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| # model_api.py | |
| from fastapi import FastAPI | |
| from pydantic import BaseModel | |
| app = FastAPI() | |
| # Define the input data model for your AI API | |
| class EquipmentInput(BaseModel): | |
| usage_hours: float | |
| idle_hours: float | |
| movement_frequency: float | |
| cost_per_hour: float | |
| last_maintenance: str # Expecting 'YYYY-MM-DD' format | |
| # Example GET endpoint (test your server) | |
| async def root(): | |
| return {"message": "AI model API is up and running!"} | |
| # Example POST endpoint to accept input and respond with dummy AI results | |
| async def predict(input_data: EquipmentInput): | |
| # Here you would call your real AI model logic | |
| # For now, returning dummy data | |
| suggestion = "Move" | |
| confidence = 0.92 | |
| utilization_score = 0.88 | |
| return { | |
| "suggestion": suggestion, | |
| "confidence": confidence, | |
| "utilization_score": utilization_score, | |
| "input_received": input_data.dict() | |
| } | |