATION = "Report Translation"
-    RUN_FINISHED = WorkflowRunEvent.workflow_completed.value
-    TOOL_USED = RunEvent.tool_call_completed.value
+    REPORT_GENERATION_END = "Report Generation End"
+    TOOL_USED = RunEvent.tool_call_started.value
+    TOOL_USED_END = RunEvent.tool_call_completed.value
+    RUN_END = WorkflowRunEvent.workflow_completed.value
 
     def check_event(self, event: str, step_name: str) -> bool:
-        return event == self.value or (WorkflowRunEvent.step_completed == event and step_name == self.value)
+        if event == self.value:
+            return True
+
+        index = self.value.rfind(" End")
+        value = self.value[:index] if index > -1 else self.value
+        step_state = WorkflowRunEvent.step_completed if index > -1 else WorkflowRunEvent.step_started
+        return step_name == value and step_state == event
 
     @classmethod
-    def get_log_events(cls, run_id: int) -> list[tuple['PipelineEvent', Callable[[Any], None]]]:
+    def get_log_events(cls, run_id: int) -> list[tuple['PipelineEvent', Callable[[Any], str | None]]]:
         return [
-            (PipelineEvent.QUERY_CHECK, lambda _: logging.info(f"[{run_id}] Query Check completed.")),
-            (PipelineEvent.QUERY_ANALYZER, lambda _: logging.info(f"[{run_id}] Query Analyzer completed.")),
-            (PipelineEvent.INFO_RECOVERY, lambda _: logging.info(f"[{run_id}] Info Recovery completed.")),
-            (PipelineEvent.REPORT_GENERATION, lambda _: logging.info(f"[{run_id}] Report Generation completed.")),
-            (PipelineEvent.TOOL_USED, lambda e: logging.info(f"[{run_id}] Tool used [{e.tool.tool_name} {e.tool.tool_args}] by {e.agent_name}.")),
-            (PipelineEvent.RUN_FINISHED, lambda _: logging.info(f"[{run_id}] Run completed.")),
+            (PipelineEvent.QUERY_CHECK_END, lambda _: logging.info(f"[{run_id}] Query Check completed.")),
+            (PipelineEvent.INFO_RECOVERY_END, lambda _: logging.info(f"[{run_id}] Info Recovery completed.")),
+            (PipelineEvent.REPORT_GENERATION_END, lambda _: logging.info(f"[{run_id}] Report Generation completed.")),
+            (PipelineEvent.TOOL_USED_END, lambda e: logging.info(f"[{run_id}] Tool used [{e.tool.tool_name} {e.tool.tool_args}] by {e.agent_name}.")),
+            (PipelineEvent.RUN_END, lambda _: logging.info(f"[{run_id}] Run completed.")),
         ]
 
 
@@ -53,7 +58,7 @@ class Pipeline:
         """
         self.inputs = inputs
 
-    def interact(self, listeners: list[tuple[PipelineEvent, Callable[[Any], None]]] = []) -> str:
+    async def interact(self, listeners: list[tuple[PipelineEvent, Callable[[Any], str | None]]] = []) -> str:
         """
         Esegue la pipeline di agenti per rispondere alla query dell'utente.
         Args:
@@ -61,9 +66,12 @@ class Pipeline:
         Returns:
             La risposta generata dalla pipeline.
         """
-        return asyncio.run(self.interact_async(listeners))
+        response = ""
+        async for chunk in self.interact_stream(listeners):
+            response = chunk
+        return response
 
-    async def interact_async(self, listeners: list[tuple[PipelineEvent, Callable[[Any], None]]] = []) -> str:
+    async def interact_stream(self, listeners: list[tuple[PipelineEvent, Callable[[Any], str | None]]] = []) -> AsyncGenerator[str, None]:
         """
         Versione asincrona che esegue la pipeline di agenti per rispondere alla query dell'utente.
         Args:
@@ -81,9 +89,8 @@ class Pipeline:
         )
 
         workflow = self.build_workflow()
-        result = await self.run(workflow, query, events=events)
-        return result
-
+        async for item in self.run_stream(workflow, query, events=events):
+            yield item
 
     def build_workflow(self) -> Workflow:
         """
@@ -99,7 +106,8 @@ class Pipeline:
         # Step 2: Crea gli steps
         def condition_query_ok(step_input: StepInput) -> StepOutput:
             val = step_input.previous_step_content
-            return StepOutput(stop=not val.is_crypto) if isinstance(val, QueryOutputs) else StepOutput(stop=True)
+            stop = (not val.is_crypto) if isinstance(val, QueryOutputs) else True
+            return StepOutput(stop=stop)
 
         query_check = Step(name=PipelineEvent.QUERY_CHECK, agent=query_check)
         info_recovery = Step(name=PipelineEvent.INFO_RECOVERY, team=team)
@@ -114,33 +122,39 @@ class Pipeline:
         ])
 
