pre merge con phil

src/app.py

@@ -1,7 +1,7 @@
 import gradio as gr
 
 from dotenv import load_dotenv
-from app.tool import ToolAgent
+from app.pipeline import Pipeline
 from agno.utils.log import log_info
 
 ########################################
@@ -16,31 +16,31 @@ if __name__ == "__main__":
     load_dotenv()
     ######################################
 
-    tool_agent = ToolAgent()
+    pipeline = Pipeline()
 
     with gr.Blocks() as demo:
         gr.Markdown("# 🤖 Agente di Analisi e Consulenza Crypto")
 
         with gr.Row():
             provider = gr.Dropdown(
-                choices=tool_agent.list_providers(),
+                choices=pipeline.list_providers(),
                 type="index",
                 label="Modello da usare"
             )
-            provider.change(fn=tool_agent.choose_provider, inputs=provider, outputs=None)
+            provider.change(fn=pipeline.choose_provider, inputs=provider, outputs=None)
 
             style = gr.Dropdown(
-                choices=tool_agent.list_styles(),
+                choices=pipeline.list_styles(),
                 type="index",
                 label="Stile di investimento"
             )
-            style.change(fn=tool_agent.choose_style, inputs=style, outputs=None)
+            style.change(fn=pipeline.choose_style, inputs=style, outputs=None)
 
         user_input = gr.Textbox(label="Richiesta utente")
         output = gr.Textbox(label="Risultato analisi", lines=12)
 
         analyze_btn = gr.Button("🔎 Analizza")
-        analyze_btn.click(fn=tool_agent.interact, inputs=[user_input], outputs=output)
+        analyze_btn.click(fn=pipeline.interact, inputs=[user_input], outputs=output)
 
     server, port = ("0.0.0.0", 8000)
     log_info(f"Starting UPO AppAI on http://{server}:{port}")

src/app/agents/market_agent.py

@@ -0,0 +1,35 @@
+
+from agno.agent import Agent
+from src.app.toolkits.market_toolkit import MarketToolkit
+
+
+class MarketAgent(Agent):
+    """
+    Wrapper che trasforma MarketToolkit in un Agent compatibile con Team.
+    Espone un metodo run(query) che restituisce dati di mercato.
+    """
+
+    def __init__(self, currency: str = "USD"):
+        self.toolkit = MarketToolkit()
+        self.currency = currency
+        self.name = "MarketAgent"
+
+    def run(self, query: str) -> str:
+        # Heuristica semplice: se la query cita simboli specifici, recupera quelli
+        symbols = []
+        for token in query.upper().split():
+            if token in ("BTC", "ETH", "XRP", "LTC", "BCH"):  # TODO: estendere dinamicamente
+                symbols.append(token)
+
+        if not symbols:
+            symbols = ["BTC", "ETH"]  # default
+
+        results = []
+        for sym in symbols:
+            try:
+                price = self.toolkit.get_current_price(sym)
+                results.append(f"{sym}: {price}")
+            except Exception as e:
+                results.append(f"{sym}: errore ({e})")
+
+        return "📊 Dati di mercato:\n" + "\n".join(results)

src/app/pipeline.py

@@ -1,11 +1,11 @@
 from app.agents.news_agent import NewsAgent
 from app.agents.social_agent import SocialAgent
-from app.agents.predictor import PredictorStyle, PredictorInput, PredictorOutput, PREDICTOR_INSTRUCTIONS
+from app.predictor import PredictorStyle, PredictorInput, PredictorOutput, PREDICTOR_INSTRUCTIONS
 from app.markets import MarketAPIs
 from app.models import AppModels
 from agno.utils.log import log_info
 
-class ToolAgent:
+class Pipeline:
     """
     Classe principale che coordina gli agenti per rispondere alle richieste dell'utente.
     """

tests/agents/test_predictor.py

@@ -1,5 +1,5 @@
 import pytest
-from app.agents.predictor import PREDICTOR_INSTRUCTIONS, PredictorInput, PredictorOutput, PredictorStyle
+from app.predictor import PREDICTOR_INSTRUCTIONS, PredictorInput, PredictorOutput, PredictorStyle
 from app.markets.base import ProductInfo
 from app.models import AppModels