| import os | |
| import pandas as pd | |
| import numpy as np | |
| from dotenv import load_dotenv | |
| from ml.inference import MLEngine | |
| from agent.agent import MaintenanceAgent | |
| load_dotenv() | |
| raw_df = pd.DataFrame({ | |
| "vdc1": np.random.normal(600, 3, 200), | |
| "idc1": np.random.normal(10.0, 0.2, 200) | |
| }) | |
| engine = MLEngine() | |
| phase2_output = engine.predict_from_raw(raw_df) | |
| print("\n=== ML OUTPUT ===") | |
| print(phase2_output) | |
| agent = MaintenanceAgent( | |
| api_key=os.getenv("GOOGLE_API_KEY"), | |
| model_name="gemini-2.5-flash-lite", | |
| temperature=0.0 | |
| ) | |
| agent_output = agent.run(phase2_output) | |
| print("\n=== AGENT OUTPUT ===") | |
| print(agent_output) | |