     @classmethod
-    async def run(cls, workflow: Workflow, query: QueryInputs, events: list[tuple[PipelineEvent, Callable[[Any], None]]]) -> str:
+    async def run_stream(cls, workflow: Workflow, query: QueryInputs, events: list[tuple[PipelineEvent, Callable[[Any], str | None]]]) -> AsyncGenerator[str, None]:
         """
-        Esegue il workflow e gestisce gli eventi tramite le callback fornite.
+        Esegue il workflow e restituisce gli eventi di stato e il risultato finale.
         Args:
-            workflow: istanza di Workflow da eseguire
-            query: query dell'utente da passare al workflow
-            events: dizionario di callback per eventi specifici (opzionale)
-        Returns:
-            La risposta generata dal workflow.
+            workflow: L'istanza di Workflow da eseguire
+            query: Gli input della query
+            events: La lista di eventi e callback da gestire durante l'esecuzione.
+        Yields:
+            Aggiornamenti di stato e la risposta finale generata dal workflow.
         """
         iterator = await workflow.arun(query, stream=True, stream_intermediate_steps=True)
-
         content = None
+
         async for event in iterator:
             step_name = getattr(event, 'step_name', '')
+
+            # Chiama i listeners (se presenti) per ogni evento
             for app_event, listener in events:
                 if app_event.check_event(event.event, step_name):
-                    listener(event)
-            if event.event == WorkflowRunEvent.step_completed:
+                    update = listener(event)
+                    if update: yield update
+
+            # Salva il contenuto finale quando uno step è completato
+            if event.event == WorkflowRunEvent.step_completed.value:
                 content = getattr(event, 'content', '')
 
+        # Restituisce la risposta finale
         if content and isinstance(content, str):
             think_str = "</think>"
             think = content.rfind(think_str)
-            return content[(think + len(think_str)):] if think != -1 else content
-        if content and isinstance(content, QueryOutputs):
-            return content.response
-
-        logging.error(f"No output from workflow: {content}")
-        return "No output from workflow, something went wrong."
+            yield content[(think + len(think_str)):] if think != -1 else content
+        elif content and isinstance(content, QueryOutputs):
+            yield content.response
+        else:
+            logging.error(f"No output from workflow: {content}")
+            yield "Nessun output dal workflow, qualcosa è andato storto."

src/app/api/tools/market_tool.py

@@ -1,4 +1,6 @@
 from agno.tools import Toolkit
+
+from app.agents.action_registry import friendly_action
 from app.api.tools.instructions import MARKET_TOOL_INSTRUCTIONS
 from app.api.wrapper_handler import WrapperHandler
 from app.api.core.markets import MarketWrapper, Price, ProductInfo
@@ -40,6 +42,7 @@ class MarketAPIsTool(MarketWrapper, Toolkit):
             ],
         )
 
+    @friendly_action("🔍 Recupero le informazioni sul prodotto richiesto...")
     def get_product(self, asset_id: str) -> ProductInfo:
         """
         Gets product information for a *single* asset from the *first available* provider.
@@ -56,6 +59,7 @@ class MarketAPIsTool(MarketWrapper, Toolkit):
         """
         return self.handler.try_call(lambda w: w.get_product(asset_id))
 
+    @friendly_action("📦 Recupero i dati su più asset...")
     def get_products(self, asset_ids: list[str]) -> list[ProductInfo]:
         """
         Gets product information for a *list* of assets from the *first available* provider.
@@ -72,6 +76,7 @@ class MarketAPIsTool(MarketWrapper, Toolkit):
         """
         return self.handler.try_call(lambda w: w.get_products(asset_ids))
 
+    @friendly_action("📊 Recupero i dati storici dei prezzi...")
     def get_historical_prices(self, asset_id: str, limit: int = 100) -> list[Price]:
         """
         Gets historical price data for a *single* asset from the *first available* provider.
@@ -89,6 +94,7 @@ class MarketAPIsTool(MarketWrapper, Toolkit):
         """
         return self.handler.try_call(lambda w: w.get_historical_prices(asset_id, limit))
 
