Refactor team management; asynchronous execution

src/app/agents/pipeline.py

@@ -1,8 +1,5 @@
-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
+from app.agents.predictor import PREDICTOR_INSTRUCTIONS, PredictorOutput, PredictorStyle
 
 
 class Pipeline:
@@ -17,7 +14,6 @@ class Pipeline:
         self.all_styles = list(PredictorStyle)
 
         self.style = self.all_styles[0]
-        self.team = create_team_with(AppModels.OLLAMA_QWEN_1B)
         self.choose_predictor(0)  # Modello di default
 
     # ======================
@@ -65,41 +61,10 @@ class Pipeline:
         4. Restituisce la strategia finale
         """
         # Step 1: raccolta output dai membri del Team
-        team_outputs = self.team.run(query) # type: ignore
-
-        # Step 2: aggregazione output strutturati
-        all_products: list[ProductInfo] = []
-        sentiments: list[str] = []
-
-        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"])
-
-        aggregated_sentiment = "\n".join(sentiments)
-
-        # 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}"
-        )
+        from app.agents import AppTeam
+        from agno.agent import RunEvent
+        team = AppTeam(AppModels.OLLAMA_QWEN_1B) # TODO rendere dinamico
+        team.add_listener(RunEvent.tool_call_started, lambda e: print(f"Team tool call started: {e.agent_name}")) # type: ignore
+        team.add_listener(RunEvent.tool_call_completed, lambda e: print(f"Team tool call completed: {e.agent_name}")) # type: ignore
+        result = team.run_team(query)
+        return result