50 lines
1.9 KiB
Python
50 lines
1.9 KiB
Python
import pytest
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from app.predictor import PREDICTOR_INSTRUCTIONS, PredictorInput, PredictorOutput, PredictorStyle
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from app.markets.base import ProductInfo
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from app.models import AppModels
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def unified_checks(model: AppModels, input):
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llm = model.get_agent(PREDICTOR_INSTRUCTIONS, output=PredictorOutput)
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result = llm.run(input)
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content = result.content
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assert isinstance(content, PredictorOutput)
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assert content.strategy not in (None, "", "null")
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assert isinstance(content.strategy, str)
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assert isinstance(content.portfolio, list)
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assert len(content.portfolio) > 0
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for item in content.portfolio:
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assert item.asset not in (None, "", "null")
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assert isinstance(item.asset, str)
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assert item.percentage > 0
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assert item.percentage <= 100
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assert isinstance(item.percentage, (int, float))
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assert item.motivation not in (None, "", "null")
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assert isinstance(item.motivation, str)
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# La somma delle percentuali deve essere esattamente 100
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total_percentage = sum(item.percentage for item in content.portfolio)
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assert abs(total_percentage - 100) < 0.01 # Permette una piccola tolleranza per errori di arrotondamento
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class TestPredictor:
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@pytest.fixture(scope="class")
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def inputs(self):
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data = []
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for symbol, price in [("BTC", 60000.00), ("ETH", 3500.00), ("SOL", 150.00)]:
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product_info = ProductInfo()
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product_info.symbol = symbol
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product_info.price = price
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data.append(product_info)
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return PredictorInput(data=data, style=PredictorStyle.AGGRESSIVE, sentiment="positivo")
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def test_gemini_model_output(self, inputs):
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unified_checks(AppModels.GEMINI, inputs)
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def test_ollama_qwen_model_output(self, inputs):
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unified_checks(AppModels.OLLAMA_QWEN, inputs)
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@pytest.mark.slow
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def test_ollama_gpt_oss_model_output(self, inputs):
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unified_checks(AppModels.OLLAMA_GPT, inputs)
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