+    @friendly_action("🧩 Aggrego le informazioni da più fonti...")
     def get_products_aggregated(self, asset_ids: list[str]) -> list[ProductInfo]:
         """
         Gets product information for multiple assets from *all available providers* and *aggregates* the results.
@@ -109,6 +115,7 @@ class MarketAPIsTool(MarketWrapper, Toolkit):
         all_products = self.handler.try_call_all(lambda w: w.get_products(asset_ids))
         return ProductInfo.aggregate(all_products)
 
+    @friendly_action("📈 Creo uno storico aggregato dei prezzi...")
     def get_historical_prices_aggregated(self, asset_id: str = "BTC", limit: int = 100) -> list[Price]:
         """
         Gets historical price data for a single asset from *all available providers* and *aggregates* the results.

src/app/api/tools/news_tool.py

@@ -1,4 +1,6 @@
 from agno.tools import Toolkit
+
+from app.agents.action_registry import friendly_action
 from app.api.tools.instructions import NEWS_TOOL_INSTRUCTIONS
 from app.api.wrapper_handler import WrapperHandler
 from app.api.core.news import NewsWrapper, Article
@@ -42,6 +44,7 @@ class NewsAPIsTool(NewsWrapper, Toolkit):
             ],
         )
 
+    @friendly_action("📰 Cerco le notizie principali...")
     def get_top_headlines(self, limit: int = 100) -> list[Article]:
         """
         Retrieves top headlines from the *first available* news provider.
@@ -58,6 +61,7 @@ class NewsAPIsTool(NewsWrapper, Toolkit):
         """
         return self.handler.try_call(lambda w: w.get_top_headlines(limit))
 
+    @friendly_action("🔎 Cerco notizie recenti sull'argomento...")
     def get_latest_news(self, query: str, limit: int = 100) -> list[Article]:
         """
         Searches for the latest news on a specific topic from the *first available* provider.
@@ -75,6 +79,7 @@ class NewsAPIsTool(NewsWrapper, Toolkit):
         """
         return self.handler.try_call(lambda w: w.get_latest_news(query, limit))
 
+    @friendly_action("🗞️ Raccolgo le notizie principali da tutte le fonti...")
     def get_top_headlines_aggregated(self, limit: int = 100) -> dict[str, list[Article]]:
         """
         Retrieves top headlines from *all available providers* and aggregates the results.
@@ -94,6 +99,7 @@ class NewsAPIsTool(NewsWrapper, Toolkit):
         """
         return self.handler.try_call_all(lambda w: w.get_top_headlines(limit))
 
+    @friendly_action("📚 Raccolgo notizie specifiche da tutte le fonti...")
     def get_latest_news_aggregated(self, query: str, limit: int = 100) -> dict[str, list[Article]]:
         """
         Searches for news on a specific topic from *all available providers* and aggregates the results.

src/app/api/tools/plan_memory_tool.py

@@ -14,6 +14,7 @@ class PlanMemoryTool(Toolkit):
 
     def __init__(self):
         self.tasks: list[Task] = []
+
         Toolkit.__init__(self, # type: ignore[call-arg]
             name="Plan Memory Toolkit",
             instructions=PLAN_MEMORY_TOOL_INSTRUCTIONS,

src/app/api/tools/social_tool.py

@@ -1,4 +1,6 @@
 from agno.tools import Toolkit
+
+from app.agents.action_registry import friendly_action
 from app.api.tools.instructions import SOCIAL_TOOL_INSTRUCTIONS
 from app.api.wrapper_handler import WrapperHandler
 from app.api.core.social import SocialPost, SocialWrapper
@@ -41,6 +43,7 @@ class SocialAPIsTool(SocialWrapper, Toolkit):
             ],
         )
 
+    @friendly_action("📱 Cerco i post più popolari sui social...")
     def get_top_crypto_posts(self, limit: int = 5) -> list[SocialPost]:
         """
         Retrieves top cryptocurrency-related posts from the *first available* social media provider.
@@ -57,6 +60,7 @@ class SocialAPIsTool(SocialWrapper, Toolkit):
         """
         return self.handler.try_call(lambda w: w.get_top_crypto_posts(limit))
 
+    @friendly_action("🌐 Raccolgo i post da tutte le piattaforme social...")
     def get_top_crypto_posts_aggregated(self, limit_per_wrapper: int = 5) -> dict[str, list[SocialPost]]:
         """
         Retrieves top cryptocurrency-related posts from *all available providers* and aggregates the results.

