+ Creato scheletro dell'app con agenti mock per test.
* Modificato pyproject.toml con versioni aggiornate di agno (aggiunto file requirements.txt per compatibilità eventuale con hugging face). # Consiglio di spostare il contenuto di src direttamente nella directory base per tenere compatibilità con hugging face in caso si voglia sfruttare in futuro.
This commit is contained in:
trojanhorse47
@@ -27,5 +27,8 @@ dependencies = [
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# altamente consigliata dato che ha anche tools integrati per fare scraping, calcoli e molto altro
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# oltre a questa è necessario installare anche le librerie specifiche per i modelli che si vogliono usare
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"agno",
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"google-genai"
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"google-genai",
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"openai",
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"anthropic",
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"google",
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]
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77
requirements.txt
Normal file
77
requirements.txt
Normal file
@@ -0,0 +1,77 @@
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agno==2.0.5
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aiofiles==24.1.0
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annotated-types==0.7.0
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anthropic==0.68.0
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anyio==4.10.0
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beautifulsoup4==4.13.5
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brotli==1.1.0
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cachetools==5.5.2
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certifi==2025.8.3
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charset-normalizer==3.4.3
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click==8.2.1
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distro==1.9.0
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docstring-parser==0.17.0
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dotenv==0.9.9
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fastapi==0.116.2
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ffmpy==0.6.1
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filelock==3.19.1
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fsspec==2025.9.0
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gitdb==4.0.12
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gitpython==3.1.45
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google==3.0.0
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google-auth==2.40.3
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google-genai==1.38.0
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gradio==5.46.0
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gradio-client==1.13.0
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groovy==0.1.2
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h11==0.16.0
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hf-xet==1.1.10
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httpcore==1.0.9
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httpx==0.28.1
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huggingface-hub==0.35.0
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idna==3.10
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jinja2==3.1.6
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jiter==0.11.0
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markdown-it-py==4.0.0
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markupsafe==3.0.2
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mdurl==0.1.2
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numpy==2.3.3
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openai==1.107.3
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orjson==3.11.3
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packaging==25.0
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pandas==2.3.2
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pillow==11.3.0
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pyasn1==0.6.1
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pyasn1-modules==0.4.2
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pydantic==2.11.9
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pydantic-core==2.33.2
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pydantic-settings==2.10.1
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pydub==0.25.1
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pygments==2.19.2
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python-dateutil==2.9.0.post0
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python-dotenv==1.1.1
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python-multipart==0.0.20
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pytz==2025.2
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pyyaml==6.0.2
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requests==2.32.5
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rich==14.1.0
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rsa==4.9.1
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ruff==0.13.0
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safehttpx==0.1.6
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semantic-version==2.10.0
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shellingham==1.5.4
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six==1.17.0
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smmap==5.0.2
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sniffio==1.3.1
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soupsieve==2.8
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starlette==0.48.0
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tenacity==9.1.2
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tomlkit==0.13.3
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tqdm==4.67.1
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typer==0.17.4
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typing-extensions==4.15.0
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typing-inspection==0.4.1
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tzdata==2025.2
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urllib3==2.5.0
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uvicorn==0.35.0
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websockets==15.0.1
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37
src/app.py
37
src/app.py
@@ -1,8 +1,35 @@
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import agno
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import gradio
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import dotenv
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import requests
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import gradio as gr
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from app.tool import ToolAgent
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tool_agent = ToolAgent()
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def analyze_request(user_input, provider, style):
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try:
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return tool_agent.interact(user_input, provider=provider, style=style)
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except Exception as e:
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return f"❌ Errore: {str(e)}"
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with gr.Blocks() as demo:
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gr.Markdown("# 🤖 Agente di Analisi e Consulenza Crypto")
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with gr.Row():
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provider = gr.Dropdown(
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choices=["mock", "openai", "anthropic", "google", "deepseek"],
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value="mock",
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label="Modello da usare"
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)
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style = gr.Dropdown(
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choices=["conservative", "aggressive"],
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value="conservative",
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label="Stile di investimento"
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)
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user_input = gr.Textbox(label="Richiesta utente")
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output = gr.Textbox(label="Risultato analisi", lines=12)
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analyze_btn = gr.Button("🔎 Analizza")
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analyze_btn.click(fn=analyze_request, inputs=[user_input, provider, style], outputs=output)
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if __name__ == "__main__":
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print("Hello World!")
