Pydantic

- use Pydantic for input & output for models
- update ToolAgent to utilize new model definitions
- improve test cases for consistency

tests/agents/test_predictor.py

@@ -1,19 +1,29 @@
-import json
 import pytest
-from app.agents import predictor
-from app.models import Models
+from app.agents.predictor import PREDICTOR_INSTRUCTIONS, PredictorInput, PredictorOutput, PredictorStyle
+from app.markets.base import ProductInfo
+from app.models import AppModels
 
-def unified_checks(model: Models, input):
-    llm = model.get_agent(predictor.instructions())
+def unified_checks(model: AppModels, input):
+    llm = model.get_agent(PREDICTOR_INSTRUCTIONS, output=PredictorOutput)
     result = llm.run(input)
+    content = result.content
 
-    print(result.content)
-    potential_json = Models.extract_json_str_from_response(result.content)
-    content = json.loads(potential_json)  # Verifica che l'output sia un JSON valido
-
-    assert content['strategia'] is not None
-    assert isinstance(content['portafoglio'], list)
-    assert abs(sum(item['percentuale'] for item in content['portafoglio']) - 100) < 0.01  # La somma deve essere esattamente 100
+    assert isinstance(content, PredictorOutput)
+    assert content.strategy not in (None, "", "null")
+    assert isinstance(content.strategy, str)
+    assert isinstance(content.portfolio, list)
+    assert len(content.portfolio) > 0
+    for item in content.portfolio:
+        assert item.asset not in (None, "", "null")
+        assert isinstance(item.asset, str)
+        assert item.percentage > 0
+        assert item.percentage <= 100
+        assert isinstance(item.percentage, (int, float))
+        assert item.motivation not in (None, "", "null")
+        assert isinstance(item.motivation, str)
+    # La somma delle percentuali deve essere esattamente 100
+    total_percentage = sum(item.percentage for item in content.portfolio)
+    assert abs(total_percentage - 100) < 0.01  # Permette una piccola tolleranza per errori di arrotondamento
 
 class TestPredictor:
 
@@ -21,23 +31,19 @@ class TestPredictor:
     def inputs(self):
         data = []
         for symbol, price in [("BTC", 60000.00), ("ETH", 3500.00), ("SOL", 150.00)]:
-            product_info = predictor.ProductInfo()
+            product_info = ProductInfo()
             product_info.symbol = symbol
             product_info.price = price
             data.append(product_info)
 
-        return predictor.prepare_inputs(
-            data=data,
-            style=predictor.PredictorStyle.AGGRESSIVE,
-            sentiment="positivo"
-        )
+        return PredictorInput(data=data, style=PredictorStyle.AGGRESSIVE, sentiment="positivo")
 
     def test_gemini_model_output(self, inputs):
-        unified_checks(Models.GEMINI, inputs)
+        unified_checks(AppModels.GEMINI, inputs)
+
+    def test_ollama_qwen_model_output(self, inputs):
+        unified_checks(AppModels.OLLAMA_QWEN, inputs)
 
     @pytest.mark.slow
     def test_ollama_gpt_oss_model_output(self, inputs):
-        unified_checks(Models.OLLAMA_GPT, inputs)
-
-    def test_ollama_qwen_model_output(self, inputs):
-        unified_checks(Models.OLLAMA_QWEN, inputs)
+        unified_checks(AppModels.OLLAMA_GPT, inputs)