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KRONECTOR - Agent Chain Test Script
"""
import os
from dotenv import load_dotenv
from agents.prediction_agent import PredictionOutput
from agents.critique_agent import critique_agent
from agents.synthesis_agent import synthesis_agent
def main():
load_dotenv()
# Simulate a user query
query = "Why did the model predict Max Verstappen to win the 2026 Monaco GP?"
# Simulate a PredictionOutput from LightGBM
mock_prediction: PredictionOutput = {
"probability": 0.82,
"shap_values": {
"grid_position": -0.8500, # Starting pole (lower grid pos is better, so SHAP might show negative impact on position but positive on win. Let's just use positive numbers for impact)
"driver_form_last3": 0.6200,
"team_pit_speed": 0.3100,
"safety_car_probability": -0.1500,
"weather_rainfall": 0.0500
},
"feature_names": [
"grid_position", "driver_form_last3", "team_pit_speed",
"safety_car_probability", "weather_rainfall"
],
"model_version": "latest",
"run_id": "test_123"
}
print("--- 1. CRITIQUE AGENT ---")
critique = critique_agent(mock_prediction)
print(f"Approved: {critique['approved']}")
print(f"Confidence: {critique['confidence_rating']}")
print("Notes:")
print(critique["critique_notes"])
print("\n")
print("--- 2. SYNTHESIS AGENT (Llama3) ---")
synthesis = synthesis_agent(query, mock_prediction, critique)
print(synthesis["final_response"])
if __name__ == "__main__":
main()
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