main.py

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, Field
import joblib
import pandas as pd
import os

from Preprocessing import preprocess_input,model

app = FastAPI(
    title="Electricity Cost Prediction API",
    description="Predicts electricity cost based on facility and operational parameters"
)

if model is None:
    raise RuntimeError("Critical Error: ML model failed to load from external source during application startup.")

class ElectricityInput(BaseModel):
    site_area: float = Field(..., description="Area of the site in square units")
    structure_type: str = Field(..., description="Type of structure (e.g., 'residential', 'commercial')")
    water_consumption: float = Field(..., description="Daily/monthly water consumption")
    recycling_rate: float = Field(..., description="Percentage of waste recycled")
    utilisation_rate: float = Field(..., description="Rate of facility utilization")
    air_qality_index: float = Field(..., description="Air quality index")
    issue_reolution_time: float = Field(..., description="Time taken to resolve issues (e.g., in hours)")
    resident_count: int = Field(..., description="Number of residents/occupants")

#Basically all these inputs for the base model will be converted into pydantic object after checking, optimizing and safety assurance 

#Moving to main API code-> Using the predict model for my task
@app.post("/predict")
async def predict_electricity_cost(data: ElectricityInput):
    print("Predicts the total electricity cost based on the provided input features")
    
    try:
        input_data_dict = data.model_dump() 
        processed_df = preprocess_input(input_data_dict)
        prediction = model.predict(processed_df)[0]
        predicted_cost = round(float(prediction), 2)
        return {"predicted_electricity_cost": predicted_cost}

    except Exception as e:
        print(f"An unexpected error occurred during prediction: {e}")
        raise HTTPException(
            status_code=500, #Internal server error....Basically giving error coverups
            detail=f"An internal server error occurred during prediction. Error : {e}"
        )

#My overall work in this API File ->
#User input : JSON -> Pydantic object -> dictionary -> DataFrame -> model.predict()

@app.get("/health")
async def health_check():  
    #Using asyn function, because it can pause wherever the function needs to run a different block of code and  then restart again
    return {"status": "ok", "message": "Electricity Cost Prediction API is running accurately!"}

#So, this was the overall API implementation.....Also, I've created the docker and .dockerignore files in this folder to package my work and deploy it....basically storing it in a container...You can see that I've marked up all error points as much as possible to resolve all the incoming issues fast