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demo.launch()
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0
src/app/__init__.py
Normal file
0
src/app/__init__.py
Normal file
0
src/app/agents/__init__.py
Normal file
0
src/app/agents/__init__.py
Normal file
5
src/app/agents/market_agent.py
Normal file
5
src/app/agents/market_agent.py
Normal file
@@ -0,0 +1,5 @@
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class MarketAgent:
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@staticmethod
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def analyze(query: str) -> str:
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# Mock analisi mercato
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return "📊 Analisi di mercato: BTC stabile, ETH in leggera crescita, altcoin volatili."
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5
src/app/agents/news_agent.py
Normal file
5
src/app/agents/news_agent.py
Normal file
@@ -0,0 +1,5 @@
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class NewsAgent:
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@staticmethod
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def analyze(query: str) -> str:
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# Mock analisi news
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return "📰 Sentiment news: ottimismo sul mercato crypto grazie all'adozione istituzionale."
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152
src/app/agents/predictor_agent.py
Normal file
152
src/app/agents/predictor_agent.py
Normal file
@@ -0,0 +1,152 @@
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import json
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import os
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from typing import Any
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import requests
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import openai
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import anthropic
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from google.genai import Client
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from dotenv import load_dotenv
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load_dotenv()
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class PredictorAgent:
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def __init__(self):
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# Ollama via HTTP locale
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self.providers = {
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"ollama": {"type": "ollama", "host": os.getenv("OLLAMA_HOST", "http://localhost:11434"), "model": "llama3"}
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}
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# OpenAI
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openai_key = os.getenv("OPENAI_API_KEY")
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if openai_key:
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openai.api_key = openai_key
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self.providers["openai"] = {"type": "openai", "client": openai, "model": "gpt-4o-mini"}
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# Anthropic
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anthropic_key = os.getenv("ANTHROPIC_API_KEY")
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if anthropic_key:
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client = anthropic.Client(api_key=anthropic_key)
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self.providers["anthropic"] = {"type": "anthropic", "client": client, "model": "claude-3"}
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# Google Gemini
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google_key = os.getenv("GEMINI_API_KEY")
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if google_key:
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client = Client(credentials={"api_key": google_key})
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self.providers["google"] = {"type": "google", "client": client, "model": "gemini-1.5-flash"}
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# DeepSeek
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deepseek_key = os.getenv("DEEPSEEK_API_KEY")
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if deepseek_key:
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self.providers["deepseek"] = {"type": "deepseek", "api_key": deepseek_key, "model": "deepseek-chat"}
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def predict(self, data, sentiment, style="conservative", provider="mock"):
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provider = provider.lower()
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if provider == "mock" or provider not in self.providers:
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return self._predict_mock(style)
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prompt = f"""
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Sei un consulente finanziario crypto.
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Dati di mercato: {data}
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Sentiment estratto: {sentiment}
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Stile richiesto: {style}
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Fornisci una strategia di investimento chiara e breve (max 5 frasi),
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con percentuali di portafoglio e motivazioni sintetiche."""
