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| import pandas as pd | |
| import joblib | |
| from fastapi import FastAPI | |
| import uvicorn | |
| import numpy as np | |
| import os | |
| app = FastAPI() | |
| #app.mount("/static", StaticFiles(directory="static"), name="static") | |
| #templates = Jinja2Templates(directory="templates") | |
| def load_model(): | |
| cwd = os.getcwd() | |
| destination = os.path.join(cwd, "Assets") | |
| imputer_filepath = os.path.join(destination, "numerical_imputer.joblib") | |
| scaler_filepath = os.path.join(destination, "scaler.joblib") | |
| model_filepath = os.path.join(destination, "lr_model.joblib") | |
| num_imputer = joblib.load(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 | |
| def read_root(): | |
| return "Sepsis Prediction App" | |
| 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) | |