src/app/api/tools/symbols_tool.py

@@ -22,6 +22,7 @@ class CryptoSymbolsTools(Toolkit):
     def __init__(self, cache_file: str = 'resources/cryptos.csv'):
         self.cache_file = cache_file
         self.final_table = pd.read_csv(self.cache_file) if os.path.exists(self.cache_file) else pd.DataFrame() # type: ignore
+
         Toolkit.__init__(self, # type: ignore
             name="Crypto Symbols Tool",
             instructions=SYMBOLS_TOOL_INSTRUCTIONS,

src/app/interface/chat.py

@@ -1,7 +1,9 @@
 import os
 import json
+from typing import Any, Callable
 import gradio as gr
-from app.agents.pipeline import Pipeline, PipelineInputs
+from app.agents.action_registry import get_user_friendly_action
+from app.agents.pipeline import Pipeline, PipelineEvent, PipelineInputs
 
 
 class ChatManager:
@@ -49,13 +51,28 @@ class ChatManager:
     ########################################
     # Funzioni Gradio
     ########################################
-    def gradio_respond(self, message: str, history: list[tuple[str, str]]) -> str:
+    async def gradio_respond(self, message: str, history: list[tuple[str, str]]):
+        """
+        Versione asincrona in streaming.
+        Produce (yield) aggiornamenti di stato e la risposta finale.
+        """
         self.inputs.user_query = message
         pipeline = Pipeline(self.inputs)
-        response = pipeline.interact()
+        listeners: list[tuple[PipelineEvent, Callable[[Any], str | None]]] = [ # type: ignore
+            (PipelineEvent.QUERY_CHECK, lambda _: "🔍 Sto controllando la tua richiesta..."),
+            (PipelineEvent.INFO_RECOVERY, lambda _: "📊 Sto recuperando i dati (mercato, news, social)..."),
+            (PipelineEvent.REPORT_GENERATION, lambda _: "✍️ Sto scrivendo il report finale..."),
+            (PipelineEvent.TOOL_USED, lambda e:  get_user_friendly_action(e.tool.tool_name))
+        ]
 
-        self.history.append((message, response))
-        return response
+        response = None
+        async for chunk in pipeline.interact_stream(listeners=listeners):
+            response = chunk  # Salva l'ultimo chunk (che sarà la risposta finale)
+            yield response  # Restituisce l'aggiornamento (o la risposta finale) a Gradio
+
+        # Dopo che il generatore è completo, salva l'ultima risposta nello storico
+        if response:
+            self.history.append((message, response))
 
     def gradio_save(self) -> str:
         self.save_chat("chat.json")
@@ -72,7 +89,7 @@ class ChatManager:
 
 
     def gradio_build_interface(self) -> gr.Blocks:
-        with gr.Blocks() as interface:
+        with gr.Blocks(fill_height=True, fill_width=True) as interface:
             gr.Markdown("# 🤖 Agente di Analisi e Consulenza Crypto (Chat)")
 
             # --- Prepara le etichette di default per i dropdown

src/app/interface/telegram.py

@@ -271,7 +271,7 @@ class TelegramApp:
         await bot.delete_message(chat_id=chat_id, message_id=update.message.id)
 
         def update_user(update_step: str = "") -> None:
-            if update_step: run_message.update_step(update_step)
+            if update_step: run_message.update_step_with_tool(update_step)
             else: run_message.update()
 
             message = run_message.get_latest()
@@ -280,11 +280,11 @@ class TelegramApp:
 
         await bot.send_chat_action(chat_id=chat_id, action=ChatAction.TYPING)
         pipeline = Pipeline(inputs)
-        report_content = await pipeline.interact_async(listeners=[
-            (PipelineEvent.QUERY_CHECK, lambda _: update_user()),
-            (PipelineEvent.TOOL_USED, lambda e: update_user(e.tool.tool_name.replace('get_', '').replace("_", "\\_"))),
-            (PipelineEvent.INFO_RECOVERY, lambda _: update_user()),
-            (PipelineEvent.REPORT_GENERATION, lambda _: update_user()),
+        report_content = await pipeline.interact(listeners=[
+            (PipelineEvent.QUERY_CHECK_END, lambda _: update_user()),
+            (PipelineEvent.TOOL_USED_END, lambda e: update_user(e.tool.tool_name.replace('get_', '').replace("_", "\\_"))),
+            (PipelineEvent.INFO_RECOVERY_END, lambda _: update_user()),
+            (PipelineEvent.REPORT_GENERATION_END, lambda _: update_user()),
         ])
 
         # attach report file to the message