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cfg: Any = self.providers[provider]
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try:
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if cfg["type"] == "ollama":
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return self._predict_ollama_http(prompt, cfg["host"], cfg["model"])
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elif cfg["type"] == "openai":
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return self._predict_openai(prompt, cfg["client"], cfg["model"])
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elif cfg["type"] == "anthropic":
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return self._predict_anthropic(prompt, cfg["client"], cfg["model"])
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elif cfg["type"] == "google":
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return self._predict_google(prompt, cfg["client"], cfg["model"])
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elif cfg["type"] == "deepseek":
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return self._predict_deepseek(prompt, cfg["api_key"], cfg["model"])
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return None
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except Exception as e:
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return f"⚠️ Provider {provider} non riconosciuto: {e}"
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@staticmethod
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def _predict_ollama_http(prompt, host, model):
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url = host.rstrip("/") + "/api/generate"
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payload = {"model": model, "prompt": prompt, "max_tokens": 300}
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r = requests.post(url, json=payload, timeout=60)
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r.raise_for_status()
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try:
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data = r.json()
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if isinstance(data, dict):
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for key in ("text", "generated", "content"):
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if key in data:
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return str(data[key])
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if "choices" in data and isinstance(data["choices"], list) and data["choices"]:
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c = data["choices"][0]
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if "text" in c:
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return c["text"]
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return json.dumps(data)
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except ValueError:
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return r.text
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@staticmethod
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def _predict_openai(prompt, client, model):
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resp = client.ChatCompletion.create(
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model=model,
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messages=[
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{"role": "system", "content": "Sei un consulente finanziario crypto."},
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{"role": "user", "content": prompt}
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],
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max_tokens=300,
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temperature=0.7
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)
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return resp["choices"][0]["message"]["content"].strip()
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@staticmethod
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def _predict_anthropic(prompt, client, model):
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response = client.completions.create(
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model=model,
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prompt=prompt,
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max_tokens=300,
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temperature=0.7
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)
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return response.completion.strip()
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@staticmethod
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def _predict_google(prompt, client, model):
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response = client.generate_text(
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model=model,
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prompt=prompt,
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max_output_tokens=300,
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temperature=0.7
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)
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return response.text.strip()
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@staticmethod
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def _predict_deepseek(prompt, api_key, model):
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url = "https://api.deepseek.ai/v1/chat/completions"
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headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
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payload = {
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"model": model,
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"messages": [{"role": "user", "content": prompt}],
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"max_tokens": 300,
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"temperature": 0.7
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}
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response = requests.post(url, headers=headers, json=payload)
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response.raise_for_status()
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data = response.json()
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return data["choices"][0]["message"]["content"].strip()
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@staticmethod
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def _predict_mock(style):
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if style.lower() in ("aggressive", "aggr"):
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return (
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"🚀 Strategia aggressiva (mock): "
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"30% BTC, 20% ETH, 50% altcoins emergenti. "
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"Motivo: alta volatilità + potenziale upside."
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)
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return (
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"🛡️ Strategia conservativa (mock): "
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"60% BTC, 30% ETH, 10% stablecoins. "
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"Motivo: protezione da volatilità + focus su asset solidi."
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)
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5
src/app/agents/social_agent.py
Normal file
5
src/app/agents/social_agent.py
Normal file
@@ -0,0 +1,5 @@
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class SocialAgent:
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@staticmethod
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def analyze(query: str) -> str:
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# Mock analisi social
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return "💬 Sentiment social: forte interesse retail su nuove altcoin emergenti."
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31
src/app/tool.py
Normal file
31
src/app/tool.py
Normal file
@@ -0,0 +1,31 @@
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from .agents.market_agent import MarketAgent
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from .agents.news_agent import NewsAgent
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from .agents.social_agent import SocialAgent
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from .agents.predictor_agent import PredictorAgent
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class ToolAgent:
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def __init__(self):
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self.market_agent = MarketAgent()
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self.news_agent = NewsAgent()
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self.social_agent = SocialAgent()
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self.predictor_agent = PredictorAgent()
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def interact(self, query, provider="mock", style="conservative"):
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# Step 1: raccolta analisi
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market_data = self.market_agent.analyze(query)
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news_sentiment = self.news_agent.analyze(query)
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social_sentiment = self.social_agent.analyze(query)
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# Step 2: aggrega sentiment
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sentiment = f"{news_sentiment}\n{social_sentiment}"
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# Step 3: previsione
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prediction = self.predictor_agent.predict(
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data=market_data,
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||||
sentiment=sentiment,
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style=style,
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||||
provider=provider
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||||
)
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||||
return f"{market_data}\n{sentiment}\n\n📈 Consiglio finale:\n{prediction}"
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@@ -17,4 +17,4 @@ try:
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||||
result = reasoning_agent.run("Scrivi una poesia su un gatto. Sii breve.")
