Create model_api.py

model_api.py

@@ -0,0 +1,34 @@
+# 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)
+@app.get("/")
+async def root():
+    return {"message": "AI model API is up and running!"}
+
+# Example POST endpoint to accept input and respond with dummy AI results
+@app.post("/predict")
+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()
+    }