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import pandas as pd
import joblib
from fastapi import FastAPI
import uvicorn
import numpy as np
import os
app = FastAPI()
def load_model():
num_imputer_filepath = "numerical_imputer.joblib"
scaler_filepath = "scaler.joblib"
model_filepath = "lr_model.joblib"
num_imputer = joblib.load(num_imputer_filepath)
scaler = joblib.load(scaler_filepath)
model = joblib.load(model_filepath)
return num_imputer, scaler, model
def preprocess_input_data(input_data, num_imputer, scaler):
input_data_df = pd.DataFrame([input_data])
num_columns = [col for col in input_data_df.columns if input_data_df[col].dtype != 'object']
input_data_imputed_num = num_imputer.transform(input_data_df[num_columns])
input_scaled_df = pd.DataFrame(scaler.transform(input_data_imputed_num), columns=num_columns)
return input_scaled_df
@app.get("/")
def read_root():
"""
Welcome to the Sepsis Prediction API! 🩺💉
This API provides advanced machine learning predictions for sepsis. ⚡📊
For more information and to explore the API's capabilities,
please visit the documentation: [API Documentation](https://abubakari-sepsis-fastapi-prediction-app.hf.space/docs/) 📚🔍
Stay proactive in detecting sepsis with our predictive tool! 🚀🔮
"""
return "Sepsis Prediction App"
@app.get("/sepsis/predict")
def predict_sepsis_endpoint(PRG: float, PL: float, PR: float, SK: float, TS: float,
M11: float, BD2: float, Age: float, Insurance: int):
num_imputer, scaler, model = load_model()
input_data = {
'PRG': [PRG],
'PL': [PL],
'PR': [PR],
'SK': [SK],
'TS': [TS],
'M11': [M11],
'BD2': [BD2],
'Age': [Age],
'Insurance': [Insurance]
}
input_scaled_df = preprocess_input_data(input_data, num_imputer, scaler)
probabilities = model.predict_proba(input_scaled_df)[0]
prediction = np.argmax(probabilities)
sepsis_status = "Positive" if prediction == 1 else "Negative"
probability = probabilities[1] if prediction == 1 else probabilities[0]
#statement = f"The patient is {sepsis_status}. There is a {'high' if prediction == 1 else 'low'} probability ({probability:.2f}) that the patient is susceptible to developing sepsis."
if prediction == 1:
status_icon = "✔" # Red 'X' icon for positive sepsis prediction
sepsis_explanation = "Sepsis is a life-threatening condition caused by an infection. A positive prediction suggests that the patient might be exhibiting sepsis symptoms and requires immediate medical attention."
else:
status_icon = "✘" # Green checkmark icon for negative sepsis prediction
sepsis_explanation = "Sepsis is a life-threatening condition caused by an infection. A negative prediction suggests that the patient is not currently exhibiting sepsis symptoms."
statement = f"The patient's sepsis status is {sepsis_status} {status_icon} with a probability of {probability:.2f}. {sepsis_explanation}"
user_input_statement = "Please note this is the user-inputted data: "
output_df = pd.DataFrame([input_data])
result = {'predicted_sepsis': sepsis_status, 'statement': statement, 'user_input_statement': user_input_statement, 'input_data_df': output_df.to_dict('records')}
return result
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=7860, reload=True)
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