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service.py
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service.py
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# main.py
import pandas as pd
from fastapi import FastAPI
from joblib import load
from pydantic import BaseModel
from src.features.feature_definitions import feature_build
app = FastAPI()
class PredictionInput(BaseModel):
# Define the input parameters required for making predictions
vendor_id: float
pickup_datetime: float
passenger_count: float
pickup_longitude: float
pickup_latitude: float
dropoff_longitude: float
dropoff_latitude: float
store_and_fwd_flag: float
# Load the pre-trained RandomForest model
model_path = "model.joblib"
model = load(model_path)
@app.get("/")
def home():
return "Working fine"
@app.post("/predict")
def predict(input_data: PredictionInput):
# Extract features from input_data and make predictions using the loaded model
features = {
'vendor_id': input_data.vendor_id,
'pickup_datetime': input_data.pickup_datetime,
'passenger_count': input_data.passenger_count,
'pickup_longitude': input_data.pickup_longitude,
'pickup_latitude': input_data.pickup_latitude,
'dropoff_longitude': input_data.dropoff_longitude,
'dropoff_latitude': input_data.dropoff_latitude,
'store_and_fwd_flag': input_data.store_and_fwd_flag
}
features = pd.DataFrame(features, index=[0])
features = feature_build(features, 'prod')
prediction = model.predict(features)[0].item()
# Return the prediction
return {"prediction": prediction}
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8080)