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2d10552 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 | from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import joblib
import pandas as pd
import logging
# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Load the model
try:
model = joblib.load("titanic_model.pkl")
logger.info(f"Model loaded successfully. Feature names: {model.feature_names_in_}")
except Exception as e:
logger.error(f"Error loading model: {e}")
raise
# Create the Pydantic model for the input data
class Passenger(BaseModel):
pclass: int
sex: str
age: float
sibsp: int
parch: int
fare: float
embarked: str
# {
# "pclass": 1,
# "sex": "male",
# "age": 30,
# "sibsp": 0,
# "parch": 0,
# "fare": 100,
# "embarked": "S"
# }
# Create the FastAPI instance
app = FastAPI()
# Create the root endpoint
@app.get("/")
def read_root():
return {"message": "Welcome to the Titanic Survival Prediction API"}
# Create the predict endpoint
@app.post("/predict")
def predict(passenger: Passenger):
try:
# Convert the input data to a DataFrame
input_dict = passenger.model_dump()
logger.info(f"Input data: {input_dict}")
input_data = pd.DataFrame([input_dict])
logger.info(f"DataFrame created with columns: {input_data.columns.tolist()}")
# One-Hot Encode the input data
input_data = pd.get_dummies(input_data)
logger.info(f"After one-hot encoding, columns: {input_data.columns.tolist()}")
# Check if model has feature_names_in_ attribute
if not hasattr(model, 'feature_names_in_'):
raise HTTPException(status_code=500, detail="Model does not have feature_names_in_ attribute")
logger.info(f"Model expects columns: {model.feature_names_in_}")
# Align the input data columns with the model columns
input_data = input_data.reindex(columns=model.feature_names_in_, fill_value=0)
logger.info(f"After reindexing, columns: {input_data.columns.tolist()}")
# Check if we have the right number of features
if input_data.shape[1] != len(model.feature_names_in_):
raise HTTPException(
status_code=500,
detail=f"Feature mismatch: Input has {input_data.shape[1]} features, model expects {len(model.feature_names_in_)}"
)
# Predict the survival of the passenger
prediction = model.predict(input_data)
return {
"prediction": int(prediction[0]),
"prediction_probability": float(model.predict_proba(input_data)[0][1]) if hasattr(model, 'predict_proba') else None
}
except Exception as e:
logger.error(f"Prediction error: {e}")
raise HTTPException(status_code=500, detail=f"Prediction failed: {str(e)}") |