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from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import List
import pandas as pd
import numpy as np
from model_load_save import load_model
import dill
def load_preprocessing_components():
with open("encoder.pkl", "rb") as f:
encoder = dill.load(f)
with open("scaler.pkl", "rb") as f:
scaler = dill.load(f)
return encoder, scaler
app = FastAPI()
# Load trained model
model = load_model()
encoder, scaler = load_preprocessing_components()
# Define input schema
class InferenceData(BaseModel):
Age: float
Sex: str
ChestPainType: str
RestingBP: float
Cholesterol: float
FastingBS: int
RestingECG: str
MaxHR: float
ExerciseAngina: str
Oldpeak: float
ST_Slope: str
# Health check endpoint
@app.get("/")
def read_root():
return {"message": "Inference API is up and running"}
# Helper function for preprocessing
def preprocess_data(df: pd.DataFrame) -> np.ndarray:
# Encode categorical variables
encoded = encoder.transform(df[encoder.feature_names_in_])
encoded_df = pd.DataFrame(encoded, columns=encoder.get_feature_names_out(), index=df.index)
# Extracting features
df = pd.concat([df.drop(encoder.feature_names_in_, axis=1), encoded_df], axis=1)
# Combine and scale features
df_selected = pd.concat([df[['Oldpeak', 'MaxHR', 'Age']], df[['ExerciseAngina_Y', 'ST_Slope_Flat', 'ST_Slope_Up']]], axis=1) # directly extracted selected features
# Scale features
df = scaler.transform(df_selected)
return df
# Endpoint for single prediction
@app.post("/predict")
def predict(data: InferenceData):
try:
# Convert input data to DataFrame
df = pd.DataFrame([data.model_dump()])
# Preprocess data
processed_data = preprocess_data(df)
# Make prediction
prediction = model.predict(processed_data)
# Return prediction result
return {"prediction": int(prediction[0])}
except Exception as e:
raise HTTPException(status_code=500, detail=f"Error during prediction: {str(e)}")
# Endpoint for batch prediction
@app.post("/batch_predict")
def batch_predict(data: List[InferenceData]):
try:
# Convert list of inputs to DataFrame
df = pd.DataFrame([item.model_dump() for item in data])
# Preprocess data
processed_data = preprocess_data(df)
# Make batch predictions
predictions = model.predict(processed_data)
# Format and return predictions
results = [{"input": item.model_dump(), "prediction": int(pred)} for item, pred in zip(data, predictions)]
return {"predictions": results}
except Exception as e:
raise HTTPException(status_code=500, detail=f"Error during batch prediction: {str(e)}") |