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||||
print(result.content)
|
||||
except Exception as e:
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||||
pass
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||||
print(f"Si è verificato un errore: {e}")
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||||
|
||||
274
uv.lock
generated
274
uv.lock
generated
@@ -4,7 +4,7 @@ requires-python = "==3.12.*"
|
||||
|
||||
[[package]]
|
||||
name = "agno"
|
||||
version = "1.8.1"
|
||||
version = "2.0.5"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "docstring-parser" },
|
||||
@@ -17,13 +17,12 @@ dependencies = [
|
||||
{ name = "python-multipart" },
|
||||
{ name = "pyyaml" },
|
||||
{ name = "rich" },
|
||||
{ name = "tomli" },
|
||||
{ name = "typer" },
|
||||
{ name = "typing-extensions" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/4a/fc/8e815919fde2104c56091632a39b1b5db4b3ffe01c4b192f57d0252b9a7b/agno-1.8.1.tar.gz", hash = "sha256:ef134860987f7bd3397e028b32ce90e51e0920dc8265e886e76a55651a6384ce", size = 754435, upload-time = "2025-08-27T16:27:51.777Z" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/da/30/53264fe9543fcb1d8b860ca3a3b705da5032e73f93818273d3169c3edc1b/agno-2.0.5.tar.gz", hash = "sha256:37d72fb98eb97c3abdb15be186f795b39f8ff668d97d9f9382e5971329e075d1", size = 855430, upload-time = "2025-09-17T02:45:10.348Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/0d/2c/88321406cf5218475859e6f04d1485771d30b02668537890117d12b1f89f/agno-1.8.1-py3-none-any.whl", hash = "sha256:1c16c2cec2b930a6fd8a15859a57da8f497223f041ce87a11b9a29b1b61f35e5", size = 996593, upload-time = "2025-08-27T16:27:49.612Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/96/f7/c216f0a218ef99958b77194613250131834f08d3a48b7a33bde6c638c5be/agno-2.0.5-py3-none-any.whl", hash = "sha256:9050bdb63feca2f421c0e3e73172ac269e82ed462e54b7318868a65107a89bd3", size = 1077050, upload-time = "2025-09-17T02:45:08.153Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@@ -44,6 +43,25 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643, upload-time = "2024-05-20T21:33:24.1Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "anthropic"
|
||||
version = "0.68.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "anyio" },
|
||||
{ name = "distro" },
|
||||
{ name = "docstring-parser" },
|
||||
{ name = "httpx" },
|
||||
{ name = "jiter" },
|
||||
{ name = "pydantic" },
|
||||
{ name = "sniffio" },
|
||||
{ name = "typing-extensions" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/64/46/da44bf087ddaf3f7dbe4808c00c7cde466fe68c4fc9fbebdfc231f4ea205/anthropic-0.68.0.tar.gz", hash = "sha256:507e9b5f627d1b249128ff15b21855e718fa4ed8dabc787d0e68860a4b32a7a8", size = 471584, upload-time = "2025-09-17T15:20:19.509Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/60/32/2d7553184b05bdbec61dd600014a55b9028408aee6128b25cb6f20e3002c/anthropic-0.68.0-py3-none-any.whl", hash = "sha256:ac579ea5eca22a7165b1042e6af57c4bf556e51afae3ca80e24768d4756b78c0", size = 325199, upload-time = "2025-09-17T15:20:17.452Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "anyio"
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[[package]]
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[[package]]
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[[package]]
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{ name = "agno" },
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{ name = "google" },
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{ name = "gradio" },
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{ name = "openai" },
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[package.metadata]
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{ name = "google-genai" },
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{ name = "openai" },
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[[package]]
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Reference in New Issue
Block a user